System

Abstract

An object of a system according to an embodiment is to efficiently perform a procedure for coping with an enormous number of laws and regulations.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes an appropriate procedure based on the analysis result obtained by the analysis unit. The provision unit provides a specific procedure for executing the procedure proposed by the proposal unit.SELECTED DRAWING: Figure 1

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] Conventional technology has had the problem that the procedures required to comply with the vast number of laws and regulations are complicated and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently carry out procedures to comply with a huge number of laws and regulations. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes an appropriate procedure based on the analysis results obtained by the analysis unit. The provision unit provides specific steps for carrying out the procedure proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently carry out procedures to comply with a vast number of laws and regulations. [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) The efficiency improvement system according to an embodiment of the present invention is a system for improving efficiency and providing services using a generative AI to comply with the vast number of laws and regulations, including the Companies Act, which has 979 articles and 460,000 characters in length. This efficiency improvement system collects and analyzes information, proposes optimal procedures, and provides specific steps. For example, the efficiency improvement system uses a generative AI to collect and analyze information on each process, from drafting articles of incorporation to investment, company establishment, stock handling, and holding general shareholders' meetings. The generative AI then proposes optimal procedures for each process based on the analysis results. These proposals are customized based on information entered by the user. Furthermore, the generative AI provides specific steps for implementing the proposed procedures. This allows many companies to significantly reduce the effort required to comply with the Companies Act. For example, in drafting articles of incorporation and company establishment procedures, the generative AI proposes optimal procedures and provides specific steps, allowing companies to proceed with the procedures quickly and efficiently. Furthermore, in stock handling and holding general shareholders' meetings, implementing the procedures proposed by the generative AI can improve business efficiency while ensuring compliance with laws and regulations.

[0029] An efficiency improvement system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects information. The collection unit collects information, such as provisions of the Companies Act, related laws and regulations, and past cases. The collection unit can also use a generation AI to comprehensively collect information on each process, from creating articles of incorporation to investment, company establishment, stock handling, and holding a general shareholders' meeting. For example, the collection unit uses the generation AI to collect information such as the company's purpose, trade name, head office location, and investment amount at the time of establishment. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also use the generation AI to analyze the collected information and comprehensively collect information necessary for each process. For example, the analysis unit extracts information necessary for each process based on the information collected by the generation AI. The proposal unit proposes optimal procedures based on the analysis results obtained by the analysis unit. The proposal unit proposes appropriate procedures, such as legal procedures, business procedures, and technical procedures. The proposal unit can also use the generation AI to propose customized procedures based on information entered by the user. For example, the proposal unit suggests procedures for preparing documents required for company establishment, where to submit them, and deadlines for submission. The provision unit provides specific procedures for carrying out the procedures proposed by the proposal unit. The provision unit provides specific procedures, such as step-by-step instructions, required documents, and where to submit them. The provision unit can also use the generation AI to provide specific procedures for carrying out the proposed procedures. For example, the provision unit indicates specific instructions for preparing and sending a notice of shareholders' meeting, how to prepare minutes, and so on when holding a general shareholders' meeting. This allows the efficiency improvement system according to the embodiment to efficiently collect, analyze, propose, and provide information.

[0030] The efficiency improvement system includes a reception unit that receives information input by a user. The reception unit receives the information input by a user. The reception unit can receive the information input by a user in the form of, for example, text information, numerical information, image information, or the like. The reception unit can also use a generation AI to efficiently receive the information input by a user. For example, the reception unit uses a generation AI to analyze the information input by a user and extract necessary information. This allows the information input by a user to be efficiently received.

[0031] The suggestion unit can propose customized procedures based on information input by the user. For example, the suggestion unit proposes the procedure for preparing documents required for company establishment, where to submit them, and the deadline for submission, based on the information input by the user. The suggestion unit can also use the generation AI to propose the optimal procedure based on the user's specific situation. For example, the suggestion unit uses the generation AI to propose procedures that meet specific conditions based on the information input by the user. This makes it possible to propose the optimal procedure based on the user's specific situation.

[0032] The provision unit can provide specific steps for carrying out the proposed procedures. For example, when holding a general shareholders' meeting, the provision unit can provide specific instructions such as how to prepare and send a convocation notice and how to prepare minutes. The provision unit can also use the generation AI to provide specific steps for carrying out the proposed procedures. For example, the provision unit can have the generation AI provide step-by-step instructions to enable the user to smoothly carry out the procedures. This allows the proposed procedures to be carried out smoothly.

[0033] The collection department can collect information on provisions of the Companies Act, related laws and regulations, and past cases. For example, the collection department comprehensively collects information such as provisions of the Companies Act, related laws and regulations, and past cases. The collection department can also use generation AI to efficiently collect necessary information. For example, the collection department uses generation AI to collect information such as specific chapters and provisions of the Companies Act, related laws and regulations, precedents, and industry best practices. This allows the collection of necessary information comprehensively.

[0034] The analysis unit can analyze the collected information and collect all the information necessary for each process. The analysis unit analyzes the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also use the generation AI to analyze the collected information and comprehensively collect the information necessary for each process. For example, the analysis unit extracts the information necessary for each process based on the information collected by the generation AI. This makes it possible to comprehensively collect the information necessary for each process.

[0035] The proposal unit can propose procedures for preparing documents required for company establishment, where to submit them, deadlines for submission, etc. The proposal unit can also use the generation AI to propose optimal procedures based on the user's specific situation. For example, the proposal unit can use the generation AI to propose procedures based on specific conditions based on information input by the user. This allows the procedures required for company establishment to be carried out efficiently.

[0036] The provision department can provide specific instructions on how to prepare and send a convocation notice, how to prepare minutes, etc., when holding a general shareholders' meeting. For example, the provision department can provide specific instructions on how to prepare and send a convocation notice, how to prepare minutes, etc., when holding a general shareholders' meeting. The provision department can also use the generation AI to provide specific procedures for carrying out the proposed procedures. For example, the provision department can have the generation AI provide step-by-step instructions, allowing the user to carry out the procedures smoothly. This allows the procedures for the general shareholders' meeting to be carried out efficiently.

