system

Abstract

The system according to the embodiment aims to enable even employees who are not familiar with legal matters to respond to relevant laws and regulations and risks. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a creation unit, and a sending unit. The reception unit inputs the details of the business. The analysis unit analyzes the details of the business input by the reception unit and presents relevant laws and regulations and risks. The creation unit creates documents with necessary wording based on the laws and risks presented by the analysis unit. The sending unit sends the documents created by the creation unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult for employees who are not familiar with legal matters to deal with relevant laws and regulations and risks, which can lead to problems.

[0005] The system according to the embodiment aims to enable even employees who are not familiar with legal matters to respond to relevant laws and regulations and risks. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a creation unit, and a sending unit. The reception unit inputs the business content. The analysis unit analyzes the business content input by the reception unit and presents relevant laws and regulations and risks. The creation unit creates documents with necessary wording based on the laws and regulations and risks presented by the analysis unit. The sending unit sends the documents created by the creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable even employees who are not familiar with legal matters to deal with relevant laws and regulations and risks. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A corporate legal advice tool according to an embodiment of the present invention is a system in which, when a business operation is input, a generation AI presents relevant laws, regulations, risks, and countermeasures, and automatically creates and sends documents, materials, emails, etc., adding necessary wording. In this system, an employee inputs their business operation details, and the generation AI analyzes the details and presents relevant laws and risks. For example, a business operation such as starting a new project or creating a contract is input. This information is input into the generation AI, which analyzes the input business operation details and presents relevant laws and risks. For example, based on the project details, the generation AI identifies relevant security and copyright laws and presents risks associated with them. Furthermore, the generation AI automatically creates documents, materials, emails, etc., adding necessary wording, etc., based on the presented laws and risks. For example, when creating a contract, the generation AI automatically adds necessary legal wording and creates a document that clearly states countermeasures to risks. Finally, the generation AI automatically sends the created documents, materials, emails, etc., for example, by sending the contract to the relevant parties and sending risk countermeasure materials to relevant departments within the company. This tool expands the scope of automation by linking with internal systems and databases. For example, by linking with an internal project management system, it can automatically provide legal advice according to the progress of a project. In this way, a corporate legal advice tool that utilizes generative AI allows even employees who are not familiar with legal matters to quickly respond to relevant laws and risks, reducing issues related to security, copyright, etc. and contributing to improving employee work efficiency. This allows even employees who are not familiar with legal matters to quickly respond to relevant laws and risks.

[0029] The corporate legal advice tool according to the embodiment includes a reception unit, an analysis unit, a creation unit, and a sending unit. The reception unit is a unit through which employees input their work details. Examples of work details input by employees include, but are not limited to, starting a new project and creating contracts. The reception unit provides an interface for inputting the work details in text format. The reception unit can also support multiple input methods, such as voice input and image input. For example, the reception unit can convert the work details dictated by employees into text data using voice recognition technology. The analysis unit is a unit that uses generative AI to analyze the work details input by the reception unit and present relevant laws and risks. For example, the analysis unit can extract keywords from the work details using text analysis technology and identify relevant laws and risks based on the keywords. The analysis unit can also use data mining technology to refer to a database of laws and risks related to past work details and present the most appropriate laws and risks. For example, the analysis unit can automatically search for laws and risks related to the work details and present them as a list. The creation department uses a generation AI to create documents by adding necessary wording based on the laws and risks presented by the analysis department. For example, when creating a contract, the creation department automatically adds necessary legal wording and creates a document that clearly states how to address the risk. The creation department can also use the generation AI to automatically create documents such as materials and emails. For example, the creation department automatically creates risk countermeasure materials and automatically generates emails to send to relevant departments. The sending department is a department that sends documents created by the creation department. For example, the sending department sends created contracts to relevant parties. The sending department can also send created materials to relevant departments within the company. For example, the sending department automatically sends created documents using an email sending system. As a result, the corporate legal advice tool according to the embodiment automates the process from entering business details to presenting relevant laws and risks, and creating and sending documents with necessary wording, allowing even employees who are not familiar with legal matters to respond quickly.

[0030] The analysis unit may include a reference unit that references a database of relevant laws and risks. Examples of the database of relevant laws and risks include, but are not limited to, a laws and regulations database and a risk management database. For example, the analysis unit references the laws and regulations database to identify laws and regulations related to the business content. The analysis unit can also reference a risk management database to identify risks related to the business content. For example, the analysis unit automatically searches for laws and risks related to the business content and presents them as a list. By referencing the database of relevant laws and regulations and risks, the accuracy of the analysis is improved.

[0031] The sending unit may include a storage unit that stores the generated documents. The storage unit stores the generated documents in a digital format, for example. Digital storage may include, but is not limited to, cloud storage or an in-house server. The storage unit may also store the generated documents on paper. For example, the storage unit may print out the generated documents and store them as files. Furthermore, the storage unit may set a storage period. For example, the storage unit may store documents for a certain period based on legal requirements and then automatically delete them. This allows the created documents to be stored and referenced later.

[0032] The reception unit may include a linking unit that performs data integration with internal systems. The linking unit links with, for example, an internal project management system or database. Data integration includes, but is not limited to, API integration, database integration, real-time integration, and the like. For example, the linking unit uses an API to obtain project progress information from an internal system and provides legal advice based on that information. The linking unit can also use database integration to obtain related information from an internal legal database and provide the information to the analysis unit. For example, the linking unit uses real-time integration to automatically provide legal advice according to the progress of the project. This expands the scope of automation by performing data integration with internal systems.

[0033] The reception unit can analyze the user's past work content input history and select an appropriate input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit predicts and suggests an input method to be used during a specific time period based on the user's past input history. The reception unit can also analyze patterns of work content previously input by the user and suggest an optimal input method. For example, the reception unit selects an optimal input method based on the user's past input history. In this way, the optimal input method can be suggested to the user by analyzing the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to a generation AI and have the generation AI select an optimal input method.

