system

Abstract

The system according to this embodiment aims to streamline and expedite the process from application to approval. [Solution] The system according to the embodiment comprises a reception unit, a feedback unit, a modification unit, and a confirmation unit. The reception unit inputs the application details. The feedback unit analyzes the application details input by the reception unit and provides feedback on the necessary information. The modification unit modifies and supplements the application details based on the information provided by the feedback unit. The confirmation unit confirms the application details modified and supplemented by the modification unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process from application to approval takes a long time and there are many rallies due to deficiencies in the application content.

[0005] The system according to the embodiment aims to streamline the process from application to approval and proceed quickly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a feedback unit, a modification unit, and a confirmation unit. The reception unit inputs the application details. The feedback unit analyzes the application details input by the reception unit and provides feedback on the necessary information. The modification unit modifies and supplements the application details based on the information provided by the feedback unit. The confirmation unit confirms the application details modified and supplemented by the modification unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline and expedite the process from application to approval. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The governance flow efficiency system according to an embodiment of the present invention is a system that uses a generation AI to streamline the application process. In this governance flow efficiency system, the applicant inputs the application content, the generation AI feeds back the necessary information requested by the governance side to the applicant, and the applicant modifies and completes the application content, so that the governance side can quickly make a risk assessment by confirming that the application has all the necessary information. For example, if the applicant inputs content such as "I want to use the data for advertising," the generation AI analyzes that information and feeds back the necessary information. The applicant modifies and completes the application content based on the feedback from the generation AI, and the governance side can quickly make a risk assessment by confirming the modified and completed application content. This mechanism shortens the time from application to approval and accelerates the speed of the application flow. The applicant can quickly submit the application requested by the governance side by modifying the application content while receiving feedback from the generation AI. In addition, the governance side can quickly make a risk assessment by confirming that the application has all the necessary information. As a result, the efficiency of the application flow is improved, and it is expected that planning and data utilization will proceed as scheduled. In this way, the governance flow efficiency system can streamline the application process and accelerate the speed of the application flow.

[0029] The governance flow efficiency system according to this embodiment comprises a reception unit, a feedback unit, a correction unit, and a confirmation unit. The reception unit receives application details from the applicant. Application details include, but are not limited to, document applications, online applications, and patent applications. The reception unit can receive application details using, for example, an online form. The reception unit can also receive input by converting voice input or handwritten input into digital data. The feedback unit uses a generation AI to analyze the application details entered by the reception unit and provides feedback on necessary information. The feedback unit can, for example, identify missing information in the application details and provide specific correction instructions to the applicant. For example, the feedback unit may give instructions such as, "Please include the function name," "Please include the service name," "Please describe the summary of the consultation matter," "Please include the name of the target service or media," or "Please include the name of the data you wish to confirm." The correction unit corrects and supplements the application details based on the information provided by the feedback unit. The correction unit can, for example, provide an interface for the applicant to correct the application details based on the feedback. The correction unit can add to the application details and correct errors. The verification unit confirms the application content that has been corrected and supplemented by the correction unit. For example, the verification unit checks the accuracy and format of the application content and whether all necessary information is included. The verification unit can also quickly identify risk areas by confirming the data names and service names listed in the application content. As a result, the governance flow efficiency system according to this embodiment can streamline the process from application content input to confirmation and accelerate the speed of the application flow.

[0030] The reception desk receives the application details from the applicant. These details include, but are not limited to, paper applications, online applications, and patent applications. The reception desk can, for example, receive applications using online forms. These online forms feature a user-friendly interface designed to allow applicants to easily enter the necessary information. The forms include input assistance features such as dropdown menus, checkboxes, and radio buttons to minimize the risk of applicants entering incorrect information. The reception desk can also accept applications via voice input or handwritten input, converting it into digital data. For voice input, speech recognition technology is used to convert the applicant's voice into text data; for handwritten input, optical character recognition (OCR) technology is used to convert handwritten characters into digital data. This allows applicants to enter their application details in the way that best suits them, significantly reducing the effort required for input. Furthermore, the reception desk has a function to verify the entered data in real time and immediately notify applicants of input errors or incomplete information. For example, if required fields are missing or the input format is incorrect, an error message is displayed prompting the applicant to correct it. This allows the reception department to ensure the accuracy and completeness of the application and to facilitate the subsequent process.