[0037] When collecting information, the collection unit can analyze the user's past legal compliance history and select the optimal collection method. For example, the collection unit uses a generation AI to select the optimal collection method based on the legal compliance methods used by the user in the past. The collection unit can also prioritize the collection of information on specific laws and regulations from the user's past legal compliance history. Furthermore, the collection unit can analyze the user's past legal compliance history and suggest efficient collection methods. This enables efficient information collection by selecting the optimal collection method based on the user's past legal compliance history.

[0038] The collection unit can perform filtering based on the user's current work situation and areas of interest when collecting information. For example, the collection unit takes into consideration the user's current work situation and prioritizes collecting relevant legal information. The collection unit can also filter and collect specific legal information based on the user's areas of interest. Furthermore, the collection unit can combine the user's work situation and areas of interest to collect optimal legal information. This makes it possible to efficiently collect information according to the user's work situation and areas of interest.

[0039] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, when the user uses voice input, the collection unit has the generation AI collect information using voice recognition technology. In addition, when the user uses text input, the collection unit can also have the generation AI collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also have the generation AI collect information using image recognition technology. This enables efficient information collection by selecting the optimal collection means depending on the user's input method.

[0040] When collecting data, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting legal information related to that area. The collection unit can also collect area-specific legal information based on the user's geographical location information. Furthermore, the collection unit can combine the user's current location with related legal information to collect optimal information. This makes it possible to prioritize collecting highly relevant information based on the user's geographical location information.

[0041] At the time of collection, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the content posted by the user on social media and collect related legal information. The collection unit can also collect legal information of interest based on the user's social media activity history. Furthermore, the collection unit can also collect related legal information by referring to the activities of the user's friends on social media. This makes it possible to efficiently collect related information based on the user's social media activities.

[0042] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit adjusts the collection method using the generation AI based on feedback provided by the user in the past. The collection unit can also prioritize the use of specific information collection methods based on the user's past feedback. Furthermore, the collection unit can also optimize the collection method by reflecting the user's feedback and collect information efficiently. This enables efficient information collection by customizing the collection method by reflecting the user's past feedback.

[0043] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit allows the generation AI to perform a detailed analysis of important information. The analysis unit can also allow the generation AI to perform a simplified analysis of less important information. Furthermore, the analysis unit allows the generation AI to dynamically adjust the level of detail of the analysis based on the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information.

[0044] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, in the analysis unit, the generation AI applies a specific analysis algorithm to legal information. In addition, in the analysis unit, the generation AI can apply a different analysis algorithm to past case information. Furthermore, in the analysis unit, the generation AI can select the optimal analysis algorithm depending on the category of information. This enables efficient analysis by applying the optimal analysis algorithm depending on the category of information.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit allows the generation AI to improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also refer to the user's past analysis results and allow the generation AI to adjust the analysis algorithm. Furthermore, the analysis unit can analyze the user's past analysis results and allow the generation AI to select the optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0046] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit allows the generation AI to prioritize analysis of information with high urgency. The analysis unit can also allow the generation AI to prioritize analysis of information with an approaching submission deadline. Furthermore, the analysis unit can also allow the generation AI to dynamically adjust the priority of analysis based on the time of submission of information. In this way, by determining the priority of analysis based on the time of submission of information, it is possible to prioritize analysis of information with high urgency.

[0047] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit allows the generation AI to prioritize analysis of highly relevant information. The analysis unit can also allow the generation AI to postpone analysis of less relevant information. Furthermore, the analysis unit can also allow the generation AI to dynamically adjust the order of analysis based on the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, highly relevant information can be analyzed preferentially.

[0048] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can have the generation AI provide analysis results that use a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit can have the generation AI provide analysis results in simple language. Furthermore, the analysis unit can also have the generation AI dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0049] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the procedure. For example, the suggestion unit allows the generation AI to make detailed proposals for important procedures. The suggestion unit can also allow the generation AI to make simplified proposals for procedures with low importance. Furthermore, the suggestion unit allows the generation AI to dynamically adjust the level of detail of the proposal based on the importance of the procedure. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the procedure.

[0050] When making a proposal, the proposal department can apply different proposal algorithms depending on the procedure category. For example, the proposal department's generation AI applies a specific proposal algorithm to company establishment procedures. The proposal department can also apply a different proposal algorithm to stock handling procedures. Furthermore, the proposal department can also select the optimal proposal algorithm for the generation AI depending on the procedure category. This enables efficient proposals by applying the optimal proposal algorithm depending on the procedure category.

[0051] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit allows the generation AI to improve the accuracy of the proposal based on the user's past proposal results. The suggestion unit can also refer to the user's past proposal results and allow the generation AI to adjust the proposal algorithm. Furthermore, the suggestion unit can analyze the user's past proposal results and allow the generation AI to select the optimal proposal method. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results.

[0052] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the procedure. For example, the suggestion unit allows the generation AI to give priority to proposals for procedures with a high level of urgency. The suggestion unit can also allow the generation AI to give priority to proposals for procedures with an approaching submission deadline. Furthermore, the suggestion unit can also allow the generation AI to dynamically adjust the priority of the proposal based on the time of submission of the procedure. In this way, by determining the priority of the proposal based on the time of submission of the procedure, it is possible to give priority to proposals for procedures with a high level of urgency.

[0053] The suggestion unit can adjust the order of suggestions based on the relevance of the procedures when making suggestions. For example, the suggestion unit can have the generation AI give priority to suggestions for highly relevant procedures. The suggestion unit can also have the generation AI put off proposing less relevant procedures. Furthermore, the suggestion unit can also have the generation AI dynamically adjust the order of suggestions based on the relevance of the procedures. In this way, by adjusting the order of suggestions based on the relevance of the procedures, highly relevant procedures can be suggested preferentially.

