system

Abstract

The system according to this embodiment aims to streamline the approval process and improve business operations. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a feedback unit. The reception unit inputs the approval request form. The analysis unit analyzes the approval request form input by the reception unit and checks whether there are any problems with the content or any points that need improvement. The feedback unit provides feedback to the applicant and approver based on the results checked by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the approval process requires time and labor, making efficient business operations difficult.

[0005] The system according to the embodiment aims to streamline the approval process and improve business operations.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a feedback unit. The reception unit inputs an approval application form. The analysis unit analyzes the approval application form input by the reception unit and checks whether there are any problems or points for improvement in the content. The feedback unit feeds back the results checked by the analysis unit to the applicant and the approver. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the approval process and improve business operations. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The internal approval system according to an embodiment of the present invention is a system for realizing efficient business processes in a business environment. This system utilizes a generating AI to automatically perform an initial check of the contents of an approval request. When an approval request is entered into the system, the generating AI analyzes the request and checks whether there are any problems with the contents or areas that need improvement. The generating AI performs the analysis using prompts appropriate to the type of approval request and provides feedback to the applicant and approver. This feedback includes suggestions for improvement and the results of the content check. This system enables a significant reduction in the time required for the approval process, reduces human error, and enables efficient business operations. Furthermore, it reduces the burden on human approvers, contributing to improved productivity throughout the company. For example, when an approval request is entered into the system, the generating AI automatically analyzes the contents and checks for problems and areas for improvement. The generating AI performs the analysis using prompts appropriate to the type of approval request and provides feedback to the applicant. This feedback includes the results of the application content check and suggestions for improvement. This allows applicants to quickly refine their proposals. Furthermore, when the generating AI analyzes the content of proposal applications, it can refer to past proposal data and provide feedback on similar applications. This enables applicants to create more appropriate proposals by referring to past examples. In this way, an internal proposal initial approval system utilizing generating AI streamlines the company's proposal process and contributes to improved business operations. As a result, the internal proposal initial approval system can automatically perform an initial check of proposal content, enabling efficient business operations.

[0029] The internal approval system according to this embodiment comprises a reception unit, an analysis unit, and a feedback unit. The reception unit inputs approval requests. Approval requests include, but are not limited to, expense requests, capital investment requests, and personnel change requests. The reception unit inputs approval requests as electronic forms, for example. The reception unit can also scan paper approval requests to digitize them and input them into the system. Furthermore, the reception unit can convert voice input and handwritten input into digital data for input. For example, the reception unit can convert voice input into text data using voice recognition technology. The reception unit can also convert handwritten input into digital data using OCR technology. The analysis unit uses a generation AI to analyze the approval requests input by the reception unit and check whether there are any problems with the content or any areas that need improvement. The analysis unit, for example, analyzes each item of the approval request to confirm that all the necessary information is included. Furthermore, the analysis unit can apply natural language processing technology using generative AI to evaluate whether the content of the approval request form is appropriate. For example, the analysis unit inputs the content of the approval request form into the generative AI, which then evaluates the appropriateness of the content. In addition, the analysis unit can also suggest areas for improvement based on the content of the approval request form. For example, the analysis unit uses generative AI to analyze the content of the approval request form and extract areas for improvement. The feedback unit provides feedback to the applicant and approver on the results checked by the analysis unit. For example, the feedback unit provides the analysis results to the applicant and approver via email or a notification system. The feedback unit can also display the analysis results in a dashboard or report format. For example, the feedback unit visually displays the analysis results as graphs or charts. As a result, the internal approval system according to this embodiment can automatically perform an initial check of the content of the approval request form, enabling efficient business operations.

[0030] The reception department inputs approval requests. These requests include, but are not limited to, expense requests, capital investment requests, and personnel change requests. The reception department inputs approval requests as electronic forms. Specifically, applicants access a dedicated web form and input the necessary information. The web form is designed so that the fields change dynamically depending on the content of the application, guiding applicants to input all the necessary information. The reception department can also scan paper approval requests, digitize them, and input them into the system. The scanner performs high-resolution scanning and converts the data into text using OCR (optical character recognition) technology. This allows paper application forms to be centrally managed as digital data. Furthermore, the reception department can also input voice input and handwritten input by converting them into digital data. For example, the reception department can convert voice input into text data using speech recognition technology. Speech recognition technology analyzes the applicant's voice in real time and converts it into accurate text data. The reception department can also convert handwritten input into digital data using OCR technology. Handwritten input is performed using tablets or digital pens, and OCR technology converts handwritten characters into text data. This allows the reception department to support various input methods, enabling applicants to submit their approval requests in the most convenient way for them.

[0031] The analysis unit uses generative AI to analyze the approval request forms entered by the reception unit and check whether there are any problems with the content or areas that need improvement. For example, the analysis unit analyzes each item in the approval request form and verifies that all necessary information is included. The generative AI evaluates the appropriateness of each item based on a large amount of approval request form data that it has learned in advance. For example, in the case of an expense request form, it checks whether the requested amount, purpose of use, and related project information are accurately described. The analysis unit can also apply natural language processing technology using the generative AI to evaluate whether the content of the approval request form is appropriate. The generative AI analyzes the content of the request form contextually and evaluates the consistency and logic of the content. For example, in the case of a capital investment request form, it checks whether the necessity of the investment and the expected effects are clearly stated. Furthermore, the analysis unit can also suggest areas for improvement based on the content of the approval request form. The generative AI analyzes the content of the request form and extracts specific areas for improvement based on past data and best practices. For example, if the application form is insufficient, the system will suggest additional information or areas that need correction. This allows the analysis department to quickly and accurately evaluate the content of the approval application and provide specific feedback to the applicant.