[0031] The Feedback Department uses generative AI to analyze the application content entered by the Reception Department and provide necessary feedback. The generative AI utilizes natural language processing (NLP) technology to analyze the application content in detail and identify any missing or incomplete information provided by the applicant. For example, the generative AI can understand the context of the application content and detect when specific keywords or phrases are missing. The Feedback Department identifies the missing information in the application content and provides specific correction instructions to the applicant. Specifically, it may give instructions such as, "Please include the function name," "Please include the service name," "Please describe the summary of the consultation matter," "Please include the name of the target service or medium," or "Please include the name of the data you wish to confirm." These instructions are automatically generated by the generative AI and provided to the applicant in real time. Furthermore, the Feedback Department can also provide specific examples and templates to help applicants understand the correction instructions. For example, if an applicant has difficulty describing the "function name," the Feedback Department can provide specific examples such as "Example: User authentication function, Data analysis function." This allows applicants to quickly and accurately complete the necessary information. The feedback department plays a role not only in improving the quality of application content, but also in reducing the burden on applicants and increasing the efficiency of the entire application process.

[0032] The revision department modifies and supplements the application content based on the information provided by the feedback department. For example, the revision department provides an interface for applicants to modify their application content based on the feedback. This interface is user-friendly and intuitively designed to allow applicants to easily perform revision work. The revision department can add to the application content and correct errors. For example, if an applicant receives instructions from the feedback department to "please include the function name," the revision department provides a dedicated field for the applicant to enter the function name and validates the input in real time. The revision department also has a function that allows applicants to refer to past application content and related documents while performing revision work. This allows applicants to quickly find the necessary information and make accurate corrections. Furthermore, the revision department can automate the process of sending the data back to the feedback department for re-evaluation after the applicant has completed the revision work. This allows the revision department to ensure the quality of the application content and improve the efficiency of the entire application process.

[0033] The verification department reviews the application content that has been corrected and supplemented by the revision department. For example, the verification department checks the accuracy and format of the application content and whether all necessary information is included. The verification department can quickly identify risk areas by checking the data names and service names listed in the application content. Specifically, the verification department uses a checklist to confirm whether the application content follows the prescribed format and whether all necessary information is included. The verification department also verifies the consistency and coherence of the application content and checks for inconsistencies and errors. For example, it checks whether the data names listed in the application content match those in other parts and whether the service names are accurately listed. Furthermore, the verification department can perform a risk assessment of the application content using algorithms to quickly identify risk areas. This allows the verification department to ensure the quality of the application content and accelerate the speed of the application flow. After the final verification is complete, the verification department can also automate the process of approving or rejecting the application content. This allows the verification department to improve the efficiency of the entire application process and provide rapid feedback to applicants.

[0034] The feedback unit can use a generation AI to identify missing information in the application and provide specific correction instructions to the applicant. For example, the feedback unit can identify missing information in the application and provide specific correction instructions to the applicant. For example, the feedback unit may give instructions such as, "Please include the function name," "Please include the service name," "Please describe the summary of the consultation matter," "Please include the name of the target service or media," or "Please include the name of the data you wish to confirm." By identifying missing information in the application and providing specific correction instructions, the accuracy of the application can be improved. Some or all of the above processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the application content into the generation AI, which identifies the missing information and outputs specific correction instructions.

[0035] The correction unit can modify and supplement the application content based on feedback from the generating AI. For example, the correction unit modifies and supplements the application content based on feedback from the generating AI. For example, the correction unit provides an interface for the applicant to modify the application content based on the feedback. The correction unit can add to the application content and correct errors. This improves the accuracy of the application content by modifying and supplementing it based on feedback from the generating AI. Some or all of the above processing in the correction unit is performed using the generating AI. For example, the correction unit takes feedback from the generating AI as input and causes the generating AI to modify and supplement the application content.