[0054] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can cause the generation AI to provide a proposal that uses a lot of technical terminology. Also, if the user does not have technical expertise, the suggestion unit can cause the generation AI to provide a proposal in simple language. Furthermore, the suggestion unit can also cause the generation AI to dynamically adjust the use of technical terminology in the proposal according to the user's level of expertise. This makes it possible to provide a proposal that is easy for the user to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise.

[0055] The providing unit can adjust the level of detail provided based on the importance of the procedure when providing the procedure. For example, the providing unit has the generating AI provide detailed procedures for important procedures. The providing unit can also have the generating AI provide simplified procedures for procedures with low importance. Furthermore, the providing unit can also have the generating AI dynamically adjust the level of detail provided according to the importance of the procedure. This allows for efficient procedure provision by adjusting the level of detail provided according to the importance of the procedure.

[0056] The provision unit can apply different provision algorithms depending on the procedure category when providing the procedures. For example, the generation AI in the provision unit applies a specific provision algorithm to company establishment procedures. The provision unit can also apply a different provision algorithm to stock handling procedures. Furthermore, the generation AI in the provision unit can select the optimal provision algorithm depending on the procedure category. This enables efficient procedure provision by applying the optimal provision algorithm depending on the procedure category.

[0057] The providing unit can improve the accuracy of provision by referring to the user's past provision results when providing the data. For example, the providing unit allows the generation AI to improve the accuracy of provision based on the user's past provision results. The providing unit can also refer to the user's past provision results and allow the generation AI to adjust the provision algorithm. Furthermore, the providing unit can analyze the user's past provision results and allow the generation AI to select the optimal provision method. In this way, the accuracy of provision can be improved by referring to the user's past provision results.

[0058] The provision unit can determine the priority of provision based on the time of submission of the procedure at the time of provision. For example, the provision unit allows the generation AI to provide procedures preferentially for procedures with high urgency. The provision unit can also allow the generation AI to provide procedures preferentially for procedures with an approaching submission deadline. Furthermore, the provision unit can allow the generation AI to dynamically adjust the priority of provision based on the time of submission of the procedure. In this way, by determining the priority of provision based on the time of submission of the procedure, it is possible to provide procedures with high urgency preferentially.

[0059] The providing unit can adjust the order of provision based on the relevance of the procedures when providing them. For example, the providing unit can have the generation AI provide procedures with priority for highly relevant procedures. The providing unit can also have the generation AI provide procedures with lower relevance at a later date. Furthermore, the providing unit can also have the generation AI dynamically adjust the order of provision based on the relevance of the procedures. In this way, by adjusting the order of provision based on the relevance of the procedures, highly relevant procedures can be provided with priority.

[0060] The providing unit can adjust the use of technical terminology in the provided instructions according to the user's level of expertise when providing the instructions. For example, if the user has technical expertise, the providing unit can have the generation AI provide instructions that use a lot of technical terminology. Also, if the user does not have technical expertise, the providing unit can have the generation AI provide instructions in simple language. Furthermore, the providing unit can also have the generation AI dynamically adjust the use of technical terminology in the provided instructions according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the provided instructions according to the user's level of expertise, it is possible to provide instructions that are easy for the user to understand.

[0061] The reception unit can select the optimal reception method by referring to the user's past input history when receiving a call. For example, the reception unit uses a generation AI to select the optimal reception method based on the user's past input history. The reception unit can also preferentially suggest a specific input method based on the user's past input history. Furthermore, the reception unit can analyze the user's past input history and suggest an efficient reception method. This allows for efficient reception by selecting the optimal reception method based on the user's past input history.

[0062] The reception unit can customize the reception content based on the user's current work situation when receiving a call. For example, the reception unit takes into account the user's current work situation and provides the relevant reception content through the generation AI. The reception unit can also have the generation AI propose the optimal reception method based on the user's work situation. Furthermore, the reception unit can analyze the user's work situation and provide the customized reception content through the generation AI. This enables efficient reception by providing reception content that suits the user's current work situation.

[0063] The reception unit can select the optimal reception method by taking into consideration the user's geographical location information when receiving a call. For example, if the user is in a specific area, the reception unit provides reception details related to that area. The reception unit can also suggest a reception method specific to the area based on the user's geographical location information. Furthermore, the reception unit can combine reception details related to the user's current location to provide the optimal reception method. This enables efficient reception by selecting the optimal reception method based on the user's geographical location information.

[0064] The reception unit can analyze the user's social media activity at the time of reception and suggest related reception contents. The reception unit can, for example, analyze the content posted by the user on social media and suggest related reception contents. The reception unit can also provide reception contents of interest based on the user's social media activity history. Furthermore, the reception unit can also suggest related reception contents by referring to the activities of the user's friends on social media. This enables efficient reception by suggesting related reception contents based on the user's social media activity.

[0065] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a call. For example, the reception unit adjusts the reception method using a generation AI based on feedback provided by the user in the past. The reception unit can also prioritize the use of a specific reception method based on the user's past feedback. Furthermore, the reception unit can also optimize the reception method by reflecting the user's feedback and perform efficient reception. As a result, efficient reception is possible by customizing the reception method by reflecting the user's past feedback.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] During analysis, the analysis unit can determine the priority of analysis based on the reliability of the information. For example, the generation AI can prioritize analysis of highly reliable information. Also, the generation AI can postpone analysis of less reliable information. Furthermore, the analysis unit can allow the generation AI to dynamically adjust the priority of analysis based on the reliability of the information. This allows highly reliable information to be analyzed preferentially by determining the priority of analysis based on the reliability of the information.

[0068] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion history. For example, the generation AI makes the optimal suggestion based on suggestions the user has received in the past. The suggestion unit can also prioritize the use of a specific suggestion method based on the user's past suggestion history. Furthermore, the suggestion unit can analyze the user's past suggestion history and suggest an efficient suggestion method. This enables efficient suggestions by improving the accuracy of the suggestion based on the user's past suggestion history.