[0032] The Feedback Department provides feedback to applicants and approvers on the results checked by the Analysis Department. For example, the Feedback Department provides analysis results to applicants and approvers via email or a notification system. Emails clearly state the details of the analysis results and areas for improvement, allowing applicants to revise their applications based on this information. The notification system sends real-time notifications to applicants' and approvers' devices, encouraging prompt action. Furthermore, the Feedback Department can display analysis results in dashboard or report format. Dashboards visually display the results using graphs and charts, allowing applicants and approvers to grasp the analysis results at a glance. For example, evaluation results and areas for improvement for each item in the application are color-coded, making it immediately clear which parts are problematic. Reports provide detailed analysis results in text format, allowing applicants and approvers to review the specific content. This enables the Feedback Department to provide analysis results in an easy-to-understand manner, supporting applicants and approvers in responding quickly and appropriately. Additionally, the Feedback Department can collect feedback from applicants and approvers to improve the system. For example, data is collected to improve the accuracy of the analysis algorithm based on the revisions submitted by the applicant and the evaluations of the approvers. This allows the feedback unit to continuously improve the overall performance of the system.

[0033] The analysis unit can perform analysis using prompts appropriate to the type of approval request. For example, in the case of an expense request, the analysis unit uses prompts to confirm the details of the expense and the reason for expenditure. In the case of a capital investment request, the analysis unit can also use prompts to confirm the purpose of the investment and the expected effects. Furthermore, in the case of a personnel transfer request, the analysis unit can use prompts to confirm the reason for the transfer and the details of the new position. This allows for more appropriate feedback to be provided by performing analysis according to the type of approval request. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs prompts appropriate to the type of approval request into the generating AI, and the generating AI analyzes the contents of the approval request form based on the prompts.

[0034] The analysis unit can refer to past approval data and provide feedback on similar applications. For example, the analysis unit can search the past approval database to identify similar applications. The analysis unit can also set criteria for evaluating the appropriateness of applications based on past approval data. Furthermore, the analysis unit can refer to past approval data to suggest improvements to applications. This allows for more appropriate feedback to be provided by referring to past approval data. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs past approval data into the generation AI, which analyzes the data and generates feedback on similar applications.

[0035] The Feedback Department can provide the results of the application review and suggestions for improvement. For example, the Feedback Department notifies the applicant of the results checked by the Analysis Department. The Feedback Department can also suggest improvements to the application based on the analysis results. Furthermore, the Feedback Department can provide the review results of the application to the approver and support the approval process. This allows for the refinement of the approval process by providing the review results and suggestions for improvement. Some or all of the above processes in the Feedback Department are performed using AI. For example, the Feedback Department inputs the results from the Analysis Department into the AI, which analyzes the results and generates feedback.

[0036] The analysis unit can implement data encryption and privacy protection measures when analyzing the contents of approval request forms. For example, the analysis unit can encrypt and store the data of the approval request forms. The analysis unit can also implement data access control and privacy protection measures. Furthermore, the analysis unit can anonymize the data to protect personal information. In this way, security can be ensured by encrypting the data and implementing privacy protection measures. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs the data of the approval request forms into the generation AI, which then performs data encryption and anonymization.

[0037] The feedback unit can provide rapid feedback to applicants and approvers. For example, the feedback unit can notify analysis results in real time. The feedback unit can also build a notification system to provide rapid feedback to applicants and approvers. Furthermore, the feedback unit can optimize the method of providing feedback to achieve rapid feedback. This allows for a reduction in the approval process time by providing feedback quickly. Some or all of the above processes in the feedback unit are performed using AI. For example, the feedback unit inputs analysis results into the AI, which analyzes the results and generates rapid feedback.

[0038] The reception desk can analyze the user's past application history when they enter approval request forms and select the most suitable input method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). Furthermore, the reception desk can automatically select a specific format based on the user's past application history, simplifying the input process. In addition, the reception desk can analyze the user's past application history and suggest the most efficient input method. This allows the reception desk to select the optimal input method by analyzing the user's past application history. Some or all of the above processes in the reception desk are performed using AI. For example, the reception desk inputs the user's past application history data into a generating AI, which then analyzes the data and suggests the most suitable input method.

[0039] The reception desk can filter approval requests based on the user's current work situation and areas of interest when the request is entered. For example, the reception desk analyzes the user's current work situation and prioritizes displaying highly relevant approval requests. The reception desk can also filter and display relevant approval requests based on the user's areas of interest. Furthermore, the reception desk can suggest the optimal method for entering approval requests, taking into account the user's work situation and areas of interest. This allows for the priority display of highly relevant approval requests by filtering based on the user's current work situation and areas of interest. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's work situation data and areas of interest data into a generating AI, which then analyzes and filters the data.

[0040] The reception desk can prioritize the input of highly relevant approval requests by considering the user's geographical location when the user is entering an approval request form. For example, the reception desk can prioritize displaying highly relevant approval requests based on the user's current location. The reception desk can also suggest the optimal method for entering approval requests, taking the user's geographical location into consideration. Furthermore, the reception desk can filter and display relevant approval requests based on the user's geographical location. This allows for the priority input of highly relevant approval requests by considering the user's geographical location. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's geographical location data into a generating AI, which analyzes the data and prioritizes displaying highly relevant requests.

