System

Abstract

An object of a system according to an embodiment is to automatically identify a decider or a consenter based on the content of a circular memo.SOLUTION: A system includes a reception part, an analysis part, a specification part, and an agreement part. The reception part inputs a circular memo application content. The analysis unit analyzes the request for decision making contents input by the acceptance unit. The specification part specifies a decider on the basis of the circular memo application contents analyzed by the analysis part. The agreement unit identifies the agreeing party based on the decider identified by the identification 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 required the decision maker and approver to be manually identified when submitting a request for approval, which was inefficient.

[0005] The system according to the embodiment aims to automatically identify the approver and approvers based on the contents of the approval request form. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an identification unit, and a consent unit. The reception unit inputs the contents of the approval request form. The analysis unit analyzes the contents of the approval request form input by the reception unit. The identification unit identifies an authorizer based on the contents of the approval request form analyzed by the analysis unit. The consent unit identifies a consenter based on the authorizer identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically identify the approver and approvers based on the contents of the approval request form. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A request form issuance system according to an embodiment of the present invention automatically analyzes the contents of a request form and automatically assigns the approver and approvers based on the request form regulations. The request form issuance system improves the efficiency of request form issuance by having a user input the request form contents, and an AI analyzes the contents to identify the appropriate approver and approver. For example, in the request form issuance system, a user inputs detailed information such as the request form content, purpose, and amount. The request form issuance system then analyzes the input request form contents using AI and identifies the appropriate approver and approver in accordance with the request form regulations. This eliminates the need for manual approval setting and allows the request form approval process to proceed smoothly. This improves the efficiency of request form issuance and eliminates the need for manual approval setting. For example, when a user submits a budget request for a new project, the approval system will set the appropriate approver based on the amount and set the person in charge of the relevant department as the approver, which will expedite the approval process and ensure a smooth start to the project.

[0029] The approval request form issuance system according to the embodiment includes a reception unit, an analysis unit, an identification unit, and an agreement unit. A user inputs the approval request form content into the reception unit. The approval request form content input by the user includes, but is not limited to, detailed information such as the approval request content, purpose, and amount. For example, the reception unit allows the user to input a budget request for a new project or an equipment purchase request. The analysis unit uses AI to analyze the approval request form content input by the reception unit. The analysis unit analyzes the approval request form content using, for example, text analysis technology and extracts information such as the approval request content, purpose, and amount. The analysis unit can also analyze the approval request form content using natural language processing technology. The identification unit identifies an approver based on the approval request form content analyzed by the analysis unit. For example, the identification unit identifies an approver based on the amount based on approval request regulations. The identification unit can also identify an appropriate approver based on the approval request content and purpose. The consent unit identifies the approver based on the approver identified by the identification unit. For example, if approval from a specific department is required based on the approval regulations, the consent unit sets the person in charge of that department as the approver. The consent unit can also identify an appropriate approver depending on the content and purpose of the approval. This allows the approval request form issuance system according to the embodiment to improve the efficiency of approval request form issuance and eliminate the need for manual approval request form setting. For example, when a user submits a budget request for a new project, the approval request form issuance system sets an appropriate approver according to the amount and sets the person in charge of the relevant department as the approver. This allows the approval process for the approval request to proceed quickly and the project to start smoothly.

[0030] The approval document issuing system is equipped with an update unit to respond to changes to approval document regulations. The update unit provides functions for responding to changes to approval document regulations. For example, the update unit automates the procedure for changing approval document regulations and reflects the latest approval document regulations in the system. The update unit also manages the change history of approval document regulations and allows past changes to be referenced. Furthermore, the update unit is equipped with a notification function for changes to approval document regulations, and can notify relevant parties of the changes. This allows the approval document issuing system to respond to changes to approval document regulations.

[0031] The approval request system is equipped with an exception processing unit that performs exception processing under specific conditions. The exception processing unit provides the function of performing exception processing under specific conditions. For example, the exception processing unit identifies conditions that require exception processing based on approval request regulations and performs processing according to those conditions. The exception processing unit can also provide an interface that allows users to manually perform exception processing. Furthermore, the exception processing unit manages the history of exception processing and makes it possible to refer to the contents of past exception processing. This enables the approval request system to perform exception processing under specific conditions.

[0032] The approval request form system includes a correction unit that allows users to manually correct the approval request form content. The correction unit provides a function for users to manually correct the approval request form content. For example, the correction unit provides an interface for users to edit the approval request form content. The correction unit also manages the history of correction content and allows users to refer to past correction content. Furthermore, the correction unit can automatically reanalyze the content based on the correction content and reset the appropriate approver and approver. This allows users to manually correct the approval request form system.

[0033] The approval request issuing system is equipped with a security unit that protects the data of approval request contents and controls access. The security unit provides functions for protecting the data of approval request contents and controlling access. For example, the security unit encrypts and stores the contents of approval request to protect them from unauthorized access. The security unit also performs user authentication and authority management to achieve access control. Furthermore, the security unit manages access logs and can track the access history to the approval request contents. This enables the approval request issuing system to protect the data of approval request contents and control access.

[0034] The approval request issuance system is equipped with a natural language processing unit that understands the context of the approval request content and improves the accuracy of analysis. The natural language processing unit provides functions for understanding the context of the approval request content and improving the accuracy of analysis. For example, the natural language processing unit uses natural language processing technology to analyze the approval request content and understand the context. The natural language processing unit can also use a context analysis algorithm to accurately grasp the meaning of the approval request content. Furthermore, the natural language processing unit can identify the appropriate approver and consenter based on the analysis results. This allows the approval request issuance system to understand the context of the approval request content and improve the accuracy of analysis.

[0035] The reception unit can analyze the user's past history of submitting approval requests and suggest the optimal input method. For example, the reception unit can automatically display as candidates the approval request contents that the user has frequently entered in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the approval request contents to be used in a specific time period based on the user's past history of submitting approval requests. This makes it possible to suggest the optimal input method based on the user's past history.

[0036] The reception unit can customize input fields based on the user's current work situation and areas of interest when entering the details of the approval request form. For example, the reception unit allows the user to preferentially enter approval details related to a currently ongoing project. The reception unit can also automatically display relevant input fields based on the user's areas of interest. Furthermore, the reception unit can dynamically adjust the required input fields depending on the user's work situation. This makes it possible to customize input fields according to the user's work situation and areas of interest.

[0037] The reception unit can select the optimal input means depending on the user's input method when entering the contents of the approval request form. For example, when the user inputs the approval request content by voice, the reception unit converts it into text using voice recognition technology. In addition, when the user inputs the approval request content using an image, the reception unit can also analyze the content using image recognition technology. Furthermore, when the user inputs the approval request content using text, the reception unit can also provide an input completion function to enable efficient input. This makes it possible to select the optimal input means depending on the user's input method.

[0038] When inputting the contents of the approval request form, the reception unit can prioritize the input of highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize the display of approval request contents related to that area. The reception unit can also automatically input information about related departments and personnel based on the user's current location. Furthermore, the reception unit can automatically display related regulations and rules based on the user's geographical location information. This makes it possible to prioritize the input of highly relevant information based on the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and input related information when entering the details of the approval request form. For example, the reception unit can automatically input related approval request details based on information shared by the user on social media. The reception unit can also analyze the user's social media activity and automatically input information about related departments and personnel. Furthermore, the reception unit can automatically display related regulations and rules based on the content posted by the user on social media. This makes it possible to input related information based on the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting the contents of the approval request form. For example, the reception unit can suggest the optimal input method based on the user's past feedback. The reception unit can also improve the input interface by reflecting the user's past feedback. Furthermore, the reception unit can also optimize the input procedure based on the user's past feedback. This makes it possible to customize the input method based on the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the approval request content. For example, the analysis unit performs a detailed analysis on approval request content with a high level of importance. The analysis unit can also perform a simplified analysis on approval request content with a low level of importance. Furthermore, the analysis unit can dynamically adjust the depth and scope of the analysis according to the importance. This makes it possible to adjust the level of detail of the analysis according to the importance of the approval request content.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the approval request content. For example, the analysis unit applies a financial data analysis algorithm to a budget request. The analysis unit can also apply an equipment evaluation algorithm to an equipment purchase request. Furthermore, the analysis unit can also apply a project evaluation algorithm to a project proposal. This makes it possible to apply analysis algorithms depending on the category of the approval request content.