[0069] The provision unit can customize the content of the provision based on the user's current work situation when providing the procedure. For example, the generation AI provides a relevant procedure taking into account the user's current work situation. The provision unit can also have the generation AI propose an optimal procedure based on the user's work situation. Furthermore, the provision unit can analyze the user's work situation and have the generation AI provide a customized procedure. This enables efficient procedure provision by providing a procedure that suits the user's current work situation.

[0070] The reception unit can select the optimal reception method by taking into account the user's geographical location information when receiving a call. For example, if the user is in a specific area, reception details related to that area are provided. The reception unit can also suggest a reception method specific to the area based on the user's geographical location information. Furthermore, the reception unit can combine reception details related to the user's current location to provide the optimal reception method. This allows for efficient reception by selecting the optimal reception method based on the user's geographical location information.

[0071] During collection, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the content posted by the user on social media and collect related legal information. The collection unit can also collect legal information of interest based on the user's social media activity history. Furthermore, the collection unit can also collect related legal information by referring to the activities of the user's friends on social media. This makes it possible to efficiently collect related information based on the user's social media activities.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection department collects information. For example, the collection department collects information such as provisions of the Companies Act, related laws and regulations, and past cases. The collection department can also use the generation AI to comprehensively collect information on each process, from creating articles of incorporation to investment, company establishment, stock handling, and holding general shareholders' meetings. For example, the collection department uses the generation AI to collect information such as the company's purpose, trade name, head office location, and amount of investment at the time of establishment. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also use the generation AI to analyze the collected information and comprehensively collect the information necessary for each process. For example, the analysis unit extracts the information necessary for each process based on the information collected by the generation AI. Step 3: The proposal unit proposes optimal procedures based on the analysis results obtained by the analysis unit. The proposal unit proposes appropriate procedures, such as legal procedures, business procedures, and technical procedures. The proposal unit can also use the generation AI to propose customized procedures based on information entered by the user. For example, the proposal unit uses the generation AI to propose procedures for preparing documents required for company establishment, as well as where and when to submit them and the deadline for submission. Step 4: The provision department provides specific procedures for carrying out the procedures proposed by the proposal department. The provision department provides specific procedures, such as step-by-step instructions, required documents, and where to submit them. The provision department can also use the generation AI to provide specific procedures for carrying out the proposed procedures. For example, the provision department may have the generation AI provide specific instructions on how to prepare and send a convocation notice, how to prepare minutes, etc., when holding a general shareholders' meeting.

[0074] (Example 2) The efficiency improvement system according to an embodiment of the present invention is a system for improving efficiency and providing services using a generative AI to comply with the vast number of laws and regulations, including the Companies Act, which has 979 articles and 460,000 characters in length. This efficiency improvement system collects and analyzes information, proposes optimal procedures, and provides specific steps. For example, the efficiency improvement system uses a generative AI to collect and analyze information on each process, from drafting articles of incorporation to investment, company establishment, stock handling, and holding general shareholders' meetings. The generative AI then proposes optimal procedures for each process based on the analysis results. These proposals are customized based on information entered by the user. Furthermore, the generative AI provides specific steps for implementing the proposed procedures. This allows many companies to significantly reduce the effort required to comply with the Companies Act. For example, in drafting articles of incorporation and company establishment procedures, the generative AI proposes optimal procedures and provides specific steps, allowing companies to proceed with the procedures quickly and efficiently. Furthermore, in stock handling and holding general shareholders' meetings, implementing the procedures proposed by the generative AI can improve business efficiency while ensuring compliance with laws and regulations.

[0075] An efficiency improvement system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects information. The collection unit collects information, such as provisions of the Companies Act, related laws and regulations, and past cases. The collection unit can also use a generation AI to comprehensively collect information on each process, from creating articles of incorporation to investment, company establishment, stock handling, and holding a general shareholders' meeting. For example, the collection unit uses the generation AI to collect information such as the company's purpose, trade name, head office location, and investment amount at the time of establishment. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also use the generation AI to analyze the collected information and comprehensively collect information necessary for each process. For example, the analysis unit extracts information necessary for each process based on the information collected by the generation AI. The proposal unit proposes optimal procedures based on the analysis results obtained by the analysis unit. The proposal unit proposes appropriate procedures, such as legal procedures, business procedures, and technical procedures. The proposal unit can also use the generation AI to propose customized procedures based on information entered by the user. For example, the proposal unit suggests procedures for preparing documents required for company establishment, where to submit them, and deadlines for submission. The provision unit provides specific procedures for carrying out the procedures proposed by the proposal unit. The provision unit provides specific procedures, such as step-by-step instructions, required documents, and where to submit them. The provision unit can also use the generation AI to provide specific procedures for carrying out the proposed procedures. For example, the provision unit indicates specific instructions for preparing and sending a notice of shareholders' meeting, how to prepare minutes, and so on when holding a general shareholders' meeting. This allows the efficiency improvement system according to the embodiment to efficiently collect, analyze, propose, and provide information.

[0076] The efficiency improvement system includes a reception unit that receives information input by a user. The reception unit receives the information input by a user. The reception unit can receive the information input by a user in the form of, for example, text information, numerical information, image information, or the like. The reception unit can also use a generation AI to efficiently receive the information input by a user. For example, the reception unit uses a generation AI to analyze the information input by a user and extract necessary information. This allows the information input by a user to be efficiently received.

[0077] The suggestion unit can propose customized procedures based on information input by the user. For example, the suggestion unit proposes the procedure for preparing documents required for company establishment, where to submit them, and the deadline for submission, based on the information input by the user. The suggestion unit can also use the generation AI to propose the optimal procedure based on the user's specific situation. For example, the suggestion unit uses the generation AI to propose procedures that meet specific conditions based on the information input by the user. This makes it possible to propose the optimal procedure based on the user's specific situation.