[0036] The verification unit can verify the application content that has been corrected and supplemented by the generation AI. For example, the verification unit verifies the application content that has been corrected and supplemented by the generation AI. For example, the verification unit checks the accuracy and format of the application content and whether all necessary information is included. The verification unit can verify the data names and service names described in the application content and quickly identify risk areas. In this way, the accuracy of the application content can be improved by verifying the application content that has been corrected and supplemented by the generation AI. Some or all of the above processing in the verification unit is performed using the generation AI. For example, the verification unit takes the application content that has been corrected and supplemented by the generation AI as input and has the generation AI perform the verification.

[0037] The feedback unit can provide more accurate feedback by utilizing past application data. For example, the feedback unit analyzes past application data to identify missing information in the application and provides specific correction instructions to the applicant. This improves the accuracy of feedback by utilizing past application data. Some or all of the above processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs past application data into the generation AI, and the generation AI outputs feedback based on the past data.

[0038] The feedback unit can identify risky aspects of the application and provide specific instructions for revision to the applicant. For example, the feedback unit can identify legal and technical risks and provide specific instructions for revision to the applicant. This allows for rapid risk assessment by identifying risky aspects of the application and providing specific instructions for revision. Some or all of the above processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the application content into the generation AI, which identifies risky aspects and outputs specific instructions for revision.

[0039] The reception desk can analyze the applicant's past application history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the application content that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest application content to be used at specific times based on the user's past application history. In this way, by analyzing past application history, the reception desk can suggest the optimal input method for the applicant. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input past application history into AI, and the AI ​​can suggest the optimal input method.

[0040] The reception system can customize input fields based on the applicant's current projects and areas of interest when they enter their application details. For example, the reception system will prioritize displaying input fields related to the applicant's current projects. It can also automatically suggest relevant input fields based on the applicant's areas of interest. Furthermore, the reception system can customize input fields based on areas the applicant has previously shown interest in. This improves input efficiency by customizing input fields based on the applicant's current projects and areas of interest. Some or all of the above processes in the reception system may be performed using AI or not. For example, the reception system can input the applicant's project data into the AI, which then suggests the most suitable input fields.

[0041] The reception desk can prioritize displaying relevant input fields when an applicant enters application details, taking into account the applicant's geographical location. For example, if the applicant is in a specific region, the reception desk will prioritize displaying input fields related to that region. The reception desk can also automatically suggest relevant input fields based on the applicant's current location. Furthermore, the reception desk can customize input fields based on places the applicant has visited in the past. This allows for the priority display of relevant input fields by considering the applicant's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the applicant's geographical location information into the AI, and the AI ​​suggests relevant input fields.

[0042] The reception desk can analyze the applicant's social media activity when they enter their application details and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on the applicant's social media activity. It can also customize input fields based on information the applicant has shared on social media. Furthermore, the reception desk can automatically suggest input fields based on the applicant's social media interests. This allows the reception desk to suggest relevant input fields by analyzing the applicant's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the applicant's social media data into an AI, which then suggests relevant input fields.

[0043] The feedback unit can adjust the level of detail in the feedback based on the importance of the application. For example, the feedback unit can provide detailed feedback for important applications. It can also provide concise feedback for general applications. Furthermore, it can provide rapid and detailed feedback for urgent applications. This allows for the provision of appropriate feedback by adjusting the level of detail based on the importance of the application. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit inputs application importance data into the AI, and the AI ​​adjusts the level of detail in the feedback.

[0044] The feedback unit can apply different feedback algorithms depending on the category of the application content when providing feedback. For example, the feedback unit can apply a specialized technical feedback algorithm to technical applications. It can also apply a specialized business feedback algorithm to business-related applications. Furthermore, it can apply a specialized legal feedback algorithm to legal applications. This allows for the provision of appropriate feedback by applying different feedback algorithms depending on the category of the application content. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit inputs the category data of the application content into the AI, and the AI ​​applies the appropriate feedback algorithm.