[0041] The reception desk can analyze a user's social media activity when they input an approval request form and input relevant requests. For example, the reception desk analyzes the user's social media activity and prioritizes displaying highly relevant approval requests. The reception desk can also suggest the optimal way to input approval requests based on the user's social media activity. Furthermore, the reception desk can filter and display relevant approval requests considering the user's social media activity. This allows for the priority input of highly relevant approval requests by analyzing the user's social media activity. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's social media data into a generating AI, which analyzes the data and inputs relevant requests.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the approval request form during the analysis. For example, the analysis unit will perform a detailed analysis for approval requests with high importance. Conversely, the analysis unit can also perform a simplified analysis for approval requests with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to importance. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the approval request form. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the importance data of the approval request form into the generation AI, and the generation AI adjusts the level of detail of the analysis based on importance.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the approval request during the analysis. For example, the analysis unit can apply a financial analysis algorithm to a financial approval request. It can also apply a human resources analysis algorithm to a human resources approval request. Furthermore, the analysis unit can select and apply the most appropriate analysis algorithm depending on the category of the approval request. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the category of the approval request. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the approval category data into the generation AI, which then applies the most appropriate analysis algorithm according to the category.

[0044] The analysis unit can determine the priority of analysis based on the submission date of the approval request forms. For example, the analysis unit will prioritize the analysis of approval request forms submitted earlier. It can also postpone the analysis of approval request forms submitted later. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission date. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the submission date of the approval request forms. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs the approval request form submission date data into the generating AI, and the generating AI determines the analysis priority based on the submission date.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the approval requests during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant approval requests. It can also postpone the analysis of less relevant approval requests. Furthermore, the analysis unit can dynamically adjust the order of analysis based on relevance. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of the approval requests. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs the relevance data of the approval requests into the generating AI, which then adjusts the order of analysis based on the relevance.

[0046] The feedback unit can analyze the user's past application content and select the optimal feedback method during the feedback process. For example, the feedback unit can propose the optimal feedback method based on the user's past application content. The feedback unit can also analyze the user's past application content and provide highly relevant feedback. Furthermore, the feedback unit can select the optimal feedback method by referring to the user's past application content. In this way, the optimal feedback method can be selected by analyzing the user's past application content. Some or all of the above processes in the feedback unit are performed using AI. For example, the feedback unit inputs the user's past application content data into a generating AI, which analyzes the data and proposes the optimal feedback method.

[0047] The feedback unit can customize the means of feedback based on the user's current work situation. For example, the feedback unit can analyze the user's current work situation and propose the optimal feedback method. The feedback unit can also customize the means of feedback according to the user's work situation. Furthermore, the feedback unit can select the optimal feedback method considering the user's current work situation. This allows for more appropriate feedback to be provided by customizing the means of feedback based on the user's current work situation. Some or all of the above processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's work situation data into a generating AI, which analyzes the data and proposes the optimal feedback method.

[0048] The feedback unit can select the optimal feedback method when providing feedback, taking into account the user's geographical location information. For example, the feedback unit can propose the optimal feedback method based on the user's current location. The feedback unit can also provide highly relevant feedback by considering the user's geographical location information. Furthermore, the feedback unit can select the optimal feedback means based on the user's geographical location information. In this way, the optimal feedback method can be selected by considering the user's geographical location information. Some or all of the above processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's geographical location data into a generating AI, which analyzes the data and proposes the optimal feedback method.

[0049] The feedback unit can analyze the user's social media activity and propose feedback methods during the feedback process. For example, the feedback unit can analyze the user's social media activity and propose the most suitable feedback method. Furthermore, the feedback unit can provide highly relevant feedback based on the user's social media activity. In addition, the feedback unit can select the most suitable feedback method considering the user's social media activity. Thus, by analyzing the user's social media activity, it can propose the most suitable feedback method. Some or all of the above processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's social media data into a generating AI, which analyzes the data and proposes the most suitable feedback method.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The analysis unit can improve the accuracy of its analysis by referring to the user's past feedback history when analyzing the contents of approval requests. For example, it can analyze the feedback the user has received in the past to prevent similar problems from recurring. The analysis unit can also understand the user's tendencies and patterns based on past feedback history to perform more accurate analysis. Furthermore, the analysis unit can optimize the content and format of feedback provided to the user by referring to past feedback history. In this way, the accuracy of the analysis can be improved by referring to past feedback history. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input the user's past feedback history data into the generation AI, which can then analyze the data to improve the accuracy of the analysis.

[0052] The reception department can analyze the user's past application history when they enter approval request forms and provide optimal input assistance. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). The reception department can also automatically select a specific format based on the user's past application history, simplifying the input process. Furthermore, the reception department can analyze the user's past application history and suggest the most efficient input method. In this way, it can provide optimal input assistance by analyzing the user's past application history. Some or all of the above processes in the reception department are performed using AI. For example, the reception department can input the user's past application data into a generating AI, which can then analyze the data and provide optimal input assistance.

[0053] The analysis unit can prioritize the analysis of approval requests by considering the user's current work situation. For example, it can prioritize the analysis of approval requests related to projects the user is currently working on. It can also prioritize the analysis of approval requests with high relevance, depending on the user's work situation. Furthermore, it can select the optimal analysis method considering the user's work situation. This allows for the provision of more appropriate analysis results by considering the user's current work situation. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input user work situation data into the generation AI, which can then analyze the data and determine the analysis priority.