[0078] The provision unit can provide specific steps for carrying out the proposed procedures. For example, when holding a general shareholders' meeting, the provision unit can provide specific instructions such as how to prepare and send a convocation notice and how to prepare minutes. The provision unit can also use the generation AI to provide specific steps for carrying out the proposed procedures. For example, the provision unit can have the generation AI provide step-by-step instructions to enable the user to smoothly carry out the procedures. This allows the proposed procedures to be carried out smoothly.

[0079] The collection department can collect information on provisions of the Companies Act, related laws and regulations, and past cases. For example, the collection department comprehensively collects information such as provisions of the Companies Act, related laws and regulations, and past cases. The collection department can also use generation AI to efficiently collect necessary information. For example, the collection department uses generation AI to collect information such as specific chapters and provisions of the Companies Act, related laws and regulations, precedents, and industry best practices. This allows the collection of necessary information comprehensively.

[0080] The analysis unit can analyze the collected information and collect all the information necessary for each process. The analysis unit analyzes the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also use the generation AI to analyze the collected information and comprehensively collect the information necessary for each process. For example, the analysis unit extracts the information necessary for each process based on the information collected by the generation AI. This makes it possible to comprehensively collect the information necessary for each process.

[0081] The proposal unit can propose procedures for preparing documents required for company establishment, where to submit them, deadlines for submission, etc. The proposal unit can also use the generation AI to propose optimal procedures based on the user's specific situation. For example, the proposal unit can use the generation AI to propose procedures based on specific conditions based on information input by the user. This allows the procedures required for company establishment to be carried out efficiently.

[0082] The provision department can provide specific instructions on how to prepare and send a convocation notice, how to prepare minutes, etc., when holding a general shareholders' meeting. For example, the provision department can provide specific instructions on how to prepare and send a convocation notice, how to prepare minutes, etc., when holding a general shareholders' meeting. The provision department can also use the generation AI to provide specific procedures for carrying out the proposed procedures. For example, the provision department can have the generation AI provide step-by-step instructions, allowing the user to carry out the procedures smoothly. This allows the procedures for the general shareholders' meeting to be carried out efficiently.

[0083] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit causes the generation AI to reduce the frequency of information collection, thereby reducing the user's burden. Furthermore, if the user is relaxed, the collection unit can also cause the generation AI to increase the frequency of information collection and collect more detailed information. Furthermore, if the user is in a hurry, the collection unit can also cause the generation AI to quickly collect information and provide necessary information preferentially. This reduces the user's burden by adjusting the timing of information collection 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.

[0084] When collecting information, the collection unit can analyze the user's past legal compliance history and select the optimal collection method. For example, the collection unit uses a generation AI to select the optimal collection method based on the legal compliance methods used by the user in the past. The collection unit can also prioritize the collection of information on specific laws and regulations from the user's past legal compliance history. Furthermore, the collection unit can analyze the user's past legal compliance history and suggest efficient collection methods. This enables efficient information collection by selecting the optimal collection method based on the user's past legal compliance history.

[0085] The collection unit can perform filtering based on the user's current work situation and areas of interest when collecting information. For example, the collection unit takes into consideration the user's current work situation and prioritizes collecting relevant legal information. The collection unit can also filter and collect specific legal information based on the user's areas of interest. Furthermore, the collection unit can combine the user's work situation and areas of interest to collect optimal legal information. This makes it possible to efficiently collect information according to the user's work situation and areas of interest.

[0086] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, when the user uses voice input, the collection unit has the generation AI collect information using voice recognition technology. In addition, when the user uses text input, the collection unit can also have the generation AI collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also have the generation AI collect information using image recognition technology. This enables efficient information collection by selecting the optimal collection means depending on the user's input method.

[0087] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit causes the generation AI to prioritize collecting important information and omit unnecessary information. Furthermore, when the user is relaxed, the collection unit can cause the generation AI to collect detailed information and provide comprehensive information. Furthermore, when the user is in a hurry, the collection unit can cause the generation AI to quickly collect necessary information and prioritize it. Thus, by determining the priority of information according to the user's emotions, important information can be collected preferentially. 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 collecting data, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting legal information related to that area. The collection unit can also collect area-specific legal information based on the user's geographical location information. Furthermore, the collection unit can combine the user's current location with related legal information to collect optimal information. This makes it possible to prioritize collecting highly relevant information based on the user's geographical location information.

[0089] At the time of collection, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the content posted by the user on social media and collect related legal information. The collection unit can also collect legal information of interest based on the user's social media activity history. Furthermore, the collection unit can also collect related legal information by referring to the activities of the user's friends on social media. This makes it possible to efficiently collect related information based on the user's social media activities.

[0090] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit adjusts the collection method using the generation AI based on feedback provided by the user in the past. The collection unit can also prioritize the use of specific information collection methods based on the user's past feedback. Furthermore, the collection unit can also optimize the collection method by reflecting the user's feedback and collect information efficiently. This enables efficient information collection by customizing the collection method by reflecting the user's past feedback.

[0091] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the generation AI can provide an analysis result that focuses on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit allows the generation AI to perform a detailed analysis of important information. The analysis unit can also allow the generation AI to perform a simplified analysis of less important information. Furthermore, the analysis unit allows the generation AI to dynamically adjust the level of detail of the analysis based on the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information.

[0093] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, in the analysis unit, the generation AI applies a specific analysis algorithm to legal information. In addition, in the analysis unit, the generation AI can apply a different analysis algorithm to past case information. Furthermore, in the analysis unit, the generation AI can select the optimal analysis algorithm depending on the category of information. This enables efficient analysis by applying the optimal analysis algorithm depending on the category of information.