[0043] 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 improves the current analysis accuracy based on the analysis results performed by the user in the past. The analysis unit can also refer to the user's past analysis results to perform optimal analysis for similar approval requests. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. This makes it possible to improve the analysis accuracy based on the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the approval request content. For example, the analysis unit can prioritize analysis of urgent approval request content. The analysis unit can also prioritize analysis of approval request content with an approaching submission deadline. Furthermore, the analysis unit can dynamically adjust the priority of analysis depending on the submission time. This makes it possible to determine the priority of analysis depending on the submission time of the approval request content.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the approval request contents. For example, the analysis unit prioritizes analysis of highly relevant approval request contents. The analysis unit can also postpone analysis of less relevant approval request contents. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the approval request contents. This makes it possible to adjust the order of analysis based on the relevance of the approval request contents.

[0046] 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 provide the analysis results using detailed technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple terms. Furthermore, the analysis unit can dynamically adjust the way in which the analysis results are presented according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0047] When identifying an authorizer, the identification unit can adjust the level of detail of the identification based on the importance of the approval request content. For example, the identification unit performs detailed authorizer identification for approval request content with high importance. The identification unit can also perform simplified authorizer identification for approval request content with low importance. Furthermore, the identification unit can dynamically adjust the depth and scope of authorizer identification according to the importance. This makes it possible to adjust the level of detail of authorizer identification according to the importance of the approval request content.

[0048] When identifying the decision maker, the identification unit can apply different identification algorithms depending on the category of the approval request content. For example, the identification unit can apply a financial data analysis algorithm to a budget request. The identification unit can also apply an equipment evaluation algorithm to an equipment purchase request. Furthermore, the identification unit can also apply a project evaluation algorithm to a project proposal. This makes it possible to apply an algorithm for identifying the decision maker depending on the category of the approval request content.

[0049] When identifying an authorizer, the identification unit can improve the accuracy of the identification by referring to the user's past identification results. The identification unit improves the current identification accuracy, for example, based on the user's past authorizer identification results. The identification unit can also refer to the user's past identification results to identify the most suitable authorizer for similar approval requests. Furthermore, the identification unit can analyze the user's past identification results and optimize the identification algorithm. This makes it possible to improve the accuracy of identifying an authorizer based on the user's past identification results.

[0050] When identifying an authorizer, the identification unit can determine the priority of the identification based on the submission time of the approval request content. For example, the identification unit can prioritize the identification of an authorizer for urgent approval requests. The identification unit can also prioritize the identification of an authorizer for approval requests with an approaching submission deadline. Furthermore, the identification unit can dynamically adjust the priority of the identification of an authorizer depending on the submission time. This makes it possible to determine the priority of the identification of an authorizer depending on the submission time of the approval request content.

[0051] When identifying authorizers, the identification unit can adjust the identification order based on the relevance of the approval request content. For example, the identification unit can prioritize identifying authorizers for highly relevant approval request content. The identification unit can also postpone identifying authorizers for less relevant approval request content. Furthermore, the identification unit can dynamically adjust the order of authorizer identification according to the relevance of the approval request content. This makes it possible to adjust the order of authorizer identification according to the relevance of the approval request content.

[0052] When identifying an authorizer, the identification unit can adjust the use of specific technical terms depending on the user's level of expertise. For example, if the user has specialized knowledge, the identification unit can identify the authorizer using detailed technical terms. Alternatively, if the user does not have specialized knowledge, the identification unit can identify the authorizer using simple language. Furthermore, the identification unit can dynamically adjust the way in which the authorizer is identified depending on the user's level of expertise. This makes it possible to adjust the use of specific technical terms for the authorizer depending on the user's level of expertise.

[0053] When identifying consenting parties, the consent unit can adjust the level of detail of identification based on the importance of the approval request content. For example, the consent unit performs detailed consenting party identification for approval request content with a high level of importance. The consent unit can also perform simplified consenting party identification for approval request content with a low level of importance. Furthermore, the consent unit can dynamically adjust the depth and scope of consenting party identification according to the importance. This makes it possible to adjust the level of detail of consenting party identification according to the importance of the approval request content.

[0054] When identifying approvers, the consent unit can apply different identification algorithms depending on the category of the approval request content. For example, the consent unit can apply a financial data analysis algorithm to a budget request. The consent unit can also apply an equipment evaluation algorithm to an equipment purchase request. Furthermore, the consent unit can also apply a project evaluation algorithm to a project proposal. This makes it possible to apply an algorithm for identifying approvers depending on the category of the approval request content.

[0055] When identifying consenting parties, the consent unit can improve the accuracy of identification by referring to the user's past identification results. For example, the consent unit improves the current identification accuracy based on consenting party identification results performed by the user in the past. The consent unit can also refer to the user's past identification results to identify the most appropriate consenting party for similar approval requests. Furthermore, the consent unit can analyze the user's past identification results and optimize the identification algorithm. This makes it possible to improve the accuracy of identifying consenting parties based on the user's past identification results.

[0056] When identifying consenting parties, the consenting unit can determine a specific priority order based on the submission time of the approval request content. For example, the consenting unit can prioritize identifying consenting parties for urgent approval requests. The consenting unit can also prioritize identifying consenting parties for approval requests whose submission deadline is approaching. Furthermore, the consenting unit can dynamically adjust the priority order for identifying consenting parties according to the submission time. This makes it possible to determine the priority order for identifying consenting parties according to the submission time of the approval request content.

[0057] When identifying consenting parties, the consenting unit can adjust the identification order based on the relevance of the approval request content. For example, the consenting unit can prioritize identifying consenting parties for highly relevant approval request content. The consenting unit can also postpone identifying consenting parties for less relevant approval request content. Furthermore, the consenting unit can dynamically adjust the identification order of consenting parties according to the relevance of the approval request content. This makes it possible to adjust the identification order of consenting parties according to the relevance of the approval request content.

[0058] When identifying a consenting party, the consenting unit can adjust the use of specific technical terms according to the user's level of expertise. For example, if the user has specialized knowledge, the consenting unit can identify the consenting party using detailed technical terms. Alternatively, if the user does not have specialized knowledge, the consenting unit can also identify the consenting party using simple language. Furthermore, the consenting unit can dynamically adjust the way in which the consenting party is identified according to the user's level of expertise. This makes it possible to adjust the use of specific technical terms for the consenting party according to the user's level of expertise.

[0059] When updating approval regulations, the update unit can refer to past update history to select the optimal update method. For example, the update unit can propose the optimal update procedure based on the past update history. The update unit can also refer to the past update history to select the optimal update method for similar approval regulations. Furthermore, the update unit can analyze the past update history and optimize the update procedure. This makes it possible to select the optimal update method based on the past update history.

[0060] The update unit can improve the updated content by reflecting user feedback when updating the approval request rules. For example, the update unit improves the updated content of the approval request rules based on feedback provided by the user. The update unit can also optimize the update procedure by reflecting user feedback. Furthermore, the update unit can dynamically adjust the updated content based on user feedback. This makes it possible to improve the updated content of the approval request rules based on user feedback.

[0061] When updating the approval regulations, the update unit can weight the update contents based on the submission date of the approval contents. For example, the update unit prioritizes updating approval contents whose submission date is approaching. The update unit can also postpone updating approval contents whose submission date is further away. Furthermore, the update unit can dynamically adjust the weighting of the update contents depending on the submission date. This makes it possible to weight the update contents based on the submission date of the approval contents.