[0034] When inputting the task details, the reception unit can filter the task details based on the user's current project and areas of interest. For example, the reception unit can prioritize displaying task details related to the user's current project. For example, the reception unit can filter and display related task details based on the user's areas of interest. The reception unit can also suggest related task details based on areas in which the user has previously shown interest. For example, the reception unit can identify the user's current project and areas of interest and filter the task details based on the identified project and areas of interest. This allows highly relevant information to be prioritized and displayed by filtering the task details based on the user's current project and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's project data and area of ​​interest data to a generation AI and have the generation AI perform the filtering.

[0035] When inputting task details, the reception unit can prioritize inputting highly relevant task details by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying task details related to that area. For example, the reception unit can prioritize input of task details related to locations close to the user's current location. The reception unit can also prioritize suggesting task details related to locations the user has visited in the past. For example, the reception unit can filter task details based on the user's geographical location information and prioritize displaying highly relevant information. This allows highly relevant task details to be input preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's geographical location data to a generation AI and cause the generation AI to select highly relevant task details.

[0036] When inputting job content, the reception unit can analyze the user's social media activity and input related job content. For example, the reception unit can prioritize displaying job content related to topics mentioned by the user on social media. For example, the reception unit can analyze the user's social media posts and suggest related job content. The reception unit can also input related job content based on the activity of accounts the user follows. For example, the reception unit can filter job content based on the user's social media activity and prioritize displaying highly relevant information. In this way, by analyzing the user's social media activity, related job content can be input preferentially. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data to a generation AI and cause the generation AI to select related job content.

[0037] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the work contents during analysis. The analysis unit, for example, analyzes the interrelationships between the work contents and identifies related laws and regulations and risks. For example, the analysis unit evaluates the extent of the impact of risks by taking into account the interrelationships between the work contents. The analysis unit can also propose optimal countermeasures based on the interrelationships between the work contents. For example, the analysis unit analyzes the interrelationships between the work contents and identifies related laws and regulations and risks. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between the work contents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input interrelationship data between the work contents into a generation AI and cause the generation AI to improve the accuracy of the analysis.

[0038] During analysis, the analysis unit can take into account the attribute information of the person who submitted the work content. The analysis unit can identify relevant laws and risks based on, for example, the submitter's job title and responsibilities. For example, the analysis unit can evaluate the extent of the risk's impact by taking into account the submitter's past work history. The analysis unit can also propose optimal countermeasures based on the submitter's attribute information. For example, the analysis unit can analyze the submitter's attribute information and identify relevant laws and risks. By taking the submitter's attribute information into account, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the submitter's attribute information data into a generation AI and have the generation AI perform the analysis.

[0039] The analysis unit can perform the analysis while taking into account the geographic distribution of the work content. For example, the analysis unit analyzes the geographic distribution of the work content and identifies the laws and risks for each region. For example, the analysis unit evaluates the impact range of the risk by taking the geographic distribution into account. The analysis unit can also propose optimal countermeasures based on the geographic distribution. For example, the analysis unit analyzes the geographic distribution of the work content and identifies the relevant laws and risks. In this way, the laws and risks for each region can be identified by taking the geographic distribution of the work content into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographic distribution data to a generation AI and have the generation AI perform the analysis.

[0040] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the business content. The analysis unit, for example, improves the accuracy of the analysis by referring to literature on laws and regulations and risks related to the business content. For example, the analysis unit evaluates the extent of the impact of a risk based on the related literature. The analysis unit can also suggest optimal countermeasures by referring to the related literature. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to the business content. In this way, the accuracy of the analysis is improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0041] The creation unit can adjust the level of detail of the document based on the importance of the business content when creating the document. For example, the creation unit creates a detailed document for business content with high importance. For example, the creation unit creates a concise document for business content with low importance. The creation unit can also adjust the level of detail of the document according to the importance of the business content. For example, the creation unit evaluates the importance of the business content and determines the level of detail of the document based on that. In this way, an appropriate document can be created by adjusting the level of detail of the document according to the importance of the business content. Some or all of the above-mentioned processing in the creation unit may be performed using, or without, AI. For example, the creation unit can input importance data of the business content to a generation AI and have the generation AI adjust the level of detail of the document.

[0042] The creation unit can apply different document creation algorithms depending on the category of work content when creating documents. For example, when creating a contract, the creation unit can apply an algorithm that emphasizes legal language. For example, when creating a project report, the creation unit can apply an algorithm that describes progress in detail. Furthermore, when creating risk countermeasure materials, the creation unit can apply an algorithm that clearly describes details of risks and how to deal with them. For example, the creation unit can classify the category of work content and apply a document creation algorithm accordingly. This allows documents to be created efficiently by applying an appropriate document creation algorithm depending on the category of work content. Some or all of the above-mentioned processing in the creation unit can be performed using, for example, AI, or can be performed without using AI. For example, the creation unit can input business content category data into a generation AI and have the generation AI apply a document creation algorithm.

[0043] When creating documents, the creation unit can determine the priority of documents based on the submission time of the business content. For example, the creation unit prioritizes document creation for urgent business content. For example, the creation unit prioritizes document creation for business content with an upcoming submission deadline. The creation unit can also adjust the order of document creation based on the submission time. For example, the creation unit evaluates the submission time of the business content and determines the priority of documents based on that. This allows documents to be created at the appropriate time by determining the priority of documents based on the submission time of the business content. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input data on the submission time of business content to a generation AI and have the generation AI determine the priority of documents.

[0044] The creation unit can adjust the order of documents based on the relevance of the business content when creating documents. For example, the creation unit creates documents with priority for highly relevant business content. For example, the creation unit adjusts the creation order of documents based on the relevance of the business content. The creation unit can also adjust the order of documents to prioritize processing highly relevant business content. For example, the creation unit evaluates the relevance of the business content and determines the order of documents based on that. This allows documents to be created efficiently by adjusting the order of documents based on the relevance of the business content. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input relevance data of business content to a generation AI and have the generation AI adjust the order of documents.

[0045] When sending, the sending unit can select the optimal sending method by taking into account the recipient's attribute information. For example, if the recipient is a company, the sending unit sends the email in an official email format. For example, if the recipient is an individual, the sending unit sends the email in a concise message format. In addition, if the recipient is international, the sending unit can select a multilingual sending method. For example, the sending unit selects a sending method based on the recipient's attribute information. This allows the optimal sending method to be selected by taking into account the recipient's attribute information. Some or all of the above-described processing in the sending unit may be performed using AI, for example, or may be performed without using AI. For example, the sending unit can input the recipient's attribute information data into the generation AI and have the generation AI select the sending method.