[0045] The feedback unit can prioritize feedback based on the submission date of the application. For example, it can prioritize urgent applications. It can also provide prompt feedback to applications with approaching deadlines. Furthermore, it can provide regular feedback to applications with ample time before submission. By prioritizing feedback based on the submission date of the application, feedback can be provided at the appropriate time. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit inputs application submission date data into the AI, and the AI ​​determines the feedback priority.

[0046] The feedback unit can adjust the order of feedback based on the relevance of the application content. For example, if an application is related to other applications, the feedback unit will provide feedback in order of relevance. The feedback unit can also provide feedback individually if the application content is independent. Furthermore, if an application spans multiple categories, the feedback unit can provide feedback for each category. This allows the feedback to be provided in an appropriate order by adjusting the order of feedback based on the relevance of the application content. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit inputs relevance data of the application content into the AI, and the AI ​​adjusts the order of feedback.

[0047] The revision unit can select the optimal revision method by referring to the past revision history of the application content when making revisions. For example, the revision unit can propose the optimal revision method based on the history of similar application content revisions in the past. The revision unit can also identify parts that frequently require revision from the past revision history and propose revisions in advance. Furthermore, the revision unit can analyze the past revision history and select the most efficient revision method. In this way, the optimal revision method can be selected by referring to the past revision history. Some or all of the above processes in the revision unit may be performed using AI or not. For example, the revision unit inputs past revision history data into the AI, and the AI ​​selects the optimal revision method.

[0048] The revision unit can customize the means of revision based on the current status of the application content when revisions are made. For example, if the application content relates to an ongoing project, the revision unit can propose a revision method appropriate to that status. Furthermore, if the application content relates to a new project, the revision unit can propose a revision method suitable for the initial stages. In addition, if the application content relates to a project nearing completion, the revision unit can propose a revision method suitable for final confirmation. This allows for the provision of appropriate revision methods by customizing the means of revision based on the current status of the application content. Some or all of the above-described processes in the revision unit may be performed using AI or not. For example, the revision unit inputs current status data of the application content into the AI, and the AI ​​proposes the optimal revision method.

[0049] The revision unit can select the optimal revision method when making revisions, taking into account the geographical distribution of the application content. For example, if the application content relates to a specific region, the revision unit can propose a revision method that is appropriate for the characteristics of that region. Furthermore, if the application content relates to multiple regions, the revision unit can propose a revision method suitable for each region. In addition, if the application content relates to an international project, the revision unit can propose a revision method from an international perspective. This allows for the selection of the optimal revision method by considering the geographical distribution of the application content. Some or all of the above processing in the revision unit may be performed using AI, or not. For example, the revision unit inputs geographical distribution data of the application content into the AI, and the AI ​​selects the optimal revision method.

[0050] The revision unit can improve the accuracy of revisions by referring to relevant literature related to the application content during the revision process. For example, the revision unit can automatically refer to literature related to the application content and use it as a reference for revisions. Furthermore, if the application content relates to a specific field, the revision unit can also make revisions by referring to the latest literature in that field. In addition, if the application content relates to multiple fields, the revision unit can make revisions by referring to literature in each field. This improves the accuracy of revisions by referring to relevant literature related to the application content. Some or all of the above processes in the revision unit may be performed using AI or not. For example, the revision unit can input the relevant literature data for the application content into the AI, and the AI ​​can improve the accuracy of the revisions.

[0051] The verification unit can select the optimal verification method by referring to the past verification history of the application content during the verification process. For example, the verification unit can propose the optimal verification method based on the history of similar application content being verified in the past. The verification unit can also identify parts that frequently require verification from the past verification history and propose verification in advance. Furthermore, the verification unit can analyze the past verification history and select the most efficient verification method. In this way, the optimal verification method can be selected by referring to the past verification history. Some or all of the above processes in the verification unit may be performed using AI or not. For example, the verification unit inputs past verification history data into the AI, and the AI ​​selects the optimal verification method.