[0062] When updating the approval regulations, the update unit can integrate information from different data sources to enhance the updated content. For example, the update unit integrates information from different data sources to enhance the updated content of the approval regulations. The update unit can also refer to information from different data sources and select the optimal updated content. Furthermore, the update unit can dynamically adjust the updated content based on information from different data sources. This makes it possible to enhance the updated content by integrating information from different data sources.

[0063] When handling an exception, the exception processing unit can select the optimal processing method by referring to the past exception processing history. For example, the exception processing unit proposes the optimal processing procedure based on the past exception processing history. The exception processing unit can also select the optimal processing method for similar approval requests by referring to the past exception processing history. Furthermore, the exception processing unit can analyze the past exception processing history and optimize the processing procedure. This makes it possible to select the optimal processing method based on the past exception processing history.

[0064] The exception processing unit can improve the processing method by reflecting user feedback during exception processing. For example, the exception processing unit improves the exception processing procedure based on feedback provided by the user. The exception processing unit can also optimize the processing procedure by reflecting user feedback. Furthermore, the exception processing unit can dynamically adjust the processing method based on user feedback. This makes it possible to improve the exception processing method based on user feedback.

[0065] During exception processing, the exception processing unit can weight the processing content based on the submission time of the approval request content. For example, the exception processing unit prioritizes exception processing for approval request content whose submission time is approaching. The exception processing unit can also postpone exception processing for approval request content whose submission time is further away. Furthermore, the exception processing unit can dynamically adjust the weighting of the exception processing depending on the submission time. This makes it possible to weight the processing content based on the submission time of the approval request content.

[0066] The exception processing unit can integrate information from different data sources during exception processing to enhance the processing content. For example, the exception processing unit integrates information from different data sources to enhance the exception processing content. The exception processing unit can also refer to information from different data sources to select the optimal processing content. Furthermore, the exception processing unit can dynamically adjust the processing content based on information from different data sources. This makes it possible to enhance the processing content by integrating information from different data sources.

[0067] When making corrections, the correction department can refer to past correction history to select the optimal correction method. For example, the correction department can propose the optimal correction procedure based on the past correction history. The correction department can also refer to past correction history to select the optimal correction method for similar approval requests. Furthermore, the correction department can analyze past correction history and optimize the correction procedure. This makes it possible to select the optimal correction method based on the past correction history.

[0068] The correction unit can improve the correction content by reflecting user feedback during correction. For example, the correction unit improves the correction content based on feedback provided by the user. The correction unit can also optimize the correction procedure by reflecting user feedback. Furthermore, the correction unit can dynamically adjust the correction content based on user feedback. This makes it possible to improve the correction content based on user feedback.

[0069] When making corrections, the correction department can weight the correction contents based on the submission date of the approval request contents. For example, the correction department prioritizes corrections to approval request contents whose submission date is close. The correction department can also postpone corrections to approval request contents whose submission date is further away. Furthermore, the correction department can dynamically adjust the weighting of the correction contents depending on the submission date. This makes it possible to weight the correction contents based on the submission date of the approval request contents.

[0070] The correction unit can integrate information from different data sources to expand the correction content when making corrections. For example, the correction unit integrates information from different data sources to expand the correction content. The correction unit can also refer to information from different data sources and select optimal correction content. Furthermore, the correction unit can dynamically adjust the correction content based on information from different data sources. This makes it possible to expand the correction content by integrating information from different data sources.

[0071] When implementing security measures, the security department can select the most appropriate measures by referring to past security incidents. For example, the security department can propose the most appropriate countermeasure procedure based on past security incidents. The security department can also select the most appropriate countermeasure for similar approval requests by referring to past security incidents. Furthermore, the security department can analyze past security incidents and optimize the countermeasure procedure. This makes it possible to select the most appropriate countermeasure based on past security incidents.

[0072] The security unit can improve the security countermeasures by reflecting user feedback. For example, the security unit improves the security countermeasures based on feedback provided by the user. The security unit can also optimize the countermeasure procedures by reflecting user feedback. Furthermore, the security unit can dynamically adjust the countermeasures based on user feedback. This makes it possible to improve the security countermeasures based on user feedback.

[0073] When implementing security measures, the security department can weight the measures based on the submission date of the approval request content. For example, the security department can prioritize security measures for approval requests whose submission date is approaching. The security department can also postpone security measures for approval requests whose submission date is further away. Furthermore, the security department can dynamically adjust the weighting of security measures according to the submission date. This makes it possible to weight the measures based on the submission date of the approval request content.

[0074] When implementing security measures, the security department can integrate information from different data sources to enhance the content of the measures. For example, the security department integrates information from different data sources to enhance the content of the security measures. The security department can also refer to information from different data sources to select the optimal content of the measures. Furthermore, the security department can dynamically adjust the content of the measures based on information from different data sources. This makes it possible to enhance the content of the measures by integrating information from different data sources.

[0075] During natural language processing, the natural language processing unit can select the optimal processing method by referring to past processing history. For example, the natural language processing unit proposes the optimal natural language processing procedure based on the past processing history. The natural language processing unit can also select the optimal processing method for similar approval requests by referring to the past processing history. Furthermore, the natural language processing unit can analyze the past processing history and optimize the processing procedure. This makes it possible to select the optimal processing method based on the past processing history.

[0076] The natural language processing unit can improve the processing content by reflecting user feedback during natural language processing. The natural language processing unit improves the natural language processing content based on, for example, feedback provided by the user. The natural language processing unit can also optimize the processing procedure by reflecting user feedback. Furthermore, the natural language processing unit can dynamically adjust the processing content based on user feedback. This makes it possible to improve the natural language processing content based on user feedback.

[0077] During natural language processing, the natural language processing unit can weight the processing content based on the submission time of the approval request content. For example, the natural language processing unit prioritizes natural language processing for approval request content that is due to be submitted soon. The natural language processing unit can also postpone natural language processing for approval request content that is due to be submitted further away. Furthermore, the natural language processing unit can dynamically adjust the weighting of the natural language processing depending on the submission time. This makes it possible to weight the processing content based on the submission time of the approval request content.

[0078] During natural language processing, the natural language processing unit can integrate information from different data sources to enhance the processing content. For example, the natural language processing unit integrates information from different data sources to enhance the natural language processing content. The natural language processing unit can also refer to information from different data sources to select the optimal processing content. Furthermore, the natural language processing unit can dynamically adjust the processing content based on information from different data sources. This makes it possible to enhance the processing content by integrating information from different data sources.

[0079] During natural language processing, the natural language processing unit can adjust the use of technical terminology in the processing according to the user's level of expertise. For example, if the user has technical knowledge, the natural language processing unit performs natural language processing using detailed technical terminology. Also, if the user does not have technical knowledge, the natural language processing unit can perform natural language processing using simple language. Furthermore, the natural language processing unit can dynamically adjust the expression method of the natural language processing according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the processing according to the user's level of expertise.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception unit can analyze the user's past history of submitting approval requests and suggest the optimal input method. For example, it can automatically display as candidates the approval content that the user has frequently entered in the past. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the approval content that will be used in a specific time period based on the user's past history of submitting approval requests. This makes it possible to suggest the optimal input method based on the user's past history.

[0082] When identifying the decision maker, the identification department can apply different identification algorithms depending on the category of the approval request content. For example, a financial data analysis algorithm can be applied to a budget request. The identification department can also apply an equipment evaluation algorithm to an equipment purchase request. Furthermore, the identification department can also apply a project evaluation algorithm to a project proposal. This makes it possible to apply an algorithm for identifying the decision maker depending on the category of the approval request content.

[0083] When updating approval regulations, the update unit can integrate information from different data sources to enhance the updated content. For example, information from different data sources can be integrated to enhance the updated content of the approval regulations. The update unit can also reference information from different data sources to select the optimal updated content. Furthermore, the update unit can dynamically adjust the updated content based on information from different data sources. This makes it possible to enhance the updated content by integrating information from different data sources.