[0094] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit allows the generation AI to improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also refer to the user's past analysis results and allow the generation AI to adjust the analysis algorithm. Furthermore, the analysis unit can analyze the user's past analysis results and allow the generation AI to select the optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0095] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can cause the generation AI to provide a short, to-the-point analysis result. Furthermore, if the user is relaxed, the analysis unit can cause the generation AI to provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can cause the generation AI to provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit allows the generation AI to prioritize analysis of information with high urgency. The analysis unit can also allow the generation AI to prioritize analysis of information with an approaching submission deadline. Furthermore, the analysis unit can also allow the generation AI to dynamically adjust the priority of analysis based on the time of submission of information. In this way, by determining the priority of analysis based on the time of submission of information, it is possible to prioritize analysis of information with high urgency.

[0097] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit allows the generation AI to prioritize analysis of highly relevant information. The analysis unit can also allow the generation AI to postpone analysis of less relevant information. Furthermore, the analysis unit can also allow the generation AI to dynamically adjust the order of analysis based on the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, highly relevant information can be analyzed preferentially.

[0098] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can have the generation AI provide analysis results that use a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit can have the generation AI provide analysis results in simple language. Furthermore, the analysis unit can also have the generation AI dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This makes it possible to provide analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0099] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple and highly visible suggestion. Furthermore, if the user is relaxed, the suggestion unit can also provide a detailed suggestion. Furthermore, if the user is in a hurry, the suggestion unit can also provide a suggestion that focuses on the main points. In this way, by adjusting the way the suggestions are expressed according to the user's emotions, it is possible to provide a suggestion that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the procedure. For example, the suggestion unit allows the generation AI to make detailed proposals for important procedures. The suggestion unit can also allow the generation AI to make simplified proposals for procedures with low importance. Furthermore, the suggestion unit allows the generation AI to dynamically adjust the level of detail of the proposal based on the importance of the procedure. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the procedure.

[0101] When making a proposal, the proposal department can apply different proposal algorithms depending on the procedure category. For example, the proposal department's generation AI applies a specific proposal algorithm to company establishment procedures. The proposal department can also apply a different proposal algorithm to stock handling procedures. Furthermore, the proposal department can also select the optimal proposal algorithm for the generation AI depending on the procedure category. This enables efficient proposals by applying the optimal proposal algorithm depending on the procedure category.

[0102] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit allows the generation AI to improve the accuracy of the proposal based on the user's past proposal results. The suggestion unit can also refer to the user's past proposal results and allow the generation AI to adjust the proposal algorithm. Furthermore, the suggestion unit can analyze the user's past proposal results and allow the generation AI to select the optimal proposal method. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results.

[0103] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can cause the generation AI to provide a short, to-the-point suggestion. Furthermore, if the user is relaxed, the suggestion unit can cause the generation AI to provide a detailed suggestion. Furthermore, if the user is excited, the suggestion unit can cause the generation AI to provide a visually stimulating suggestion. In this way, by adjusting the length of the suggestion according to the user's emotions, it is possible to provide a suggestion of an appropriate length for the user. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0104] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the procedure. For example, the suggestion unit allows the generation AI to give priority to proposals for procedures with a high level of urgency. The suggestion unit can also allow the generation AI to give priority to proposals for procedures with an approaching submission deadline. Furthermore, the suggestion unit can also allow the generation AI to dynamically adjust the priority of the proposal based on the time of submission of the procedure. In this way, by determining the priority of the proposal based on the time of submission of the procedure, it is possible to give priority to proposals for procedures with a high level of urgency.

[0105] The suggestion unit can adjust the order of suggestions based on the relevance of the procedures when making suggestions. For example, the suggestion unit can have the generation AI give priority to suggestions for highly relevant procedures. The suggestion unit can also have the generation AI put off proposing less relevant procedures. Furthermore, the suggestion unit can also have the generation AI dynamically adjust the order of suggestions based on the relevance of the procedures. In this way, by adjusting the order of suggestions based on the relevance of the procedures, highly relevant procedures can be suggested preferentially.

[0106] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can cause the generation AI to provide a proposal that uses a lot of technical terminology. Also, if the user does not have technical expertise, the suggestion unit can cause the generation AI to provide a proposal in simple language. Furthermore, the suggestion unit can also cause the generation AI to dynamically adjust the use of technical terminology in the proposal according to the user's level of expertise. This makes it possible to provide a proposal that is easy for the user to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise.

[0107] The providing unit can estimate the user's emotions and adjust the way in which the steps are presented based on the estimated user emotions. For example, if the user is nervous, the providing unit can have the generation AI provide simple, highly visible steps. Furthermore, if the user is relaxed, the providing unit can have the generation AI provide detailed steps. Furthermore, if the user is in a hurry, the providing unit can have the generation AI provide steps that focus on the main points. In this way, by adjusting the way in which the steps are presented according to the user's emotions, it is possible to provide steps that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0108] The providing unit can adjust the level of detail provided based on the importance of the procedure when providing the procedure. For example, the providing unit has the generating AI provide detailed procedures for important procedures. The providing unit can also have the generating AI provide simplified procedures for procedures with low importance. Furthermore, the providing unit can also have the generating AI dynamically adjust the level of detail provided according to the importance of the procedure. This allows for efficient procedure provision by adjusting the level of detail provided according to the importance of the procedure.

[0109] The provision unit can apply different provision algorithms depending on the procedure category when providing the procedures. For example, the generation AI in the provision unit applies a specific provision algorithm to company establishment procedures. The provision unit can also apply a different provision algorithm to stock handling procedures. Furthermore, the generation AI in the provision unit can select the optimal provision algorithm depending on the procedure category. This enables efficient procedure provision by applying the optimal provision algorithm depending on the procedure category.

[0110] The providing unit can improve the accuracy of provision by referring to the user's past provision results when providing the data. For example, the providing unit allows the generation AI to improve the accuracy of provision based on the user's past provision results. The providing unit can also refer to the user's past provision results and allow the generation AI to adjust the provision algorithm. Furthermore, the providing unit can analyze the user's past provision results and allow the generation AI to select the optimal provision method. In this way, the accuracy of provision can be improved by referring to the user's past provision results.