[0046] The sending unit can determine the priority of sending based on the importance of the content when sending. For example, the sending unit prioritizes sending content with high importance. For example, the sending unit postpones sending content with low importance. The sending unit can also adjust the priority of sending based on the importance of the content. For example, the sending unit evaluates the importance of the content and determines the priority of sending based on that. In this way, important content can be sent preferentially by determining the priority of sending based on the importance of the content. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input importance data of the content to the generation AI and have the generation AI determine the priority of sending.

[0047] At the time of sending, the sending unit can select the optimal sending method by taking into consideration the geographical location information of the destination. For example, if the destination is domestic, the sending unit selects a domestic sending method. For example, if the destination is overseas, the sending unit sends by international mail or international email. The sending unit can also select the optimal sending method based on the geographical location information of the destination. For example, the sending unit selects a sending method based on the geographical location information of the destination. In this way, the optimal sending method can be selected by taking into consideration the geographical location information of the destination. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input geographical location information data of the destination into the generation AI and have the generation AI select the sending method.

[0048] The sending unit can improve the accuracy of the sending by referring to literature related to the sending content when sending. The sending unit improves the accuracy of the sending, for example, by referring to literature on laws and regulations and risks related to the sending content. For example, the sending unit complements the details of the sending content based on the related literature. The sending unit can also confirm the accuracy of the sending content by referring to the related literature. For example, the sending unit improves the accuracy of the sending by referring to literature related to the sending content. In this way, the accuracy of the sending is improved by referring to the related literature. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the sending.

[0049] At the time of reference, the reference unit can analyze past reference history and select optimal data. For example, the reference unit preferentially displays data that the user has frequently referenced in the past. For example, the reference unit predicts and suggests data to be used during a specific time period based on the user's past reference history. The reference unit can also analyze the user's past reference history and select optimal data. For example, the reference unit selects optimal data based on the past reference history. In this way, optimal data can be selected by analyzing the past reference history. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input past reference history data to a generation AI and have the generation AI select optimal data.

[0050] The reference unit can adjust the order of reference based on the relevance of the data during reference. The reference unit, for example, preferentially displays highly relevant data. For example, the reference unit adjusts the order of reference based on the relevance of the data. The reference unit can also adjust the order to preferentially reference highly relevant data. For example, the reference unit evaluates the relevance of the data and determines the order of reference based on that. This allows data to be referenced efficiently by adjusting the order of reference based on the relevance of the data. Some or all of the above-mentioned processing in the reference unit may be performed using AI, for example, or may be performed without using AI. For example, the reference unit can input data on the relevance of the data to a generation AI and have the generation AI adjust the order of reference.

[0051] The storage unit can analyze past storage history and select the optimal storage method when saving data. For example, the storage unit prioritizes saving data that the user has frequently saved in the past. For example, the storage unit predicts and suggests data to save during a specific time period based on the user's past storage history. The storage unit can also analyze the user's past storage history and select the optimal storage method. For example, the storage unit selects the optimal storage method based on the past storage history. In this way, the optimal storage method can be selected by analyzing the past storage history. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input past storage history data to a generation AI and have the generation AI select the optimal storage method.

[0052] The storage unit can adjust the order of storage based on the relevance of the data when storing the data. For example, the storage unit prioritizes storing highly relevant data. For example, the storage unit adjusts the order of storage based on the relevance of the data. The storage unit can also adjust the order to prioritize storing highly relevant data. For example, the storage unit evaluates the relevance of the data and determines the order of storage based on the evaluation. In this way, data can be stored efficiently by adjusting the order of storage based on the relevance of the data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data on the relevance of the data to a generation AI and have the generation AI adjust the order of storage.

[0053] At the time of linking, the linking unit can analyze past linking history and select the optimal linking method. For example, the linking unit prioritizes linking data that the user has frequently linked in the past. For example, the linking unit predicts and suggests data to link during a specific time period based on the user's past linking history. The linking unit can also analyze the user's past linking history and select the optimal linking method. For example, the linking unit selects the optimal linking method based on the past linking history. In this way, the optimal linking method can be selected by analyzing the past linking history. Some or all of the above-described processing in the linking unit may be performed using, or without, AI, for example. For example, the linking unit can input past linking history data into a generation AI and cause the generation AI to select the optimal linking method.

[0054] The linking unit can adjust the linking order based on the relevance of the data when linking. For example, the linking unit prioritizes linking highly related data. For example, the linking unit adjusts the linking order based on the relevance of the data. The linking unit can also adjust the order to prioritize linking highly related data. For example, the linking unit evaluates the relevance of the data and determines the linking order based on that. In this way, data can be linked efficiently by adjusting the linking order based on the relevance of the data. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input data on the relevance of the data to the generation AI and cause the generation AI to adjust the linking order.

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

[0056] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also predict and suggest the input method to be used during a specific time period based on the user's past input history. It can also analyze patterns of work content that the user has entered in the past and suggest the optimal input method. In this way, it is possible to suggest the optimal input method to the user by analyzing the past input history.

[0057] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between work contents. For example, it can analyze the interrelationships between work contents and identify related laws and regulations and risks. It can also evaluate the extent of the impact of risks by taking into account the interrelationships between work contents. Furthermore, it can propose optimal countermeasures based on the interrelationships between work contents. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between work contents.

[0058] When sending, the sending unit can select the optimal sending method by taking into consideration the recipient's attribute information. For example, if the recipient is a company, it can be sent in an official email format. If the recipient is an individual, it can also be sent in a simple message format. Furthermore, if the recipient is international, it can also select a sending method that supports multiple languages. This allows the optimal sending method to be selected by taking into consideration the recipient's attribute information.

[0059] When creating a document, the creation unit can adjust the level of detail of the document based on the importance of the business content. For example, a detailed document can be created for business content with high importance. Also, a concise document can be created for business content with low importance. Furthermore, the level of detail of the document can be adjusted according to the importance of the business content. In this way, by adjusting the level of detail of the document according to the importance of the business content, an appropriate document can be created.