[0084] When making corrections, the correction department can refer to past correction history to select the optimal correction method. For example, the department can propose the optimal correction procedure based on the past correction history. The correction department can also refer to past correction history to select the optimal correction method for similar approval requests. Furthermore, the correction department can analyze past correction history and optimize the correction procedure. This makes it possible to select the optimal correction method based on past correction history.

[0085] The natural language processing unit can improve the processing content by reflecting user feedback during natural language processing. For example, the natural language processing content is improved based on feedback provided by the user. The natural language processing unit can also optimize the processing procedure by reflecting user feedback. Furthermore, the natural language processing unit can dynamically adjust the processing content based on user feedback. This makes it possible to improve the natural language processing content based on user feedback.

[0086] The processing flow of the first embodiment will be briefly explained below.

[0087] Step 1: The user inputs the details of the approval request form into the reception unit. The details of the approval request form input by the user include detailed information such as the content, purpose, and amount of the approval. For example, the reception unit allows the user to input budget requests for new projects or requests for equipment purchases. Step 2: The analysis unit uses AI to analyze the content of the approval request form entered by the reception unit. The analysis unit analyzes the content of the approval request form using, for example, text analysis technology or natural language processing technology, and extracts information such as the content, purpose, and amount of the approval request. Step 3: The identification unit identifies the approver based on the contents of the approval request form analyzed by the analysis unit. For example, the identification unit identifies the approver based on the amount based on the approval request regulations. It can also identify the appropriate approver based on the content and purpose of the approval request. Step 4: The consent department identifies the consenter based on the decision maker identified by the identification department. For example, if approval from a specific department is required based on the approval regulations, the consent department will set the person in charge of that department as the consenter. It can also identify the appropriate consenter depending on the content and purpose of the approval.

[0088] (Example 2) A request form issuance system according to an embodiment of the present invention automatically analyzes the contents of a request form and automatically assigns the approver and approvers based on the request form regulations. The request form issuance system improves the efficiency of request form issuance by having a user input the request form contents, and an AI analyzes the contents to identify the appropriate approver and approver. For example, in the request form issuance system, a user inputs detailed information such as the request form content, purpose, and amount. The request form issuance system then analyzes the input request form contents using AI and identifies the appropriate approver and approver in accordance with the request form regulations. This eliminates the need for manual approval setting and allows the request form approval process to proceed smoothly. This improves the efficiency of request form issuance and eliminates the need for manual approval setting. For example, when a user submits a budget request for a new project, the approval system will set the appropriate approver based on the amount and set the person in charge of the relevant department as the approver, which will expedite the approval process and ensure a smooth start to the project.

[0089] The approval request form issuance system according to the embodiment includes a reception unit, an analysis unit, an identification unit, and an agreement unit. A user inputs the approval request form content into the reception unit. The approval request form content input by the user includes, but is not limited to, detailed information such as the approval request content, purpose, and amount. For example, the reception unit allows the user to input a budget request for a new project or an equipment purchase request. The analysis unit uses AI to analyze the approval request form content input by the reception unit. The analysis unit analyzes the approval request form content using, for example, text analysis technology and extracts information such as the approval request content, purpose, and amount. The analysis unit can also analyze the approval request form content using natural language processing technology. The identification unit identifies an approver based on the approval request form content analyzed by the analysis unit. For example, the identification unit identifies an approver based on the amount based on approval request regulations. The identification unit can also identify an appropriate approver based on the approval request content and purpose. The consent unit identifies the approver based on the approver identified by the identification unit. For example, if approval from a specific department is required based on the approval regulations, the consent unit sets the person in charge of that department as the approver. The consent unit can also identify an appropriate approver depending on the content and purpose of the approval. This allows the approval request form issuance system according to the embodiment to improve the efficiency of approval request form issuance and eliminate the need for manual approval request form setting. For example, when a user submits a budget request for a new project, the approval request form issuance system sets an appropriate approver according to the amount and sets the person in charge of the relevant department as the approver. This allows the approval process for the approval request to proceed quickly and the project to start smoothly.

[0090] The approval document issuing system is equipped with an update unit to respond to changes to approval document regulations. The update unit provides functions for responding to changes to approval document regulations. For example, the update unit automates the procedure for changing approval document regulations and reflects the latest approval document regulations in the system. The update unit also manages the change history of approval document regulations and allows past changes to be referenced. Furthermore, the update unit is equipped with a notification function for changes to approval document regulations, and can notify relevant parties of the changes. This allows the approval document issuing system to respond to changes to approval document regulations.

[0091] The approval request system is equipped with an exception processing unit that performs exception processing under specific conditions. The exception processing unit provides the function of performing exception processing under specific conditions. For example, the exception processing unit identifies conditions that require exception processing based on approval request regulations and performs processing according to those conditions. The exception processing unit can also provide an interface that allows users to manually perform exception processing. Furthermore, the exception processing unit manages the history of exception processing and makes it possible to refer to the contents of past exception processing. This enables the approval request system to perform exception processing under specific conditions.

[0092] The approval request form system includes a correction unit that allows users to manually correct the approval request form content. The correction unit provides a function for users to manually correct the approval request form content. For example, the correction unit provides an interface for users to edit the approval request form content. The correction unit also manages the history of correction content and allows users to refer to past correction content. Furthermore, the correction unit can automatically reanalyze the content based on the correction content and reset the appropriate approver and approver. This allows users to manually correct the approval request form system.

[0093] The approval request issuing system is equipped with a security unit that protects the data of approval request contents and controls access. The security unit provides functions for protecting the data of approval request contents and controlling access. For example, the security unit encrypts and stores the contents of approval request to protect them from unauthorized access. The security unit also performs user authentication and authority management to achieve access control. Furthermore, the security unit manages access logs and can track the access history to the approval request contents. This enables the approval request issuing system to protect the data of approval request contents and control access.

[0094] The approval request issuance system is equipped with a natural language processing unit that understands the context of the approval request content and improves the accuracy of analysis. The natural language processing unit provides functions for understanding the context of the approval request content and improving the accuracy of analysis. For example, the natural language processing unit uses natural language processing technology to analyze the approval request content and understand the context. The natural language processing unit can also use a context analysis algorithm to accurately grasp the meaning of the approval request content. Furthermore, the natural language processing unit can identify the appropriate approver and consenter based on the analysis results. This allows the approval request issuance system to understand the context of the approval request content and improve the accuracy of analysis.

[0095] The reception unit can estimate the user's emotions and adjust the input interface for the approval request form content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Also, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the approval request form content. This makes it possible to adjust the input interface according to the user's emotions.

[0096] The reception unit can analyze the user's past history of submitting approval requests and suggest the optimal input method. For example, the reception unit can automatically display as candidates the approval request contents that the user has frequently entered in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the approval request contents to be used in a specific time period based on the user's past history of submitting approval requests. This makes it possible to suggest the optimal input method based on the user's past history.

[0097] The reception unit can customize input fields based on the user's current work situation and areas of interest when entering the details of the approval request form. For example, the reception unit allows the user to preferentially enter approval details related to a currently ongoing project. The reception unit can also automatically display relevant input fields based on the user's areas of interest. Furthermore, the reception unit can dynamically adjust the required input fields depending on the user's work situation. This makes it possible to customize input fields according to the user's work situation and areas of interest.

[0098] The reception unit can select the optimal input means depending on the user's input method when entering the contents of the approval request form. For example, when the user inputs the approval request content by voice, the reception unit converts it into text using voice recognition technology. In addition, when the user inputs the approval request content using an image, the reception unit can also analyze the content using image recognition technology. Furthermore, when the user inputs the approval request content using text, the reception unit can also provide an input completion function to enable efficient input. This makes it possible to select the optimal input means depending on the user's input method.