[0111] The providing unit can estimate the user's emotions and adjust the length of the instructions to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can have the generation AI provide short, concise instructions. Furthermore, if the user is relaxed, the providing unit can have the generation AI provide detailed instructions. Furthermore, if the user is excited, the providing unit can have the generation AI provide visually stimulating instructions. In this way, by adjusting the length of the instructions to be provided according to the user's emotions, instructions of an appropriate length for the user can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0112] The provision unit can determine the priority of provision based on the time of submission of the procedure at the time of provision. For example, the provision unit allows the generation AI to provide procedures preferentially for procedures with high urgency. The provision unit can also allow the generation AI to provide procedures preferentially for procedures with an approaching submission deadline. Furthermore, the provision unit can allow the generation AI to dynamically adjust the priority of provision based on the time of submission of the procedure. In this way, by determining the priority of provision based on the time of submission of the procedure, it is possible to provide procedures with high urgency preferentially.

[0113] The providing unit can adjust the order of provision based on the relevance of the procedures when providing them. For example, the providing unit can have the generation AI provide procedures with priority for highly relevant procedures. The providing unit can also have the generation AI provide procedures with lower relevance at a later date. Furthermore, the providing unit can also have the generation AI dynamically adjust the order of provision based on the relevance of the procedures. In this way, by adjusting the order of provision based on the relevance of the procedures, highly relevant procedures can be provided with priority.

[0114] The providing unit can adjust the use of technical terminology in the provided instructions according to the user's level of expertise when providing the instructions. For example, if the user has technical expertise, the providing unit can have the generation AI provide instructions that use a lot of technical terminology. Also, if the user does not have technical expertise, the providing unit can have the generation AI provide instructions in simple language. Furthermore, the providing unit can also have the generation AI dynamically adjust the use of technical terminology in the provided instructions according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the provided instructions according to the user's level of expertise, it is possible to provide instructions that are easy for the user to understand.

[0115] The reception unit can estimate the user's emotions and adjust the reception expression method based on the estimated user emotions. For example, if the user is nervous, the generation AI of the reception unit can provide a simple and highly visible reception method. Furthermore, if the user is relaxed, the reception unit can also provide a detailed reception method. Furthermore, if the user is in a hurry, the generation AI can also provide a reception method that focuses on the main points. In this way, by adjusting the reception expression method according to the user's emotions, it is possible to provide a reception method that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0116] The reception unit can select the optimal reception method by referring to the user's past input history when receiving a call. For example, the reception unit uses a generation AI to select the optimal reception method based on the user's past input history. The reception unit can also preferentially suggest a specific input method based on the user's past input history. Furthermore, the reception unit can analyze the user's past input history and suggest an efficient reception method. This allows for efficient reception by selecting the optimal reception method based on the user's past input history.

[0117] The reception unit can customize the reception content based on the user's current work situation when receiving a call. For example, the reception unit takes into account the user's current work situation and provides the relevant reception content through the generation AI. The reception unit can also have the generation AI propose the optimal reception method based on the user's work situation. Furthermore, the reception unit can analyze the user's work situation and provide the customized reception content through the generation AI. This enables efficient reception by providing reception content that suits the user's current work situation.

[0118] The reception unit can estimate the user's emotions and determine the priority of reception requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit allows the generation AI to prioritize important reception requests. Furthermore, if the user is relaxed, the reception unit can also allow the generation AI to provide detailed reception requests. Furthermore, if the user is in a hurry, the reception unit can allow the generation AI to quickly process reception requests. This allows important reception requests to be prioritized by determining reception priorities 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0119] The reception unit can select the optimal reception method by taking into consideration the user's geographical location information when receiving a call. For example, if the user is in a specific area, the reception unit provides reception details related to that area. The reception unit can also suggest a reception method specific to the area based on the user's geographical location information. Furthermore, the reception unit can combine reception details related to the user's current location to provide the optimal reception method. This enables efficient reception by selecting the optimal reception method based on the user's geographical location information.

[0120] The reception unit can analyze the user's social media activity at the time of reception and suggest related reception contents. The reception unit can, for example, analyze the content posted by the user on social media and suggest related reception contents. The reception unit can also provide reception contents of interest based on the user's social media activity history. Furthermore, the reception unit can also suggest related reception contents by referring to the activities of the user's friends on social media. This enables efficient reception by suggesting related reception contents based on the user's social media activity.

[0121] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a call. For example, the reception unit adjusts the reception method using a generation AI based on feedback provided by the user in the past. The reception unit can also prioritize the use of a specific reception method based on the user's past feedback. Furthermore, the reception unit can also optimize the reception method by reflecting the user's feedback and perform efficient reception. As a result, efficient reception is possible by customizing the reception method by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by either the smart device 14 or the data processing device 12. For example, the collection unit collects information using the camera 42 or microphone 38B of the smart device 14 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal procedure based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides specific steps for executing the suggested procedure. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and provision 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 collection unit is realized by either the smart glasses 214 or the data processing device 12. For example, the collection unit collects information using the camera 42 or microphone 238 of the smart glasses 214 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal procedure based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides specific steps for executing the suggested procedure. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by either the headset type terminal 314 or the data processing device 12. For example, the collection unit collects information using the camera 42 or microphone 238 of the headset type terminal 314 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal procedure based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides specific steps for executing the suggested procedure. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by either the robot 414 or the data processing device 12. For example, the collection unit collects information using the camera 42 or microphone 238 of the robot 414 and transmits the collected information to the data processing device 12 by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal procedure based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides specific steps for executing the proposed procedure.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI will prioritize analyzing important information and postpone unnecessary information. Also, if the user is relaxed, the generation AI can analyze detailed information and provide comprehensive analysis results. Furthermore, if the user is in a hurry, the generation AI can quickly analyze necessary information and raise its priority. In this way, by determining the analysis priority according to the user's emotions, important information can be analyzed preferentially.