[0054] The feedback unit can analyze the user's past application content to select the optimal feedback method during the feedback process. For example, it can propose the optimal feedback method based on the user's past application content. The feedback unit can also analyze the user's past application content and provide highly relevant feedback. Furthermore, the feedback unit can select the optimal feedback method by referring to the user's past application content. In this way, the optimal feedback method can be selected by analyzing the user's past application content. Some or all of the above processes in the feedback unit are performed using AI. For example, the feedback unit can input the user's past application content data into a generating AI, which can analyze the data and propose the optimal feedback method.

[0055] The reception desk can filter approval requests based on the user's current work situation and areas of interest when the request is entered. For example, it can analyze the user's current work situation and prioritize displaying highly relevant approval requests. It can also filter and display relevant approval requests based on the user's areas of interest. Furthermore, it can suggest the optimal method for entering the approval request, taking into account the user's work situation and areas of interest. This allows for the priority display of highly relevant approval requests by filtering based on the user's current work situation and areas of interest. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's work situation data and areas of interest data into a generating AI, which then analyzes the data and performs filtering.

[0056] The analysis unit can implement data encryption and privacy protection measures when analyzing the contents of approval request forms. For example, it can encrypt and store the data of the approval request form. It can also implement data access control and privacy protection measures. Furthermore, it can anonymize the data to protect personal information. In this way, security can be ensured by encrypting the data and implementing privacy protection measures. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input the data of the approval request form into the generation AI, which can then perform data encryption and anonymization.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception department inputs the approval request forms. These forms include expense requests, capital investment requests, and personnel change requests. The reception department can input the approval request forms as electronic forms, or scan paper forms to digitize them and input them into the system. It can also input voice input or handwritten input by converting it into digital data. For example, voice recognition technology can be used to convert voice input into text data, and OCR technology can be used to convert handwritten input into digital data. Step 2: The analysis unit uses a generation AI to analyze the approval request form entered by the reception unit and check for any problems with its content or areas that need improvement. The analysis unit analyzes each item in the approval request form to confirm that all necessary information is included. It also applies natural language processing technology using the generation AI to evaluate whether the content of the approval request form is appropriate. Furthermore, the analysis unit can also suggest areas for improvement based on the content of the approval request form. Step 3: The Feedback Department provides feedback to the applicant and approver on the results checked by the Analysis Department. The Feedback Department provides the analysis results to the applicant and approver via email or a notification system. The analysis results can also be displayed in dashboard or report format. For example, the analysis results can be displayed visually as graphs or charts.

[0059] (Example of form 2) The internal approval system according to an embodiment of the present invention is a system for realizing efficient business processes in a business environment. This system utilizes a generating AI to automatically perform an initial check of the contents of an approval request. When an approval request is entered into the system, the generating AI analyzes the request and checks whether there are any problems with the contents or areas that need improvement. The generating AI performs the analysis using prompts appropriate to the type of approval request and provides feedback to the applicant and approver. This feedback includes suggestions for improvement and the results of the content check. This system enables a significant reduction in the time required for the approval process, reduces human error, and enables efficient business operations. Furthermore, it reduces the burden on human approvers, contributing to improved productivity throughout the company. For example, when an approval request is entered into the system, the generating AI automatically analyzes the contents and checks for problems and areas for improvement. The generating AI performs the analysis using prompts appropriate to the type of approval request and provides feedback to the applicant. This feedback includes the results of the application content check and suggestions for improvement. This allows applicants to quickly refine their proposals. Furthermore, when the generating AI analyzes the content of proposal applications, it can refer to past proposal data and provide feedback on similar applications. This enables applicants to create more appropriate proposals by referring to past examples. In this way, an internal proposal initial approval system utilizing generating AI streamlines the company's proposal process and contributes to improved business operations. As a result, the internal proposal initial approval system can automatically perform an initial check of proposal content, enabling efficient business operations.

[0060] The internal approval system according to this embodiment comprises a reception unit, an analysis unit, and a feedback unit. The reception unit inputs approval requests. Approval requests include, but are not limited to, expense requests, capital investment requests, and personnel change requests. The reception unit inputs approval requests as electronic forms, for example. The reception unit can also scan paper approval requests to digitize them and input them into the system. Furthermore, the reception unit can convert voice input and handwritten input into digital data for input. For example, the reception unit can convert voice input into text data using voice recognition technology. The reception unit can also convert handwritten input into digital data using OCR technology. The analysis unit uses a generation AI to analyze the approval requests input by the reception unit and check whether there are any problems with the content or any areas that need improvement. The analysis unit, for example, analyzes each item of the approval request to confirm that all the necessary information is included. Furthermore, the analysis unit can apply natural language processing technology using generative AI to evaluate whether the content of the approval request form is appropriate. For example, the analysis unit inputs the content of the approval request form into the generative AI, which then evaluates the appropriateness of the content. In addition, the analysis unit can also suggest areas for improvement based on the content of the approval request form. For example, the analysis unit uses generative AI to analyze the content of the approval request form and extract areas for improvement. The feedback unit provides feedback to the applicant and approver on the results checked by the analysis unit. For example, the feedback unit provides the analysis results to the applicant and approver via email or a notification system. The feedback unit can also display the analysis results in a dashboard or report format. For example, the feedback unit visually displays the analysis results as graphs or charts. As a result, the internal approval system according to this embodiment can automatically perform an initial check of the content of the approval request form, enabling efficient business operations.