[0099] The reception unit can estimate the user's emotions and determine the priority of input contents based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit can display important input contents preferentially to enable the user to input them quickly. Furthermore, when the user is relaxed, the reception unit can sequentially display detailed input contents to enable the user to input them carefully. Furthermore, when the user is in a hurry, the reception unit can display the most important input contents first to enable the user to input them quickly. This makes it possible to determine the priority of input contents according to the user's emotions.

[0100] When inputting the contents of the approval request form, the reception unit can prioritize the input of highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize the display of approval request contents related to that area. The reception unit can also automatically input information about related departments and personnel based on the user's current location. Furthermore, the reception unit can automatically display related regulations and rules based on the user's geographical location information. This makes it possible to prioritize the input of highly relevant information based on the user's geographical location information.

[0101] The reception unit can analyze the user's social media activity and input related information when entering the details of the approval request form. For example, the reception unit can automatically input related approval request details based on information shared by the user on social media. The reception unit can also analyze the user's social media activity and automatically input information about related departments and personnel. Furthermore, the reception unit can automatically display related regulations and rules based on the content posted by the user on social media. This makes it possible to input related information based on the user's social media activity.

[0102] The reception unit can customize the input method by reflecting the user's past feedback when inputting the contents of the approval request form. For example, the reception unit can suggest the optimal input method based on the user's past feedback. The reception unit can also improve the input interface by reflecting the user's past feedback. Furthermore, the reception unit can also optimize the input procedure based on the user's past feedback. This makes it possible to customize the input method based on the user's past feedback.

[0103] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can also provide analysis results that focus on the main points. This makes it possible to adjust the way the analysis is presented according to the user's emotions.

[0104] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the approval request content. For example, the analysis unit performs a detailed analysis on approval request content with a high level of importance. The analysis unit can also perform a simplified analysis on approval request content with a low level of importance. Furthermore, the analysis unit can dynamically adjust the depth and scope of the analysis according to the importance. This makes it possible to adjust the level of detail of the analysis according to the importance of the approval request content.

[0105] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the approval request content. For example, the analysis unit applies a financial data analysis algorithm to a budget request. The analysis unit can also apply an equipment evaluation algorithm to an equipment purchase request. Furthermore, the analysis unit can also apply a project evaluation algorithm to a project proposal. This makes it possible to apply analysis algorithms depending on the category of the approval request content.

[0106] 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 improves the current analysis accuracy based on the analysis results performed by the user in the past. The analysis unit can also refer to the user's past analysis results to perform optimal analysis for similar approval requests. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. This makes it possible to improve the analysis accuracy based on the user's past analysis results.

[0107] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can also provide an analysis result with visually stimulating effects. This makes it possible to adjust the length of the analysis according to the user's emotions.

[0108] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the approval request content. For example, the analysis unit can prioritize analysis of urgent approval request content. The analysis unit can also prioritize analysis of approval request content with an approaching submission deadline. Furthermore, the analysis unit can dynamically adjust the priority of analysis depending on the submission time. This makes it possible to determine the priority of analysis depending on the submission time of the approval request content.

[0109] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the approval request contents. For example, the analysis unit prioritizes analysis of highly relevant approval request contents. The analysis unit can also postpone analysis of less relevant approval request contents. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the approval request contents. This makes it possible to adjust the order of analysis based on the relevance of the approval request contents.

[0110] 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 provide the analysis results using detailed technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis results in simple terms. Furthermore, the analysis unit can dynamically adjust the way in which the analysis results are presented according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0111] The identification unit can estimate the user's emotions and adjust the method for identifying the decision maker based on the estimated user emotions. For example, if the user is nervous, the identification unit can provide a simple and highly visible method for identifying the decision maker. Furthermore, if the user is relaxed, the identification unit can also provide a detailed method for identifying the decision maker. Furthermore, if the user is in a hurry, the identification unit can also provide a method for identifying the decision maker that focuses on the main points. This makes it possible to adjust the method for identifying the decision maker according to the user's emotions.

[0112] When identifying an authorizer, the identification unit can adjust the level of detail of the identification based on the importance of the approval request content. For example, the identification unit performs detailed authorizer identification for approval request content with high importance. The identification unit can also perform simplified authorizer identification for approval request content with low importance. Furthermore, the identification unit can dynamically adjust the depth and scope of authorizer identification according to the importance. This makes it possible to adjust the level of detail of authorizer identification according to the importance of the approval request content.

[0113] When identifying the decision maker, the identification unit can apply different identification algorithms depending on the category of the approval request content. For example, the identification unit can apply a financial data analysis algorithm to a budget request. The identification unit can also apply an equipment evaluation algorithm to an equipment purchase request. Furthermore, the identification unit can also apply a project evaluation algorithm to a project proposal. This makes it possible to apply an algorithm for identifying the decision maker depending on the category of the approval request content.

[0114] When identifying an authorizer, the identification unit can improve the accuracy of the identification by referring to the user's past identification results. The identification unit improves the current identification accuracy, for example, based on the user's past authorizer identification results. The identification unit can also refer to the user's past identification results to identify the most suitable authorizer for similar approval requests. Furthermore, the identification unit can analyze the user's past identification results and optimize the identification algorithm. This makes it possible to improve the accuracy of identifying an authorizer based on the user's past identification results.

[0115] The identification unit can estimate the user's emotions and determine the priority of identifying decision makers based on the estimated user's emotions. For example, when the user is feeling stressed, the identification unit can prioritize identifying important decision makers. Furthermore, when the user is relaxed, the identification unit can perform detailed decision maker identification. Furthermore, when the user is in a hurry, the identification unit can first identify the most important decision makers. This makes it possible to determine the priority of identifying decision makers according to the user's emotions.

[0116] When identifying an authorizer, the identification unit can determine the priority of the identification based on the submission time of the approval request content. For example, the identification unit can prioritize the identification of an authorizer for urgent approval requests. The identification unit can also prioritize the identification of an authorizer for approval requests with an approaching submission deadline. Furthermore, the identification unit can dynamically adjust the priority of the identification of an authorizer depending on the submission time. This makes it possible to determine the priority of the identification of an authorizer depending on the submission time of the approval request content.

[0117] When identifying authorizers, the identification unit can adjust the identification order based on the relevance of the approval request content. For example, the identification unit can prioritize identifying authorizers for highly relevant approval request content. The identification unit can also postpone identifying authorizers for less relevant approval request content. Furthermore, the identification unit can dynamically adjust the order of authorizer identification according to the relevance of the approval request content. This makes it possible to adjust the order of authorizer identification according to the relevance of the approval request content.

[0118] When identifying an authorizer, the identification unit can adjust the use of specific technical terms depending on the user's level of expertise. For example, if the user has specialized knowledge, the identification unit can identify the authorizer using detailed technical terms. Alternatively, if the user does not have specialized knowledge, the identification unit can identify the authorizer using simple language. Furthermore, the identification unit can dynamically adjust the way in which the authorizer is identified depending on the user's level of expertise. This makes it possible to adjust the use of specific technical terms for the authorizer depending on the user's level of expertise.

[0119] The consent unit can estimate the user's emotions and adjust the method for identifying consenters based on the estimated user emotions. For example, if the user is nervous, the consent unit can provide a simple and highly visible method for identifying consenters. Furthermore, if the user is relaxed, the consent unit can also provide a detailed method for identifying consenters. Furthermore, if the user is in a hurry, the consent unit can also provide a method for identifying consenters that focuses on the main points. This makes it possible to adjust the method for identifying consenters according to the user's emotions.

[0120] When identifying consenting parties, the consent unit can adjust the level of detail of identification based on the importance of the approval request content. For example, the consent unit performs detailed consenting party identification for approval request content with a high level of importance. The consent unit can also perform simplified consenting party identification for approval request content with a low level of importance. Furthermore, the consent unit can dynamically adjust the depth and scope of consenting party identification according to the importance. This makes it possible to adjust the level of detail of consenting party identification according to the importance of the approval request content.