[0060] The sending unit can determine the priority of sending based on the importance of the content when sending. For example, it can send content with high importance first. It can also postpone sending content with low importance. Furthermore, it can adjust the priority of sending based on the importance of the content. In this way, by determining the priority of sending based on the importance of the content, it is possible to send important content with priority.

[0061] The reference unit can adjust the order of reference based on the relevance of data when referencing. For example, highly relevant data is displayed preferentially. The order of reference can also be adjusted based on the relevance of data. Furthermore, the order can be adjusted so that highly relevant data is referenced preferentially. In this way, adjusting the order of reference based on the relevance of data allows efficient data referencing.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception department is where employees enter their work details. The work details that employees enter include starting a new project and creating contracts. The reception department provides an interface for entering work details in text format and supports multiple input methods, such as voice input and image input. For example, voice recognition technology can be used to convert the work details dictated by employees into text data. Step 2: The analysis department uses generation AI to analyze the business content entered by the reception department and presents relevant laws, regulations, and risks. The analysis department uses text analysis technology to extract keywords from the business content and identify relevant laws, regulations, and risks based on these. It also uses data mining technology to refer to a database of past business content and related laws, regulations, and risks to present the most appropriate laws, regulations, and risks. For example, it can automatically search for laws, regulations, and risks related to the business content and present them as a list. Step 3: The Creation Department uses the generation AI to create documents by adding the necessary wording based on the laws and risks presented by the analysis department. The Creation Department automatically adds the legal wording required for contract creation and creates documents that clearly state how to deal with risks. The generation AI can also be used to automatically create documents such as materials and emails. For example, it can automatically create risk countermeasure materials and automatically generate emails to send to relevant departments. Step 4: The Sending Department is the department that sends the documents created by the Creation Department. The Sending Department sends the created contract to the relevant parties. The created materials can also be sent to relevant departments within the company. For example, the created documents can be sent automatically using an email sending system.

[0064] (Example 2) A corporate legal advice tool according to an embodiment of the present invention is a system in which, when a business operation is input, a generation AI presents relevant laws, regulations, risks, and countermeasures, and automatically creates and sends documents, materials, emails, etc., adding necessary wording. In this system, an employee inputs their business operation details, and the generation AI analyzes the details and presents relevant laws and risks. For example, a business operation such as starting a new project or creating a contract is input. This information is input into the generation AI, which analyzes the input business operation details and presents relevant laws and risks. For example, based on the project details, the generation AI identifies relevant security and copyright laws and presents risks associated with them. Furthermore, the generation AI automatically creates documents, materials, emails, etc., adding necessary wording, etc., based on the presented laws and risks. For example, when creating a contract, the generation AI automatically adds necessary legal wording and creates a document that clearly states countermeasures to risks. Finally, the generation AI automatically sends the created documents, materials, emails, etc., for example, by sending the contract to the relevant parties and sending risk countermeasure materials to relevant departments within the company. This tool expands the scope of automation by linking with internal systems and databases. For example, by linking with an internal project management system, it can automatically provide legal advice according to the progress of a project. In this way, a corporate legal advice tool that utilizes generative AI allows even employees who are not familiar with legal matters to quickly respond to relevant laws and risks, reducing issues related to security, copyright, etc. and contributing to improving employee work efficiency. This allows even employees who are not familiar with legal matters to quickly respond to relevant laws and risks.

[0065] The corporate legal advice tool according to the embodiment includes a reception unit, an analysis unit, a creation unit, and a sending unit. The reception unit is a unit through which employees input their work details. Examples of work details input by employees include, but are not limited to, starting a new project and creating contracts. The reception unit provides an interface for inputting the work details in text format. The reception unit can also support multiple input methods, such as voice input and image input. For example, the reception unit can convert the work details dictated by employees into text data using voice recognition technology. The analysis unit is a unit that uses generative AI to analyze the work details input by the reception unit and present relevant laws and risks. For example, the analysis unit can extract keywords from the work details using text analysis technology and identify relevant laws and risks based on the keywords. The analysis unit can also use data mining technology to refer to a database of laws and risks related to past work details and present the most appropriate laws and risks. For example, the analysis unit can automatically search for laws and risks related to the work details and present them as a list. The creation department uses a generation AI to create documents by adding necessary wording based on the laws and risks presented by the analysis department. For example, when creating a contract, the creation department automatically adds necessary legal wording and creates a document that clearly states how to address the risk. The creation department can also use the generation AI to automatically create documents such as materials and emails. For example, the creation department automatically creates risk countermeasure materials and automatically generates emails to send to relevant departments. The sending department is a department that sends documents created by the creation department. For example, the sending department sends created contracts to relevant parties. The sending department can also send created materials to relevant departments within the company. For example, the sending department automatically sends created documents using an email sending system. As a result, the corporate legal advice tool according to the embodiment automates the process from entering business details to presenting relevant laws and risks, and creating and sending documents with necessary wording, allowing even employees who are not familiar with legal matters to respond quickly.

[0066] The analysis unit may include a reference unit that references a database of relevant laws and risks. Examples of the database of relevant laws and risks include, but are not limited to, a laws and regulations database and a risk management database. For example, the analysis unit references the laws and regulations database to identify laws and regulations related to the business content. The analysis unit can also reference a risk management database to identify risks related to the business content. For example, the analysis unit automatically searches for laws and risks related to the business content and presents them as a list. By referencing the database of relevant laws and regulations and risks, the accuracy of the analysis is improved.

[0067] The sending unit may include a storage unit that stores the generated documents. The storage unit stores the generated documents in a digital format, for example. Digital storage may include, but is not limited to, cloud storage or an in-house server. The storage unit may also store the generated documents on paper. For example, the storage unit may print out the generated documents and store them as files. Furthermore, the storage unit may set a storage period. For example, the storage unit may store documents for a certain period based on legal requirements and then automatically delete them. This allows the created documents to be stored and referenced later.

[0068] The reception unit may include a linking unit that performs data integration with internal systems. The linking unit links with, for example, an internal project management system or database. Data integration includes, but is not limited to, API integration, database integration, real-time integration, and the like. For example, the linking unit uses an API to obtain project progress information from an internal system and provides legal advice based on that information. The linking unit can also use database integration to obtain related information from an internal legal database and provide the information to the analysis unit. For example, the linking unit uses real-time integration to automatically provide legal advice according to the progress of the project. This expands the scope of automation by performing data integration with internal systems.