[0124] The suggestion unit can estimate the user's emotions and adjust the level of detail in the suggestions based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide simple, highly visible suggestions. If the user is relaxed, the generation AI can also provide detailed suggestions. Furthermore, if the user is in a hurry, the generation AI can also provide suggestions that focus on the main points. In this way, by adjusting the level of detail in the suggestions according to the user's emotions, it is possible to provide suggestions that are easy for the user to understand.

[0125] The provision unit can estimate the user's emotions and determine the priority of the steps to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can provide important steps with priority and postpone unnecessary steps. Also, if the user is relaxed, the generation AI can provide detailed steps and show comprehensive steps. Furthermore, if the user is in a hurry, the generation AI can quickly provide necessary steps and increase their priority. In this way, by determining the priority of the steps to be provided according to the user's emotions, important steps can be provided with priority.

[0126] The reception unit can estimate the user's emotions and adjust the level of detail in the reception based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple and highly visible reception method. If the user is relaxed, the generation AI can also provide a detailed reception method. Furthermore, if the user is in a hurry, the generation AI can also provide a reception method that focuses on the main points. In this way, by adjusting the level of detail in the reception according to the user's emotions, it is possible to provide a reception method that is easy for the user to understand.

[0127] The collection unit can estimate the user's emotions and adjust the level of detail of the information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI will prioritize collecting important information and omit unnecessary information. Also, if the user is relaxed, the generation AI can collect detailed information and provide comprehensive information. Furthermore, if the user is in a hurry, the generation AI can quickly collect necessary information and prioritize it. In this way, by adjusting the level of detail of the information to be collected according to the user's emotions, important information can be collected preferentially.

[0128] During analysis, the analysis unit can determine the priority of analysis based on the reliability of the information. For example, the generation AI can prioritize analysis of highly reliable information. Also, the generation AI can postpone analysis of less reliable information. Furthermore, the analysis unit can allow the generation AI to dynamically adjust the priority of analysis based on the reliability of the information. This allows highly reliable information to be analyzed preferentially by determining the priority of analysis based on the reliability of the information.

[0129] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion history. For example, the generation AI makes the optimal suggestion based on suggestions the user has received in the past. The suggestion unit can also prioritize the use of a specific suggestion method based on the user's past suggestion history. Furthermore, the suggestion unit can analyze the user's past suggestion history and suggest an efficient suggestion method. This enables efficient suggestions by improving the accuracy of the suggestion based on the user's past suggestion history.

[0130] The provision unit can customize the content of the provision based on the user's current work situation when providing the procedure. For example, the generation AI provides a relevant procedure taking into account the user's current work situation. The provision unit can also have the generation AI propose an optimal procedure based on the user's work situation. Furthermore, the provision unit can analyze the user's work situation and have the generation AI provide a customized procedure. This enables efficient procedure provision by providing a procedure that suits the user's current work situation.

[0131] The reception unit can select the optimal reception method by taking into account the user's geographical location information when receiving a call. For example, if the user is in a specific area, reception details related to that area are provided. The reception unit can also suggest a reception method specific to the area based on the user's geographical location information. Furthermore, the reception unit can combine reception details related to the user's current location to provide the optimal reception method. This allows for efficient reception by selecting the optimal reception method based on the user's geographical location information.

[0132] During collection, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the content posted by the user on social media and collect related legal information. The collection unit can also collect legal information of interest based on the user's social media activity history. Furthermore, the collection unit can also collect related legal information by referring to the activities of the user's friends on social media. This makes it possible to efficiently collect related information based on the user's social media activities.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The collection department collects information. For example, the collection department collects information such as provisions of the Companies Act, related laws and regulations, and past cases. The collection department can also use the generation AI to comprehensively collect information on each process, from creating articles of incorporation to investment, company establishment, stock handling, and holding general shareholders' meetings. For example, the collection department uses the generation AI to collect information such as the company's purpose, trade name, head office location, and amount of investment at the time of establishment. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also use the generation AI to analyze the collected information and comprehensively collect the information necessary for each process. For example, the analysis unit extracts the information necessary for each process based on the information collected by the generation AI. Step 3: The proposal unit proposes optimal procedures based on the analysis results obtained by the analysis unit. The proposal unit proposes appropriate procedures, such as legal procedures, business procedures, and technical procedures. The proposal unit can also use the generation AI to propose customized procedures based on information entered by the user. For example, the proposal unit uses the generation AI to propose procedures for preparing documents required for company establishment, as well as where and when to submit them and the deadline for submission. Step 4: The provision department provides specific procedures for carrying out the procedures proposed by the proposal department. The provision department provides specific procedures, such as step-by-step instructions, required documents, and where to submit them. The provision department can also use the generation AI to provide specific procedures for carrying out the proposed procedures. For example, the provision department may have the generation AI provide specific instructions on how to prepare and send a convocation notice, how to prepare minutes, etc., when holding a general shareholders' meeting.

[0135] 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.

[0136] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[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 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.

[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 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.

[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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

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

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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 collection unit that collects information; an analysis unit that analyzes the information collected by the collection unit; a proposal unit that proposes an appropriate procedure based on the analysis result obtained by the analysis unit; a providing unit that provides a specific procedure for executing the procedure proposed by the proposing unit. A system characterized by:

2. A reception unit is provided to receive information input by the user.

2. The system of claim 1.

3. The proposal unit Suggesting customized procedures based on user-entered information 2. The system of claim 1.

4. The providing unit Provide specific steps for carrying out the proposed procedure 2. The system of claim 1.

5. The collecting unit Collect information on the provisions of the Companies Act, related laws and regulations, and past cases 2. The system of claim 1.

6. The analysis unit Analyze the collected information and collect all the information necessary for each process 2. The system of claim 1.

7. The proposal unit Propose procedures for preparing documents required for company establishment, where to submit them, deadlines for submission, etc.

2. The system of claim 1.

8. The providing unit When holding a general shareholders' meeting, we will provide specific instructions on how to prepare and send the convocation notice, as well as how to prepare minutes.

2. The system of claim 1.

9. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

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