[0121] When identifying approvers, the consent unit can apply different identification algorithms depending on the category of the approval request content. For example, the consent unit can apply a financial data analysis algorithm to a budget request. The consent unit can also apply an equipment evaluation algorithm to an equipment purchase request. Furthermore, the consent unit can also apply a project evaluation algorithm to a project proposal. This makes it possible to apply an algorithm for identifying approvers depending on the category of the approval request content.

[0122] When identifying consenting parties, the consent unit can improve the accuracy of identification by referring to the user's past identification results. For example, the consent unit improves the current identification accuracy based on consenting party identification results performed by the user in the past. The consent unit can also refer to the user's past identification results to identify the most appropriate consenting party for similar approval requests. Furthermore, the consent unit can analyze the user's past identification results and optimize the identification algorithm. This makes it possible to improve the accuracy of identifying consenting parties based on the user's past identification results.

[0123] The consent unit can estimate the user's emotions and determine the priority of identifying consenters based on the estimated user's emotions. For example, when the user is feeling stressed, the consent unit preferentially identifies important consenters. Also, when the user is relaxed, the consent unit can perform detailed consenter identification. Furthermore, when the user is in a hurry, the consent unit can first identify the most important consenters. This makes it possible to determine the priority of identifying consenters according to the user's emotions.

[0124] When identifying consenting parties, the consenting unit can determine a specific priority order based on the submission time of the approval request content. For example, the consenting unit can prioritize identifying consenting parties for urgent approval requests. The consenting unit can also prioritize identifying consenting parties for approval requests whose submission deadline is approaching. Furthermore, the consenting unit can dynamically adjust the priority order for identifying consenting parties according to the submission time. This makes it possible to determine the priority order for identifying consenting parties according to the submission time of the approval request content.

[0125] When identifying consenting parties, the consenting unit can adjust the identification order based on the relevance of the approval request content. For example, the consenting unit can prioritize identifying consenting parties for highly relevant approval request content. The consenting unit can also postpone identifying consenting parties for less relevant approval request content. Furthermore, the consenting unit can dynamically adjust the identification order of consenting parties according to the relevance of the approval request content. This makes it possible to adjust the identification order of consenting parties according to the relevance of the approval request content.

[0126] When identifying a consenting party, the consenting unit can adjust the use of specific technical terms according to the user's level of expertise. For example, if the user has specialized knowledge, the consenting unit can identify the consenting party using detailed technical terms. Alternatively, if the user does not have specialized knowledge, the consenting unit can also identify the consenting party using simple language. Furthermore, the consenting unit can dynamically adjust the way in which the consenting party is identified according to the user's level of expertise. This makes it possible to adjust the use of specific technical terms for the consenting party according to the user's level of expertise.

[0127] The update unit can estimate the user's emotions and adjust the method for updating the approval rules based on the estimated user's emotions. For example, the update unit can provide a simple update procedure when the user is stressed. The update unit can also provide a detailed update procedure when the user is relaxed. Furthermore, the update unit can also provide a procedure for quick update when the user is in a hurry. This makes it possible to adjust the method for updating the approval rules according to the user's emotions.

[0128] When updating approval regulations, the update unit can refer to past update history to select the optimal update method. For example, the update unit can propose the optimal update procedure based on the past update history. The update unit can also refer to the past update history to select the optimal update method for similar approval regulations. Furthermore, the update unit can analyze the past update history and optimize the update procedure. This makes it possible to select the optimal update method based on the past update history.

[0129] The update unit can improve the updated content by reflecting user feedback when updating the approval request rules. For example, the update unit improves the updated content of the approval request rules based on feedback provided by the user. The update unit can also optimize the update procedure by reflecting user feedback. Furthermore, the update unit can dynamically adjust the updated content based on user feedback. This makes it possible to improve the updated content of the approval request rules based on user feedback.

[0130] The update unit can estimate the user's emotions and adjust the update frequency of the approval regulations based on the estimated user's emotions. For example, the update unit can set the update frequency low when the user is feeling stressed. The update unit can also set the update frequency high when the user is relaxed. Furthermore, the update unit can also adjust the update frequency so that updates can be made quickly when the user is in a hurry. This makes it possible to adjust the update frequency of the approval regulations according to the user's emotions.

[0131] When updating the approval regulations, the update unit can weight the update contents based on the submission date of the approval contents. For example, the update unit prioritizes updating approval contents whose submission date is approaching. The update unit can also postpone updating approval contents whose submission date is further away. Furthermore, the update unit can dynamically adjust the weighting of the update contents depending on the submission date. This makes it possible to weight the update contents based on the submission date of the approval contents.

[0132] When updating the approval regulations, the update unit can integrate information from different data sources to enhance the updated content. For example, the update unit integrates information from different data sources to enhance the updated content of the approval regulations. The update unit can also refer to information from different data sources and select the optimal updated content. Furthermore, the update unit can dynamically adjust the updated content based on information from different data sources. This makes it possible to enhance the updated content by integrating information from different data sources.

[0133] The exception processing unit can estimate the user's emotions and adjust the exception processing method based on the estimated user's emotions. For example, if the user is feeling stressed, the exception processing unit can provide a simple exception processing procedure. If the user is relaxed, the exception processing unit can also provide a detailed exception processing procedure. Furthermore, if the user is in a hurry, the exception processing unit can also provide a procedure for quickly processing the exception. This makes it possible to adjust the exception processing method according to the user's emotions.

[0134] When handling an exception, the exception processing unit can select the optimal processing method by referring to the past exception processing history. For example, the exception processing unit proposes the optimal processing procedure based on the past exception processing history. The exception processing unit can also select the optimal processing method for similar approval requests by referring to the past exception processing history. Furthermore, the exception processing unit can analyze the past exception processing history and optimize the processing procedure. This makes it possible to select the optimal processing method based on the past exception processing history.

[0135] The exception processing unit can improve the processing method by reflecting user feedback during exception processing. For example, the exception processing unit improves the exception processing procedure based on feedback provided by the user. The exception processing unit can also optimize the processing procedure by reflecting user feedback. Furthermore, the exception processing unit can dynamically adjust the processing method based on user feedback. This makes it possible to improve the exception processing method based on user feedback.

[0136] The exception processing unit can estimate the user's emotions and determine the priority of exception processing based on the estimated user's emotions. For example, if the user is feeling stressed, the exception processing unit can prioritize important exception processing. Also, if the user is relaxed, the exception processing unit can perform detailed exception processing. Furthermore, if the user is in a hurry, the exception processing unit can perform the most important exception processing first. This makes it possible to determine the priority of exception processing according to the user's emotions.

[0137] During exception processing, the exception processing unit can weight the processing content based on the submission time of the approval request content. For example, the exception processing unit prioritizes exception processing for approval request content whose submission time is approaching. The exception processing unit can also postpone exception processing for approval request content whose submission time is further away. Furthermore, the exception processing unit can dynamically adjust the weighting of the exception processing depending on the submission time. This makes it possible to weight the processing content based on the submission time of the approval request content.

[0138] The exception processing unit can integrate information from different data sources during exception processing to enhance the processing content. For example, the exception processing unit integrates information from different data sources to enhance the exception processing content. The exception processing unit can also refer to information from different data sources to select the optimal processing content. Furthermore, the exception processing unit can dynamically adjust the processing content based on information from different data sources. This makes it possible to enhance the processing content by integrating information from different data sources.

[0139] The correction unit can estimate the user's emotion and adjust the correction method based on the estimated user's emotion. For example, if the user is feeling stressed, the correction unit can provide a simple correction procedure. If the user is relaxed, the correction unit can also provide a detailed correction procedure. Furthermore, if the user is in a hurry, the correction unit can also provide a procedure that allows quick correction. This makes it possible to adjust the correction method according to the user's emotion.