[0069] The reception unit can analyze the user's emotions and adjust the input timing of the task content based on the analyzed user's emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions and adjusts the input timing. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the input timing. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations and adjusts the input timing. This adjusts the input timing according to the user's emotions, thereby reducing user stress and promoting efficient input. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] The reception unit can analyze the user's past work content input history and select an appropriate input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit predicts and suggests an input method to be used during a specific time period based on the user's past input history. The reception unit can also analyze patterns of work content previously input by the user and suggest an optimal input method. For example, the reception unit selects an optimal input method based on the user's past input history. In this way, the optimal input method can be suggested to the user by analyzing the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to a generation AI and have the generation AI select an optimal input method.

[0071] When inputting the task details, the reception unit can filter the task details based on the user's current project and areas of interest. For example, the reception unit can prioritize displaying task details related to the user's current project. For example, the reception unit can filter and display related task details based on the user's areas of interest. The reception unit can also suggest related task details based on areas in which the user has previously shown interest. For example, the reception unit can identify the user's current project and areas of interest and filter the task details based on the identified project and areas of interest. This allows highly relevant information to be prioritized and displayed by filtering the task details based on the user's current project and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's project data and area of ​​interest data to a generation AI and have the generation AI perform the filtering.

[0072] The reception unit can analyze the user's emotions and determine the priority of the tasks to be input based on the analyzed user's emotions. For example, if the user is feeling stressed, the reception unit postpones tasks of lower importance. For example, if the user is relaxed, the reception unit can prioritize input of tasks of higher importance. Furthermore, if the user is in a hurry, the reception unit can prioritize input of tasks that require a quick response. For example, the reception unit can analyze the user's emotions and determine the priority of the tasks based on the emotion score. This enables efficient business processing by determining the priority of the tasks according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] When inputting task details, the reception unit can prioritize inputting highly relevant task details by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying task details related to that area. For example, the reception unit can prioritize input of task details related to locations close to the user's current location. The reception unit can also prioritize suggesting task details related to locations the user has visited in the past. For example, the reception unit can filter task details based on the user's geographical location information and prioritize displaying highly relevant information. This allows highly relevant task details to be input preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's geographical location data to a generation AI and cause the generation AI to select highly relevant task details.

[0074] When inputting job content, the reception unit can analyze the user's social media activity and input related job content. For example, the reception unit can prioritize displaying job content related to topics mentioned by the user on social media. For example, the reception unit can analyze the user's social media posts and suggest related job content. The reception unit can also input related job content based on the activity of accounts the user follows. For example, the reception unit can filter job content based on the user's social media activity and prioritize displaying highly relevant information. In this way, by analyzing the user's social media activity, related job content can be input preferentially. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data to a generation AI and cause the generation AI to select related job content.

[0075] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit relaxes the analysis criteria and provides simple analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. The analysis unit can also adjust the criteria to provide analysis results quickly if the user is in a hurry. For example, the analysis unit analyzes the user's emotions and adjusts the analysis criteria based on the emotion score. This makes it possible to provide appropriate analysis results by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the work contents during analysis. The analysis unit, for example, analyzes the interrelationships between the work contents and identifies related laws and regulations and risks. For example, the analysis unit evaluates the extent of the impact of risks by taking into account the interrelationships between the work contents. The analysis unit can also propose optimal countermeasures based on the interrelationships between the work contents. For example, the analysis unit analyzes the interrelationships between the work contents and identifies related laws and regulations and risks. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between the work contents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input interrelationship data between the work contents into a generation AI and cause the generation AI to improve the accuracy of the analysis.

[0077] During analysis, the analysis unit can take into account the attribute information of the person who submitted the work content. The analysis unit can identify relevant laws and risks based on, for example, the submitter's job title and responsibilities. For example, the analysis unit can evaluate the extent of the risk's impact by taking into account the submitter's past work history. The analysis unit can also propose optimal countermeasures based on the submitter's attribute information. For example, the analysis unit can analyze the submitter's attribute information and identify relevant laws and risks. By taking the submitter's attribute information into account, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the submitter's attribute information data into a generation AI and have the generation AI perform the analysis.

[0078] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit postpones analysis results of lower importance. For example, if the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results that require a quick response. For example, the analysis unit analyzes the user's emotions and adjusts the display order of the analysis results based on the emotion score. This allows important information to be displayed preferentially by adjusting the display order of the analysis results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The analysis unit can perform the analysis while taking into account the geographic distribution of the work content. For example, the analysis unit analyzes the geographic distribution of the work content and identifies the laws and risks for each region. For example, the analysis unit evaluates the impact range of the risk by taking the geographic distribution into account. The analysis unit can also propose optimal countermeasures based on the geographic distribution. For example, the analysis unit analyzes the geographic distribution of the work content and identifies the relevant laws and risks. In this way, the laws and risks for each region can be identified by taking the geographic distribution of the work content into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographic distribution data to a generation AI and have the generation AI perform the analysis.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the business content. The analysis unit, for example, improves the accuracy of the analysis by referring to literature on laws and regulations and risks related to the business content. For example, the analysis unit evaluates the extent of the impact of a risk based on the related literature. The analysis unit can also suggest optimal countermeasures by referring to the related literature. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to the business content. In this way, the accuracy of the analysis is improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0081] The creation unit can estimate the user's emotions and adjust the way words are expressed based on the estimated user emotions. For example, if the user is feeling stressed, the creation unit uses concise and clear words. For example, if the user is relaxed, the creation unit uses detailed and polite words. Furthermore, if the user is in a hurry, the creation unit can use words that can be quickly understood. For example, the creation unit analyzes the user's emotions and adjusts the way words are expressed based on the emotion score. This allows an appropriate document to be created by adjusting the way words are expressed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The creation unit can adjust the level of detail of the document based on the importance of the business content when creating the document. For example, the creation unit creates a detailed document for business content with high importance. For example, the creation unit creates a concise document for business content with low importance. The creation unit can also adjust the level of detail of the document according to the importance of the business content. For example, the creation unit evaluates the importance of the business content and determines the level of detail of the document based on that. In this way, an appropriate document can be created by adjusting the level of detail of the document according to the importance of the business content. Some or all of the above-mentioned processing in the creation unit may be performed using, or without, AI. For example, the creation unit can input importance data of the business content to a generation AI and have the generation AI adjust the level of detail of the document.