[0061] The reception department inputs approval requests. These requests include, but are not limited to, expense requests, capital investment requests, and personnel change requests. The reception department inputs approval requests as electronic forms. Specifically, applicants access a dedicated web form and input the necessary information. The web form is designed so that the fields change dynamically depending on the content of the application, guiding applicants to input all the necessary information. The reception department can also scan paper approval requests, digitize them, and input them into the system. The scanner performs high-resolution scanning and converts the data into text using OCR (optical character recognition) technology. This allows paper application forms to be centrally managed as digital data. Furthermore, the reception department can also input voice input and handwritten input by converting them into digital data. For example, the reception department can convert voice input into text data using speech recognition technology. Speech recognition technology analyzes the applicant's voice in real time and converts it into accurate text data. The reception department can also convert handwritten input into digital data using OCR technology. Handwritten input is performed using tablets or digital pens, and OCR technology converts handwritten characters into text data. This allows the reception department to support various input methods, enabling applicants to submit their approval requests in the most convenient way for them.

[0062] The analysis unit uses generative AI to analyze the approval request forms entered by the reception unit and check whether there are any problems with the content or areas that need improvement. For example, the analysis unit analyzes each item in the approval request form and verifies that all necessary information is included. The generative AI evaluates the appropriateness of each item based on a large amount of approval request form data that it has learned in advance. For example, in the case of an expense request form, it checks whether the requested amount, purpose of use, and related project information are accurately described. The analysis unit can also apply natural language processing technology using the generative AI to evaluate whether the content of the approval request form is appropriate. The generative AI analyzes the content of the request form contextually and evaluates the consistency and logic of the content. For example, in the case of a capital investment request form, it checks whether the necessity of the investment and the expected effects are clearly stated. Furthermore, the analysis unit can also suggest areas for improvement based on the content of the approval request form. The generative AI analyzes the content of the request form and extracts specific areas for improvement based on past data and best practices. For example, if the application form is insufficient, the system will suggest additional information or areas that need correction. This allows the analysis department to quickly and accurately evaluate the content of the approval application and provide specific feedback to the applicant.

[0063] The Feedback Department provides feedback to applicants and approvers on the results checked by the Analysis Department. For example, the Feedback Department provides analysis results to applicants and approvers via email or a notification system. Emails clearly state the details of the analysis results and areas for improvement, allowing applicants to revise their applications based on this information. The notification system sends real-time notifications to applicants' and approvers' devices, encouraging prompt action. Furthermore, the Feedback Department can display analysis results in dashboard or report format. Dashboards visually display the results using graphs and charts, allowing applicants and approvers to grasp the analysis results at a glance. For example, evaluation results and areas for improvement for each item in the application are color-coded, making it immediately clear which parts are problematic. Reports provide detailed analysis results in text format, allowing applicants and approvers to review the specific content. This enables the Feedback Department to provide analysis results in an easy-to-understand manner, supporting applicants and approvers in responding quickly and appropriately. Additionally, the Feedback Department can collect feedback from applicants and approvers to improve the system. For example, data is collected to improve the accuracy of the analysis algorithm based on the revisions submitted by the applicant and the evaluations of the approvers. This allows the feedback unit to continuously improve the overall performance of the system.

[0064] The analysis unit can perform analysis using prompts appropriate to the type of approval request. For example, in the case of an expense request, the analysis unit uses prompts to confirm the details of the expense and the reason for expenditure. In the case of a capital investment request, the analysis unit can also use prompts to confirm the purpose of the investment and the expected effects. Furthermore, in the case of a personnel transfer request, the analysis unit can use prompts to confirm the reason for the transfer and the details of the new position. This allows for more appropriate feedback to be provided by performing analysis according to the type of approval request. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs prompts appropriate to the type of approval request into the generating AI, and the generating AI analyzes the contents of the approval request form based on the prompts.

[0065] The analysis unit can refer to past approval data and provide feedback on similar applications. For example, the analysis unit can search the past approval database to identify similar applications. The analysis unit can also set criteria for evaluating the appropriateness of applications based on past approval data. Furthermore, the analysis unit can refer to past approval data to suggest improvements to applications. This allows for more appropriate feedback to be provided by referring to past approval data. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs past approval data into the generation AI, which analyzes the data and generates feedback on similar applications.

[0066] The Feedback Department can provide the results of the application review and suggestions for improvement. For example, the Feedback Department notifies the applicant of the results checked by the Analysis Department. The Feedback Department can also suggest improvements to the application based on the analysis results. Furthermore, the Feedback Department can provide the review results of the application to the approver and support the approval process. This allows for the refinement of the approval process by providing the review results and suggestions for improvement. Some or all of the above processes in the Feedback Department are performed using AI. For example, the Feedback Department inputs the results from the Analysis Department into the AI, which analyzes the results and generates feedback.

[0067] The analysis unit can implement data encryption and privacy protection measures when analyzing the contents of approval request forms. For example, the analysis unit can encrypt and store the data of the approval request forms. The analysis unit can also implement data access control and privacy protection measures. Furthermore, the analysis unit can anonymize the data to protect personal information. In this way, security can be ensured by encrypting the data and implementing privacy protection measures. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs the data of the approval request forms into the generation AI, which then performs data encryption and anonymization.