[0140] When making corrections, the correction department can refer to past correction history to select the optimal correction method. For example, the correction department can propose the optimal correction procedure based on the past correction history. The correction department can also refer to past correction history to select the optimal correction method for similar approval requests. Furthermore, the correction department can analyze past correction history and optimize the correction procedure. This makes it possible to select the optimal correction method based on the past correction history.

[0141] The correction unit can improve the correction content by reflecting user feedback during correction. For example, the correction unit improves the correction content based on feedback provided by the user. The correction unit can also optimize the correction procedure by reflecting user feedback. Furthermore, the correction unit can dynamically adjust the correction content based on user feedback. This makes it possible to improve the correction content based on user feedback.

[0142] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user's emotions. For example, when the user is feeling stressed, the correction unit prioritizes important corrections. Furthermore, when the user is relaxed, the correction unit can also perform detailed corrections. Furthermore, when the user is in a hurry, the correction unit can also perform the most important corrections first. This makes it possible to determine the priority of corrections according to the user's emotions.

[0143] When making corrections, the correction department can weight the correction contents based on the submission date of the approval request contents. For example, the correction department prioritizes corrections to approval request contents whose submission date is close. The correction department can also postpone corrections to approval request contents whose submission date is further away. Furthermore, the correction department can dynamically adjust the weighting of the correction contents depending on the submission date. This makes it possible to weight the correction contents based on the submission date of the approval request contents.

[0144] The correction unit can integrate information from different data sources to expand the correction content when making corrections. For example, the correction unit integrates information from different data sources to expand the correction content. The correction unit can also refer to information from different data sources and select optimal correction content. Furthermore, the correction unit can dynamically adjust the correction content based on information from different data sources. This makes it possible to expand the correction content by integrating information from different data sources.

[0145] The security unit can estimate the user's emotions and adjust security measures based on the estimated user emotions. For example, if the user is feeling stressed, the security unit can provide simple security measures. If the user is feeling relaxed, the security unit can also provide detailed security measures. Furthermore, if the user is in a hurry, the security unit can also provide a procedure for quickly implementing security measures. This makes it possible to adjust security measures according to the user's emotions.

[0146] When implementing security measures, the security department can select the most appropriate measures by referring to past security incidents. For example, the security department can propose the most appropriate countermeasure procedure based on past security incidents. The security department can also select the most appropriate countermeasure for similar approval requests by referring to past security incidents. Furthermore, the security department can analyze past security incidents and optimize the countermeasure procedure. This makes it possible to select the most appropriate countermeasure based on past security incidents.

[0147] The security unit can improve the security countermeasures by reflecting user feedback. For example, the security unit improves the security countermeasures based on feedback provided by the user. The security unit can also optimize the countermeasure procedures by reflecting user feedback. Furthermore, the security unit can dynamically adjust the countermeasures based on user feedback. This makes it possible to improve the security countermeasures based on user feedback.

[0148] The security unit can estimate the user's emotions and determine the priority of security measures based on the estimated user's emotions. For example, if the user is feeling stressed, the security unit can prioritize important security measures. Also, if the user is relaxed, the security unit can implement detailed security measures. Furthermore, if the user is in a hurry, the security unit can implement the most important security measures first. This makes it possible to determine the priority of security measures according to the user's emotions.

[0149] When implementing security measures, the security department can weight the measures based on the submission date of the approval request content. For example, the security department can prioritize security measures for approval requests whose submission date is approaching. The security department can also postpone security measures for approval requests whose submission date is further away. Furthermore, the security department can dynamically adjust the weighting of security measures according to the submission date. This makes it possible to weight the measures based on the submission date of the approval request content.

[0150] When implementing security measures, the security department can integrate information from different data sources to enhance the content of the measures. For example, the security department integrates information from different data sources to enhance the content of the security measures. The security department can also refer to information from different data sources to select the optimal content of the measures. Furthermore, the security department can dynamically adjust the content of the measures based on information from different data sources. This makes it possible to enhance the content of the measures by integrating information from different data sources.

[0151] The natural language processing unit can estimate the user's emotions and adjust the natural language processing method based on the estimated user's emotions. For example, if the user is feeling stressed, the natural language processing unit can provide a simple natural language processing procedure. Alternatively, if the user is relaxed, the natural language processing unit can provide a detailed natural language processing procedure. Furthermore, if the user is in a hurry, the natural language processing unit can provide a procedure that allows for quick natural language processing. This makes it possible to adjust the natural language processing method according to the user's emotions.

[0152] During natural language processing, the natural language processing unit can select the optimal processing method by referring to past processing history. For example, the natural language processing unit proposes the optimal natural language processing procedure based on the past processing history. The natural language processing unit can also select the optimal processing method for similar approval requests by referring to the past processing history. Furthermore, the natural language processing unit can analyze the past processing history and optimize the processing procedure. This makes it possible to select the optimal processing method based on the past processing history.

[0153] The natural language processing unit can improve the processing content by reflecting user feedback during natural language processing. The natural language processing unit improves the natural language processing content based on, for example, feedback provided by the user. The natural language processing unit can also optimize the processing procedure by reflecting user feedback. Furthermore, the natural language processing unit can dynamically adjust the processing content based on user feedback. This makes it possible to improve the natural language processing content based on user feedback.

[0154] The natural language processing unit can estimate the user's emotions and determine the priority of natural language processing based on the estimated user's emotions. For example, if the user is feeling stressed, the natural language processing unit can prioritize important natural language processing. Also, if the user is relaxed, the natural language processing unit can perform detailed natural language processing. Furthermore, if the user is in a hurry, the natural language processing unit can perform the most important natural language processing first. This makes it possible to determine the priority of natural language processing according to the user's emotions.

[0155] During natural language processing, the natural language processing unit can weight the processing content based on the submission time of the approval request content. For example, the natural language processing unit prioritizes natural language processing for approval request content that is due to be submitted soon. The natural language processing unit can also postpone natural language processing for approval request content that is due to be submitted further away. Furthermore, the natural language processing unit can dynamically adjust the weighting of the natural language processing depending on the submission time. This makes it possible to weight the processing content based on the submission time of the approval request content.

[0156] During natural language processing, the natural language processing unit can integrate information from different data sources to enhance the processing content. For example, the natural language processing unit integrates information from different data sources to enhance the natural language processing content. The natural language processing unit can also refer to information from different data sources to select the optimal processing content. Furthermore, the natural language processing unit can dynamically adjust the processing content based on information from different data sources. This makes it possible to enhance the processing content by integrating information from different data sources.