[0083] The creation unit can apply different document creation algorithms depending on the category of work content when creating documents. For example, when creating a contract, the creation unit can apply an algorithm that emphasizes legal language. For example, when creating a project report, the creation unit can apply an algorithm that describes progress in detail. Furthermore, when creating risk countermeasure materials, the creation unit can apply an algorithm that clearly describes details of risks and how to deal with them. For example, the creation unit can classify the category of work content and apply a document creation algorithm accordingly. This allows documents to be created efficiently by applying an appropriate document creation algorithm depending on the category of work content. Some or all of the above-mentioned processing in the creation unit can be performed using, for example, AI, or can be performed without using AI. For example, the creation unit can input business content category data into a generation AI and have the generation AI apply a document creation algorithm.

[0084] The creation unit can estimate the user's emotions and adjust the length of the document based on the estimated user's emotions. For example, if the user is feeling stressed, the creation unit creates a short, to-the-point document. For example, if the user is relaxed, the creation unit creates a longer document with detailed explanations. The creation unit can also create a short document that can be quickly understood if the user is in a hurry. For example, the creation unit analyzes the user's emotions and adjusts the length of the document based on the emotion score. This allows for the creation of an appropriate document by adjusting the length of the document according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] When creating documents, the creation unit can determine the priority of documents based on the submission time of the business content. For example, the creation unit prioritizes document creation for urgent business content. For example, the creation unit prioritizes document creation for business content with an upcoming submission deadline. The creation unit can also adjust the order of document creation based on the submission time. For example, the creation unit evaluates the submission time of the business content and determines the priority of documents based on that. This allows documents to be created at the appropriate time by determining the priority of documents based on the submission time of the business content. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input data on the submission time of business content to a generation AI and have the generation AI determine the priority of documents.

[0086] The creation unit can adjust the order of documents based on the relevance of the business content when creating documents. For example, the creation unit creates documents with priority for highly relevant business content. For example, the creation unit adjusts the creation order of documents based on the relevance of the business content. The creation unit can also adjust the order of documents to prioritize processing highly relevant business content. For example, the creation unit evaluates the relevance of the business content and determines the order of documents based on that. This allows documents to be created efficiently by adjusting the order of documents based on the relevance of the business content. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit can input relevance data of business content to a generation AI and have the generation AI adjust the order of documents.

[0087] The sending unit can estimate the user's emotions and adjust the timing of sending based on the estimated user emotions. For example, if the user is feeling stressed, the sending unit can delay the timing of sending to provide time for the user to relax. For example, if the user is relaxed, the sending unit can send immediately to efficiently proceed with work. Furthermore, if the user is in a hurry, the sending unit can also speed up the timing of sending to send quickly. For example, the sending unit can analyze the user's emotions and adjust the timing of sending based on the emotion score. This allows the timing of sending to be adjusted according to the user's emotions, thereby enabling sending at an appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] When sending, the sending unit can select the optimal sending method by taking into account the recipient's attribute information. For example, if the recipient is a company, the sending unit sends the email in an official email format. For example, if the recipient is an individual, the sending unit sends the email in a concise message format. In addition, if the recipient is international, the sending unit can select a multilingual sending method. For example, the sending unit selects a sending method based on the recipient's attribute information. This allows the optimal sending method to be selected by taking into account the recipient's attribute information. Some or all of the above-described processing in the sending unit may be performed using AI, for example, or may be performed without using AI. For example, the sending unit can input the recipient's attribute information data into the generation AI and have the generation AI select the sending method.

[0089] The sending unit can determine the priority of sending based on the importance of the content when sending. For example, the sending unit prioritizes sending content with high importance. For example, the sending unit postpones sending content with low importance. The sending unit can also adjust the priority of sending based on the importance of the content. For example, the sending unit evaluates the importance of the content and determines the priority of sending based on that. In this way, important content can be sent preferentially by determining the priority of sending based on the importance of the content. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input importance data of the content to the generation AI and have the generation AI determine the priority of sending.

[0090] The sending unit can estimate the user's emotions and adjust the display method of the sent content based on the estimated user emotions. For example, if the user is feeling stressed, the sending unit provides a simple and highly visible display method. For example, if the user is relaxed, the sending unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the sending unit can also provide a display method that focuses on the main points. For example, the sending unit analyzes the user's emotions and adjusts the display method of the sent content based on the emotion score. In this way, an appropriate display method can be provided by adjusting the display method of the sent content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] At the time of sending, the sending unit can select the optimal sending method by taking into consideration the geographical location information of the destination. For example, if the destination is domestic, the sending unit selects a domestic sending method. For example, if the destination is overseas, the sending unit sends by international mail or international email. The sending unit can also select the optimal sending method based on the geographical location information of the destination. For example, the sending unit selects a sending method based on the geographical location information of the destination. In this way, the optimal sending method can be selected by taking into consideration the geographical location information of the destination. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input geographical location information data of the destination into the generation AI and have the generation AI select the sending method.

[0092] The sending unit can improve the accuracy of the sending by referring to literature related to the sending content when sending. The sending unit improves the accuracy of the sending, for example, by referring to literature on laws and regulations and risks related to the sending content. For example, the sending unit complements the details of the sending content based on the related literature. The sending unit can also confirm the accuracy of the sending content by referring to the related literature. For example, the sending unit improves the accuracy of the sending by referring to literature related to the sending content. In this way, the accuracy of the sending is improved by referring to the related literature. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the sending.