[0068] The feedback unit can provide rapid feedback to applicants and approvers. For example, the feedback unit can notify analysis results in real time. The feedback unit can also build a notification system to provide rapid feedback to applicants and approvers. Furthermore, the feedback unit can optimize the method of providing feedback to achieve rapid feedback. This allows for a reduction in the approval process time by providing feedback quickly. Some or all of the above processes in the feedback unit are performed using AI. For example, the feedback unit inputs analysis results into the AI, which analyzes the results and generates rapid feedback.

[0069] The reception desk can estimate the user's emotions and adjust the timing of inputting the approval request form based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can delay the input timing to allow the user to input the form in a relaxed state. If the user is in a hurry, the reception desk can also speed up the input timing to allow the user to input the approval request form quickly. Furthermore, if the user is concentrating, the reception desk can adjust the input timing to prompt input at the optimal time. This allows for more appropriate input of the approval request form by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's emotion data into the generative AI, which estimates the emotion and adjusts the input timing.

[0070] The reception desk can analyze the user's past application history when they enter approval request forms and select the most suitable input method. For example, the reception desk can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). Furthermore, the reception desk can automatically select a specific format based on the user's past application history, simplifying the input process. In addition, the reception desk can analyze the user's past application history and suggest the most efficient input method. This allows the reception desk to select the optimal input method by analyzing the user's past application history. Some or all of the above processes in the reception desk are performed using AI. For example, the reception desk inputs the user's past application history data into a generating AI, which then analyzes the data and suggests the most suitable input method.

[0071] The reception desk can filter approval requests based on the user's current work situation and areas of interest when the request is entered. For example, the reception desk analyzes the user's current work situation and prioritizes displaying highly relevant approval requests. The reception desk can also filter and display relevant approval requests based on the user's areas of interest. Furthermore, the reception desk can suggest the optimal method for entering approval requests, taking into account the user's work situation and areas of interest. This allows for the priority display of highly relevant approval requests by filtering based on the user's current work situation and areas of interest. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's work situation data and areas of interest data into a generating AI, which then analyzes and filters the data.

[0072] The reception desk can estimate the user's emotions and determine the priority of the approval request form to be entered based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can lower the priority to allow them to enter the form in a relaxed state. Conversely, if the user is in a hurry, the reception desk can raise the priority to allow them to enter the approval request form quickly. Furthermore, if the user is concentrating, the reception desk can adjust the priority to prompt them to enter the form at the optimal time. In this way, by determining the priority according to the user's emotions, approval requests can be entered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's emotion data into the generative AI, which estimates the emotions and determines the priority.

[0073] The reception desk can prioritize the input of highly relevant approval requests by considering the user's geographical location when the user is entering an approval request form. For example, the reception desk can prioritize displaying highly relevant approval requests based on the user's current location. The reception desk can also suggest the optimal method for entering approval requests, taking the user's geographical location into consideration. Furthermore, the reception desk can filter and display relevant approval requests based on the user's geographical location. This allows for the priority input of highly relevant approval requests by considering the user's geographical location. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's geographical location data into a generating AI, which analyzes the data and prioritizes displaying highly relevant requests.

[0074] The reception desk can analyze a user's social media activity when they input an approval request form and input relevant requests. For example, the reception desk analyzes the user's social media activity and prioritizes displaying highly relevant approval requests. The reception desk can also suggest the optimal way to input approval requests based on the user's social media activity. Furthermore, the reception desk can filter and display relevant approval requests considering the user's social media activity. This allows for the priority input of highly relevant approval requests by analyzing the user's social media activity. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's social media data into a generating AI, which analyzes the data and inputs relevant requests.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI, which estimates the emotions and adjusts the presentation of the analysis.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the approval request form during the analysis. For example, the analysis unit will perform a detailed analysis for approval requests with high importance. Conversely, the analysis unit can also perform a simplified analysis for approval requests with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to importance. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the approval request form. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the importance data of the approval request form into the generation AI, and the generation AI adjusts the level of detail of the analysis based on importance.

[0077] The analysis unit can apply different analysis algorithms depending on the category of the approval request during the analysis. For example, the analysis unit can apply a financial analysis algorithm to a financial approval request. It can also apply a human resources analysis algorithm to a human resources approval request. Furthermore, the analysis unit can select and apply the most appropriate analysis algorithm depending on the category of the approval request. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the category of the approval request. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the approval category data into the generation AI, which then applies the most appropriate analysis algorithm according to the category.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit inputs the user's emotion data into the generative AI, which estimates the emotions and adjusts the length of the analysis.

[0079] The analysis unit can determine the priority of analysis based on the submission date of the approval request forms. For example, the analysis unit will prioritize the analysis of approval request forms submitted earlier. It can also postpone the analysis of approval request forms submitted later. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission date. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the submission date of the approval request forms. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs the approval request form submission date data into the generating AI, and the generating AI determines the analysis priority based on the submission date.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the approval requests during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant approval requests. It can also postpone the analysis of less relevant approval requests. Furthermore, the analysis unit can dynamically adjust the order of analysis based on relevance. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of the approval requests. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit inputs the relevance data of the approval requests into the generating AI, which then adjusts the order of analysis based on the relevance.

[0081] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is relaxed, the feedback unit can provide detailed feedback. If the user is in a hurry, the feedback unit can provide concise feedback. Furthermore, if the user is stressed, the feedback unit can provide visually easy-to-understand feedback. This allows for more appropriate feedback to be provided by adjusting the feedback method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's emotion data into the generative AI, which estimates the emotion and adjusts the feedback method.