[0157] During natural language processing, the natural language processing unit can adjust the use of technical terminology in the processing according to the user's level of expertise. For example, if the user has technical knowledge, the natural language processing unit performs natural language processing using detailed technical terminology. Also, if the user does not have technical knowledge, the natural language processing unit can perform natural language processing using simple language. Furthermore, the natural language processing unit can dynamically adjust the expression method of the natural language processing according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the processing according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, consent unit, update unit, exception processing unit, correction unit, security unit, and natural language processing unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and a user inputs the contents of the approval request form. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the approval request form using AI. The identification unit and consent unit are realized by the specific processing unit 290 of the data processing device 12, and identify the approver and consenter based on the approval request form regulations. The update unit is realized by the specific processing unit 290 of the data processing device 12, and responds to changes in the approval request regulations. The exception processing unit is realized by the specific processing unit 290 of the data processing device 12, and performs exception processing under specific conditions. The correction unit is realized by the control unit 46A of the smart device 14, and a user manually corrects the contents of the approval request form. The security unit is realized by the specific processing unit 290 of the data processing device 12, and performs data protection and access control for the content of the approval request. The natural language processing unit is realized by the specific processing unit 290 of the data processing device 12, and understands the context of the content of the approval request and improves the accuracy of analysis. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, consent unit, update unit, exception processing unit, correction unit, security unit, and natural language processing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and a user inputs the contents of the approval request form. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the approval request form using AI. The identification unit and consent unit are realized by the specific processing unit 290 of the data processing device 12, and identify the approver and the consenter based on the approval request rules. The update unit is realized by the specific processing unit 290 of the data processing device 12, and responds to changes in the approval request rules. The exception processing unit is realized by the specific processing unit 290 of the data processing device 12, and performs exception processing under specific conditions. The correction unit is realized by the control unit 46A of the smart glasses 214, and a user manually corrects the contents of the approval request form. The security unit is realized by the specific processing unit 290 of the data processing device 12, and performs data protection and access control for the content of the approval request. The natural language processing unit is realized by the specific processing unit 290 of the data processing device 12, and understands the context of the content of the approval request and improves the accuracy of analysis. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, consent unit, update unit, exception processing unit, correction unit, security unit, and natural language processing unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314, and a user inputs the contents of the approval request form. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the approval request form using AI. The identification unit and consent unit are realized by the specific processing unit 290 of the data processing device 12, and identify the approver and the consenter based on the approval request rules. The update unit is realized by the specific processing unit 290 of the data processing device 12, and responds to changes in the approval request rules. The exception processing unit is realized by the specific processing unit 290 of the data processing device 12, and performs exception processing under specific conditions. The correction unit is realized by the control unit 46A of the headset terminal 314, and a user manually corrects the contents of the approval request form. The security unit is realized by the specific processing unit 290 of the data processing device 12, and performs data protection and access control for the content of the approval request. The natural language processing unit is realized by the specific processing unit 290 of the data processing device 12, and understands the context of the content of the approval request and improves the accuracy of analysis. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, identification unit, consent unit, update unit, exception processing unit, correction unit, security unit, and natural language processing unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and a user inputs the contents of the approval request form. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the approval request form using AI. The identification unit and consent unit are realized by the specific processing unit 290 of the data processing device 12, and identify the approver and the consenter based on the approval request rules. The update unit is realized by the specific processing unit 290 of the data processing device 12, and responds to changes in the approval request rules. The exception processing unit is realized by the specific processing unit 290 of the data processing device 12, and performs exception processing under specific conditions. The correction unit is realized by the control unit 46A of the robot 414, and a user manually corrects the contents of the approval request form. The security unit is realized by the specific processing unit 290 of the data processing device 12, and performs data protection and access control for the content of the approval request. The natural language processing unit is realized by the specific processing unit 290 of the data processing device 12, and understands the context of the content of the approval request and improves the accuracy of analysis.

[0158] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0159] The reception unit can analyze the user's past history of submitting approval requests and suggest the optimal input method. For example, it can automatically display as candidates the approval content that the user has frequently entered in the past. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the approval content that will be used in a specific time period based on the user's past history of submitting approval requests. This makes it possible to suggest the optimal input method based on the user's past history.

[0160] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can also provide analysis results that focus on the main points. This makes it possible to adjust the way the analysis is presented according to the user's emotions.

[0161] When identifying the decision maker, the identification department can apply different identification algorithms depending on the category of the approval request content. For example, a financial data analysis algorithm can be applied to a budget request. The identification department can also apply an equipment evaluation algorithm to an equipment purchase request. Furthermore, the identification department can also apply a project evaluation algorithm to a project proposal. This makes it possible to apply an algorithm for identifying the decision maker depending on the category of the approval request content.

[0162] The consent unit can estimate the user's emotions and adjust the method for identifying consenters based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible method for identifying consenters is provided. Furthermore, if the user is relaxed, the consent unit can also provide a detailed method for identifying consenters. Furthermore, if the user is in a hurry, the consent unit can also provide a method for identifying consenters that focuses on the main points. This makes it possible to adjust the method for identifying consenters according to the user's emotions.

[0163] When updating approval regulations, the update unit can integrate information from different data sources to enhance the updated content. For example, information from different data sources can be integrated to enhance the updated content of the approval regulations. The update unit can also reference information from different data sources to select the optimal updated content. Furthermore, the update unit can dynamically adjust the updated content based on information from different data sources. This makes it possible to enhance the updated content by integrating information from different data sources.

[0164] The exception processing unit can estimate the user's emotions and adjust the exception processing method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple exception processing procedure can be provided. Alternatively, if the user is relaxed, the exception processing unit can provide a detailed exception processing procedure. Furthermore, if the user is in a hurry, the exception processing unit can provide a procedure for quickly processing the exception. This makes it possible to adjust the exception processing method according to the user's emotions.

[0165] When making corrections, the correction department can refer to past correction history to select the optimal correction method. For example, the department can propose the optimal correction procedure based on the past correction history. The correction department can also refer to past correction history to select the optimal correction method for similar approval requests. Furthermore, the correction department can analyze past correction history and optimize the correction procedure. This makes it possible to select the optimal correction method based on past correction history.

[0166] The security unit can estimate the user's emotions and adjust security measures based on the estimated user emotions. For example, if the user is feeling stressed, a simple security measure can be provided. Alternatively, if the user is feeling relaxed, the security unit can provide detailed security measures. Furthermore, if the user is in a hurry, the security unit can provide a procedure for quickly implementing security measures. This makes it possible to adjust security measures according to the user's emotions.

[0167] The natural language processing unit can improve the processing content by reflecting user feedback during natural language processing. For example, the natural language processing content is improved based on feedback provided by the user. The natural language processing unit can also optimize the processing procedure by reflecting user feedback. Furthermore, the natural language processing unit can dynamically adjust the processing content based on user feedback. This makes it possible to improve the natural language processing content based on user feedback.

[0168] The reception unit can estimate the user's emotions and determine the priority of input contents based on the estimated user's emotions. For example, if the user is feeling stressed, important input contents can be displayed preferentially to allow the user to input them quickly. In addition, if the user is relaxed, the reception unit can sequentially display detailed input contents to allow the user to input them carefully. Furthermore, if the user is in a hurry, the reception unit can display the most important input contents first to allow the user to input them quickly. This makes it possible to determine the priority of input contents according to the user's emotions.

[0169] The processing flow of the second embodiment will be briefly explained below.

[0170] Step 1: The user inputs the details of the approval request form into the reception unit. The details of the approval request form input by the user include detailed information such as the content, purpose, and amount of the approval. For example, the reception unit allows the user to input budget requests for new projects or requests for equipment purchases. Step 2: The analysis unit uses AI to analyze the content of the approval request form entered by the reception unit. The analysis unit analyzes the content of the approval request form using, for example, text analysis technology or natural language processing technology, and extracts information such as the content, purpose, and amount of the approval request. Step 3: The identification unit identifies the approver based on the contents of the approval request form analyzed by the analysis unit. For example, the identification unit identifies the approver based on the amount based on the approval request regulations. It can also identify the appropriate approver based on the content and purpose of the approval request. Step 4: The consent department identifies the consenter based on the decision maker identified by the identification department. For example, if approval from a specific department is required based on the approval regulations, the consent department will set the person in charge of that department as the consenter. It can also identify the appropriate consenter depending on the content and purpose of the approval.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0191] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0207] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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."

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] [Explanation of symbols]

[0243] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section for inputting the details of the approval request form; an analysis unit that analyzes the content of the approval request form input by the reception unit; an identification unit that identifies an authorizer based on the content of the approval request form analyzed by the analysis unit; a consent unit that identifies a consenter based on the authorizer identified by the identification unit; Equipped with A system characterized by:

2. Equipped with an update section to respond to changes in approval regulations 2. The system of claim 1.

3. It has an exception handling section that handles exceptions under specific conditions.

2. The system of claim 1.

4. Equipped with a correction section that allows users to manually correct 2. The system of claim 1.

5. Equipped with a security department that protects data on approval requests and controls access 2. The system of claim 1.

6. Equipped with a natural language processing system that understands the context of the approval request and improves analysis accuracy 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the input interface for the approval request form based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past approval history and suggest the optimal input method 2. The system of claim 1.

9. The reception unit Customize input fields based on the user's current work situation and areas of interest when entering the details of a request for approval 2. The system of claim 1.

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