[0093] The reference unit can estimate the user's emotions and determine the priority of data to be referenced based on the estimated user emotions. For example, if the user is feeling stressed, the reference unit postpones data of lower importance. For example, if the user is relaxed, the reference unit can prioritize reference to detailed data. Furthermore, if the user is in a hurry, the reference unit can prioritize reference to data that requires a quick response. For example, the reference unit can analyze the user's emotions and determine the priority of data to be referenced based on the emotion score. This allows appropriate data to be referenced preferentially by determining the priority of data to be referenced according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] At the time of reference, the reference unit can analyze past reference history and select optimal data. For example, the reference unit preferentially displays data that the user has frequently referenced in the past. For example, the reference unit predicts and suggests data to be used during a specific time period based on the user's past reference history. The reference unit can also analyze the user's past reference history and select optimal data. For example, the reference unit selects optimal data based on the past reference history. In this way, optimal data can be selected by analyzing the past reference history. Some or all of the above-described processing in the reference unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference unit can input past reference history data to a generation AI and have the generation AI select optimal data.

[0095] The reference unit can estimate the user's emotion and adjust the display method of the reference data based on the estimated user emotion. For example, if the user is feeling stressed, the reference unit provides a concise and highly visible display method. For example, if the user is relaxed, the reference unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the reference unit can also provide a display method that focuses on the main points. For example, the reference unit analyzes the user's emotion and adjusts the display method of the reference data based on the emotion score. In this way, an appropriate display method can be provided by adjusting the display method of the reference data according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] The reference unit can adjust the order of reference based on the relevance of the data during reference. The reference unit, for example, preferentially displays highly relevant data. For example, the reference unit adjusts the order of reference based on the relevance of the data. The reference unit can also adjust the order to preferentially reference highly relevant data. For example, the reference unit evaluates the relevance of the data and determines the order of reference based on that. This allows data to be referenced efficiently by adjusting the order of reference based on the relevance of the data. Some or all of the above-mentioned processing in the reference unit may be performed using AI, for example, or may be performed without using AI. For example, the reference unit can input data on the relevance of the data to a generation AI and have the generation AI adjust the order of reference.

[0097] The storage unit can estimate the user's emotions and determine the priority of data to be saved based on the estimated user emotions. For example, if the user is feeling stressed, the storage unit postpones data of lower importance. For example, if the user is relaxed, the storage unit can prioritize saving detailed data. Furthermore, if the user is in a hurry, the storage unit can prioritize saving data that requires a quick response. For example, the storage unit can analyze the user's emotions and determine the priority of data to be saved based on an emotion score. This allows appropriate data to be saved preferentially by determining the priority of data to be saved according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The storage unit can analyze past storage history and select the optimal storage method when saving data. For example, the storage unit prioritizes saving data that the user has frequently saved in the past. For example, the storage unit predicts and suggests data to save during a specific time period based on the user's past storage history. The storage unit can also analyze the user's past storage history and select the optimal storage method. For example, the storage unit selects the optimal storage method based on the past storage history. In this way, the optimal storage method can be selected by analyzing the past storage history. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input past storage history data to a generation AI and have the generation AI select the optimal storage method.

[0099] The storage unit can estimate the user's emotions and adjust the display method of the stored data based on the estimated user emotions. For example, if the user is feeling stressed, the storage unit provides a concise and highly visible display method. For example, if the user is relaxed, the storage unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the storage unit can also provide a display method that focuses on the main points. For example, the storage unit analyzes the user's emotions and adjusts the display method of the stored data based on the emotion score. In this way, an appropriate display method can be provided by adjusting the display method of the stored data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] The storage unit can adjust the order of storage based on the relevance of the data when storing the data. For example, the storage unit prioritizes storing highly relevant data. For example, the storage unit adjusts the order of storage based on the relevance of the data. The storage unit can also adjust the order to prioritize storing highly relevant data. For example, the storage unit evaluates the relevance of the data and determines the order of storage based on the evaluation. In this way, data can be stored efficiently by adjusting the order of storage based on the relevance of the data. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data on the relevance of the data to a generation AI and have the generation AI adjust the order of storage.

[0101] The linking unit can estimate the user's emotions and determine the priority of data to be linked based on the estimated user emotions. For example, if the user is feeling stressed, the linking unit postpones data of lower importance. For example, if the user is relaxed, the linking unit prioritizes linking of detailed data. Furthermore, if the user is in a hurry, the linking unit can also prioritize linking of data that requires a quick response. For example, the linking unit analyzes the user's emotions and determines the priority of data to be linked based on the emotion score. This allows appropriate data to be linked preferentially by determining the priority of data to be linked according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] At the time of linking, the linking unit can analyze past linking history and select the optimal linking method. For example, the linking unit prioritizes linking data that the user has frequently linked in the past. For example, the linking unit predicts and suggests data to link during a specific time period based on the user's past linking history. The linking unit can also analyze the user's past linking history and select the optimal linking method. For example, the linking unit selects the optimal linking method based on the past linking history. In this way, the optimal linking method can be selected by analyzing the past linking history. Some or all of the above-described processing in the linking unit may be performed using, or without, AI, for example. For example, the linking unit can input past linking history data into a generation AI and cause the generation AI to select the optimal linking method.

[0103] The linking unit can estimate the user's emotion and adjust the display method of the linked data based on the estimated user emotion. For example, if the user is feeling stressed, the linking unit provides a simple, highly visible display method. For example, if the user is relaxed, the linking unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the linking unit can also provide a display method that focuses on the main points. For example, the linking unit analyzes the user's emotion and adjusts the display method of the linked data based on the emotion score. This makes it possible to provide an appropriate display method by adjusting the display method of the linked data according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The linking unit can adjust the linking order based on the relevance of the data when linking. For example, the linking unit prioritizes linking highly related data. For example, the linking unit adjusts the linking order based on the relevance of the data. The linking unit can also adjust the order to prioritize linking highly related data. For example, the linking unit evaluates the relevance of the data and determines the linking order based on that. In this way, data can be linked efficiently by adjusting the linking order based on the relevance of the data. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input data on the relevance of the data to the generation AI and cause the generation AI to adjust the linking order. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, creation unit, and sending unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and provides an interface for employees to input work details. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes work details using a generation AI and presents relevant laws and regulations and risks. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates documents with necessary wording added. The sending unit sends the created documents to relevant parties, for example, using the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, creation unit, and sending unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and provides an interface for employees to input work details by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes work details using a generation AI and presents relevant laws and regulations and risks. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates documents with necessary wording added. The sending unit sends the created documents to relevant parties, for example, using the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, creation unit, and sending unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and provides an interface for employees to input work details by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes work details using a generation AI and presents relevant laws and regulations and risks. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates documents with necessary wording added. The sending unit sends the created documents to relevant parties, for example, using the communication I / F 44 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, creation unit, and sending unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and provides an interface for employees to input work details by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes work details using a generative AI and presents relevant laws and regulations and risks. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates documents with necessary wording added. The sending unit sends the documents created to the relevant parties using, for example, the communication I / F 44 of the robot 414.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also predict and suggest the input method to be used during a specific time period based on the user's past input history. It can also analyze patterns of work content that the user has entered in the past and suggest the optimal input method. In this way, it is possible to suggest the optimal input method to the user by analyzing the past input history.