[0082] The feedback unit can analyze the user's past application content and select the optimal feedback method during the feedback process. For example, the feedback unit can propose the optimal feedback method based on the user's past application content. The feedback unit can also analyze the user's past application content and provide highly relevant feedback. Furthermore, the feedback unit can select the optimal feedback method by referring to the user's past application content. In this way, the optimal feedback method can be selected by analyzing the user's past application content. Some or all of the above processes in the feedback unit are performed using AI. For example, the feedback unit inputs the user's past application content data into a generating AI, which analyzes the data and proposes the optimal feedback method.

[0083] The feedback unit can customize the means of feedback based on the user's current work situation. For example, the feedback unit can analyze the user's current work situation and propose the optimal feedback method. The feedback unit can also customize the means of feedback according to the user's work situation. Furthermore, the feedback unit can select the optimal feedback method considering the user's current work situation. This allows for more appropriate feedback to be provided by customizing the means of feedback based on the user's current work situation. Some or all of the above processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's work situation data into a generating AI, which analyzes the data and proposes the optimal feedback method.

[0084] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit can lower the priority and provide more detailed feedback. Conversely, if the user is in a hurry, the feedback unit can raise the priority and provide quicker feedback. Furthermore, if the user is stressed, the feedback unit can adjust the priority to provide feedback at the optimal time. This allows for more appropriate timing of feedback by determining the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit is performed using AI. For example, the feedback unit inputs user emotion data into the generative AI, which estimates the emotions and determines the priority of feedback.

[0085] The feedback unit can select the optimal feedback method when providing feedback, taking into account the user's geographical location information. For example, the feedback unit can propose the optimal feedback method based on the user's current location. The feedback unit can also provide highly relevant feedback by considering the user's geographical location information. Furthermore, the feedback unit can select the optimal feedback means based on the user's geographical location information. In this way, the optimal feedback method can be selected by considering the user's geographical location information. Some or all of the above processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's geographical location data into a generating AI, which analyzes the data and proposes the optimal feedback method.

[0086] The feedback unit can analyze the user's social media activity and propose feedback methods during the feedback process. For example, the feedback unit can analyze the user's social media activity and propose the most suitable feedback method. Furthermore, the feedback unit can provide highly relevant feedback based on the user's social media activity. In addition, the feedback unit can select the most suitable feedback method considering the user's social media activity. Thus, by analyzing the user's social media activity, it can propose the most suitable feedback method. Some or all of the above processing in the feedback unit is performed using AI. For example, the feedback unit inputs the user's social media data into a generating AI, which analyzes the data and proposes the most suitable feedback method.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The reception desk can estimate the user's emotions and customize the input interface for the approval request form based on those emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface. If the user is relaxed, it can also provide detailed input options. Furthermore, if the user is in a hurry, the reception desk can provide shortcuts for quick input. This allows for a more comfortable input experience by customizing the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk can input user emotion data into the generative AI, which can then estimate the emotion and customize the input interface.

[0089] The analysis unit can improve the accuracy of its analysis by referring to the user's past feedback history when analyzing the contents of approval requests. For example, it can analyze the feedback the user has received in the past to prevent similar problems from recurring. The analysis unit can also understand the user's tendencies and patterns based on past feedback history to perform more accurate analysis. Furthermore, the analysis unit can optimize the content and format of feedback provided to the user by referring to past feedback history. In this way, the accuracy of the analysis can be improved by referring to past feedback history. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input the user's past feedback history data into the generation AI, which can then analyze the data to improve the accuracy of the analysis.

[0090] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is relaxed, it can provide detailed feedback. If the user is in a hurry, it can provide concise feedback. Furthermore, if the user is stressed, it can provide visually easy-to-understand feedback. In this way, by adjusting the content of the feedback according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the feedback unit is performed using AI. For example, the feedback unit can input user emotion data into the generative AI, which can estimate the emotion and adjust the content of the feedback.

[0091] The reception desk can estimate the user's emotions and provide input assistance for approval requests based on those emotions. For example, if the user is stressed, the reception desk can enhance the input assistance function to make it easier for the user to input the information. If the user is relaxed, it can also provide detailed input guides. Furthermore, if the user is in a hurry, the reception desk can provide shortcuts for quick input. In this way, a more comfortable input experience can be provided by providing input assistance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk can input user emotion data into a generative AI, which can then estimate the emotion and provide input assistance.

[0092] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a concise analysis can be performed. Furthermore, if the user is stressed, a visually easy-to-understand analysis can be performed. By adjusting the timing of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the timing of the analysis.

[0093] The reception department can analyze the user's past application history when they enter approval request forms and provide optimal input assistance. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). The reception department can also automatically select a specific format based on the user's past application history, simplifying the input process. Furthermore, the reception department can analyze the user's past application history and suggest the most efficient input method. In this way, it can provide optimal input assistance by analyzing the user's past application history. Some or all of the above processes in the reception department are performed using AI. For example, the reception department can input the user's past application data into a generating AI, which can then analyze the data and provide optimal input assistance.

[0094] The analysis unit can prioritize the analysis of approval requests by considering the user's current work situation. For example, it can prioritize the analysis of approval requests related to projects the user is currently working on. It can also prioritize the analysis of approval requests with high relevance, depending on the user's work situation. Furthermore, it can select the optimal analysis method considering the user's work situation. This allows for the provision of more appropriate analysis results by considering the user's current work situation. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input user work situation data into the generation AI, which can then analyze the data and determine the analysis priority.