[0107] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between work contents. For example, it can analyze the interrelationships between work contents and identify related laws and regulations and risks. It can also evaluate the extent of the impact of risks by taking into account the interrelationships between work contents. Furthermore, it can propose optimal countermeasures based on the interrelationships between work contents. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between work contents.

[0108] When sending, the sending unit can select the optimal sending method by taking into consideration the recipient's attribute information. For example, if the recipient is a company, it can be sent in an official email format. If the recipient is an individual, it can also be sent in a simple message format. Furthermore, if the recipient is international, it can also select a sending method that supports multiple languages. This allows the optimal sending method to be selected by taking into consideration the recipient's attribute information.

[0109] The reception unit can analyze the user's emotions and adjust the timing of inputting task content based on the analyzed user emotions. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. The user's voice can also be recorded and the emotion can be estimated using voice analysis technology. Furthermore, the user's biometric data (heart rate and electrodermal activity) can be collected with a sensor and the emotion can be estimated using an emotion estimation algorithm. In this way, adjusting the input timing according to the user's emotions reduces user stress and promotes efficient input.

[0110] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is feeling stressed, the analysis criteria can be relaxed to provide simple analysis results. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, the criteria can be adjusted to provide analysis results quickly. In this way, by adjusting the analysis criteria according to the user's emotions, appropriate analysis results can be provided.

[0111] When creating a document, the creation unit can adjust the level of detail of the document based on the importance of the business content. For example, a detailed document can be created for business content with high importance. Also, a concise document can be created for business content with low importance. Furthermore, the level of detail of the document can be adjusted according to the importance of the business content. In this way, by adjusting the level of detail of the document according to the importance of the business content, an appropriate document can be created.

[0112] The creation unit can estimate the user's emotions and adjust the way words are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, concise and clear words can be used. If the user is relaxed, detailed and polite words can be used. Furthermore, if the user is in a hurry, words that can be quickly understood can be used. In this way, an appropriate document can be created by adjusting the way words are expressed according to the user's emotions.

[0113] The sending unit can determine the priority of sending based on the importance of the content when sending. For example, it can send content with high importance first. It can also postpone sending content with low importance. Furthermore, it can adjust the priority of sending based on the importance of the content. In this way, by determining the priority of sending based on the importance of the content, it is possible to send important content with priority.

[0114] The sending unit can estimate the user's emotions and adjust the timing of sending based on the estimated user emotions. For example, if the user is feeling stressed, the timing of sending can be delayed to provide time for the user to relax. Also, if the user is relaxed, the sending can be performed immediately to efficiently carry out work. Furthermore, if the user is in a hurry, the timing of sending can be advanced to send quickly. In this way, by adjusting the timing of sending according to the user's emotions, sending can be performed at an appropriate time.

[0115] The reference unit can adjust the order of reference based on the relevance of data when referencing. For example, highly relevant data is displayed preferentially. The order of reference can also be adjusted based on the relevance of data. Furthermore, the order can be adjusted so that highly relevant data is referenced preferentially. In this way, adjusting the order of reference based on the relevance of data allows efficient data referencing.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception department is where employees enter their work details. The work details that employees enter include starting a new project and creating contracts. The reception department provides an interface for entering work details in text format and supports multiple input methods, such as voice input and image input. For example, voice recognition technology can be used to convert the work details dictated by employees into text data. Step 2: The analysis department uses generation AI to analyze the business content entered by the reception department and presents relevant laws, regulations, and risks. The analysis department uses text analysis technology to extract keywords from the business content and identify relevant laws, regulations, and risks based on these. It also uses data mining technology to refer to a database of past business content and related laws, regulations, and risks to present the most appropriate laws, regulations, and risks. For example, it can automatically search for laws, regulations, and risks related to the business content and present them as a list. Step 3: The Creation Department uses the generation AI to create documents by adding the necessary wording based on the laws and risks presented by the analysis department. The Creation Department automatically adds the legal wording required for contract creation and creates documents that clearly state how to deal with risks. The generation AI can also be used to automatically create documents such as materials and emails. For example, it can automatically create risk countermeasure materials and automatically generate emails to send to relevant departments. Step 4: The Sending Department is the department that sends the documents created by the Creation Department. The Sending Department sends the created contract to the relevant parties. The created materials can also be sent to relevant departments within the company. For example, the created documents can be sent automatically using an email sending system.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a 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.

[0156] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0169] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where work details are entered; an analysis unit that analyzes the business content input by the reception unit and presents relevant laws and regulations and risks; a creation unit that creates documents by adding necessary wording based on the laws and regulations and risks presented by the analysis unit; a sending unit that sends the document created by the creation unit; Equipped with A system characterized by:

2. The analysis unit Includes a reference section that refers to a database of relevant laws and risks 2. The system of claim 1.

3. The sending unit A storage unit for storing generated documents is provided.

2. The system of claim 1.

4. The reception unit Equipped with a linking section that links data with internal systems 2. The system of claim 1.

5. The reception unit Analyze user emotions and adjust the timing of inputting work content based on the analyzed user emotions 2. The system of claim 1.

6. The reception unit Analyze the user's past work content input history and select the appropriate input method 2. The system of claim 1.

7. The reception unit Filter job descriptions based on users' current projects and areas of interest 2. The system of claim 1.

8. The reception unit Analyze user emotions and prioritize the tasks to be entered based on the analyzed user emotions.

2. The system of claim 1.

9. The reception unit When entering job details, the system takes into account the user's geographic location information and prioritizes the entry of relevant job details.

2. The system of claim 1.

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