[0095] The feedback unit can analyze the user's past application content to select the optimal feedback method during the feedback process. For example, it can propose the optimal feedback method based on the user's past application content. The feedback unit can also analyze the user's past application content and provide highly relevant feedback. Furthermore, the feedback unit can select the optimal feedback method by referring to the user's past application content. In this way, the optimal feedback method can be selected by analyzing the user's past application content. Some or all of the above processes in the feedback unit are performed using AI. For example, the feedback unit can input the user's past application content data into a generating AI, which can analyze the data and propose the optimal feedback method.

[0096] The reception desk can filter approval requests based on the user's current work situation and areas of interest when the request is entered. For example, it can analyze the user's current work situation and prioritize displaying highly relevant approval requests. It can also filter and display relevant approval requests based on the user's areas of interest. Furthermore, it can suggest the optimal method for entering the approval request, taking into account the user's work situation and areas of interest. This allows for the priority display of highly relevant approval requests by filtering based on the user's current work situation and areas of interest. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the user's work situation data and areas of interest data into a generating AI, which then analyzes the data and performs filtering.

[0097] The analysis unit can implement data encryption and privacy protection measures when analyzing the contents of approval request forms. For example, it can encrypt and store the data of the approval request form. It can also implement data access control and privacy protection measures. Furthermore, it can anonymize the data to protect personal information. In this way, security can be ensured by encrypting the data and implementing privacy protection measures. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit can input the data of the approval request form into the generation AI, which can then perform data encryption and anonymization.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The reception department inputs the approval request forms. These forms include expense requests, capital investment requests, and personnel change requests. The reception department can input the approval request forms as electronic forms, or scan paper forms to digitize them and input them into the system. It can also input voice input or handwritten input by converting it into digital data. For example, voice recognition technology can be used to convert voice input into text data, and OCR technology can be used to convert handwritten input into digital data. Step 2: The analysis unit uses a generation AI to analyze the approval request form entered by the reception unit and check for any problems with its content or areas that need improvement. The analysis unit analyzes each item in the approval request form to confirm that all necessary information is included. It also applies natural language processing technology using the generation AI to evaluate whether the content of the approval request form is appropriate. Furthermore, the analysis unit can also suggest areas for improvement based on the content of the approval request form. Step 3: The Feedback Department provides feedback to the applicant and approver on the results checked by the Analysis Department. The Feedback Department provides the analysis results to the applicant and approver via email or a notification system. The analysis results can also be displayed in dashboard or report format. For example, the analysis results can be displayed visually as graphs or charts.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the reception unit, analysis unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, which inputs the approval request form as an electronic form. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the approval request form using generated AI. The feedback unit is implemented by the output device 40 of the smart device 14, which provides the analysis results to the applicant and approver. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the reception unit, analysis unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which converts voice input into text data and inputs the approval request form. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the approval request form using generation AI. The feedback unit is implemented by the speaker 240 of the smart glasses 214, which provides the analysis results to the applicant and approver. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the reception unit, analysis unit, and feedback unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, which converts voice input into text data and inputs the approval request form. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the approval request form using a generation AI. The feedback unit is implemented by the display 343 of the headset terminal 314, which provides the analysis results to the applicant and approver. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, analysis unit, and feedback unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which converts voice input into text data and inputs the approval request form. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the approval request form using generated AI. The feedback unit is implemented by the speaker 240 of the robot 414, which provides the analysis results to the applicant and approver. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) The reception desk where the approval request form is entered, An analysis unit analyzes the approval request form entered by the aforementioned reception unit and checks whether there are any problems with the content or any points that need improvement. The system includes a feedback unit that provides feedback to the applicant and approver on the results checked by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The analysis is performed using prompts appropriate to the type of approval request. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Refer to past approval data and provide feedback on similar applications. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is We will provide you with the results of reviewing your application and suggestions for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, When analyzing the contents of approval requests, measures such as data encryption and privacy protection will be taken. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Provide prompt feedback to applicants and approvers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting approval requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter approval requests, the system analyzes their past application history and selects the most suitable input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering approval requests, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the approval request forms to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering approval requests, the system prioritizes inputting requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering approval requests, the system analyzes the user's social media activity and enters relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the level of detail of the analysis is adjusted based on the importance of the approval request document. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on the submission date of the approval request form. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the approval request documents. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is When providing feedback, the system analyzes the user's past applications to select the most suitable feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is When providing feedback, customize the feedback method based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is When providing feedback, the optimal feedback method is selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and suggest ways to provide feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where the approval request form is entered, An analysis unit analyzes the approval request form entered by the aforementioned reception unit and checks whether there are any problems with the content or any points that need improvement. The system includes a feedback unit that provides feedback to the applicant and approver on the results checked by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, The analysis is performed using prompts appropriate to the type of approval request. The system according to feature 1.

3. The aforementioned analysis unit, Refer to past approval data and provide feedback on similar applications. The system according to feature 1.

4. The aforementioned feedback unit is We will provide you with the results of reviewing your application and suggestions for improvement. The system according to feature 1.

5. The aforementioned analysis unit, When analyzing the contents of approval requests, measures such as data encryption and privacy protection will be taken. The system according to feature 1.

6. The aforementioned feedback unit is Provide prompt feedback to applicants and approvers. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting approval requests based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When users enter approval requests, the system analyzes their past application history and selects the most suitable input method. The system according to feature 1.

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