[0052] The verification unit can customize the verification process based on the current status of the application during the verification process. For example, if the application relates to an ongoing project, the verification unit can propose a verification method appropriate to that situation. Furthermore, if the application relates to a new project, the verification unit can propose a verification method suitable for the initial stages. Additionally, if the application relates to a project nearing completion, the verification unit can propose a verification method suitable for final verification. This allows for the provision of an appropriate verification method by customizing the verification process based on the current status of the application. Some or all of the above-described processes in the verification unit may be performed using AI or not. For example, the verification unit inputs current status data of the application into the AI, which then proposes the optimal verification method.

[0053] The verification unit can select the optimal verification method during the verification process, taking into account the geographical distribution of the application content. For example, if the application content relates to a specific region, the verification unit can propose a verification method appropriate to the characteristics of that region. Furthermore, if the application content relates to multiple regions, the verification unit can propose a verification method suitable for each region. In addition, if the application content relates to an international project, the verification unit can propose a verification method from an international perspective. This allows for the selection of the optimal verification method by considering the geographical distribution of the application content. Some or all of the above-described processes in the verification unit may be performed using AI, or they may not. For example, the verification unit inputs geographical distribution data of the application content into the AI, and the AI ​​selects the optimal verification method.

[0054] The verification unit can improve the accuracy of its verification by referring to relevant literature related to the application content during the verification process. For example, the verification unit can automatically refer to literature related to the application content and use it as a reference for verification. Furthermore, if the application content relates to a specific field, the verification unit can also perform verification by referring to the latest literature in that field. In addition, if the application content relates to multiple fields, the verification unit can perform verification by referring to literature in each field. This improves the accuracy of verification by referring to relevant literature related to the application content. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input the relevant literature data for the application content into the AI, and the AI ​​can improve the accuracy of the verification.

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

[0056] The reception desk can analyze the applicant's input in real time and immediately point out input errors or deficiencies. For example, if an applicant enters a date in the wrong format, the reception desk will immediately provide the correct format. Also, if an applicant attempts to proceed without entering required fields, the reception desk can display a warning and prompt them to enter the information. Furthermore, if an applicant enters information that contradicts information they have previously entered, the reception desk can point out the inconsistency and prompt them to correct it. This allows applicants to reduce input errors and proceed with their applications smoothly.

[0057] The feedback system can refer to the applicant's past feedback history and provide individually customized feedback. For example, if an applicant has received specific feedback in the past, it will take that into consideration when providing new feedback. The feedback system can also learn specific patterns from the applicant's past feedback history and provide more effective feedback. Furthermore, based on the feedback the applicant has received in the past, the feedback system can proactively point out potential problems and encourage corrections. This allows applicants to receive more accurate feedback and improve the precision of their applications.

[0058] The revision unit can analyze the applicant's input and automatically suggest similar application content. For example, if the applicant enters a specific keyword, it will suggest past application content containing similar keywords. The revision unit can also automatically complete related application content based on the applicant's input. Furthermore, the revision unit can refer to the revision history of similar past application content and suggest the most suitable revision method for the content entered by the applicant. This allows applicants to efficiently revise and complete their application content.

[0059] The verification unit can select the most suitable verification method by referring to the applicant's past verification history when verifying application content. For example, it can propose the most suitable verification method based on the history of similar applications being verified in the past. Furthermore, the verification unit can identify frequently required verification sections from past verification history and propose verification in advance. In addition, the verification unit can analyze past verification history and select the most efficient verification method. Thus, by referring to past verification history, the optimal verification method can be selected.

[0060] The feedback unit can identify risky aspects of the application and provide specific correction instructions to the applicant. For example, it can identify legal and technical risks and provide specific correction instructions to the applicant. This allows for rapid risk assessment by identifying risky aspects of the application and providing specific correction instructions. Some or all of the above processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the application content into the generation AI, which identifies risky aspects and outputs specific correction instructions.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives the application details from the applicant. These details include paper applications, online applications, and patent applications. The reception desk can input online forms, voice input, and handwritten input, converting them into digital data. Step 2: The Feedback Department uses a generation AI to analyze the application content entered by the Reception Department and provides feedback on the necessary information. The Feedback Department identifies the missing information in the application and gives specific correction instructions to the applicant. For example, it may give instructions such as, "Please include the function name," "Please include the service name," "Please describe the summary of the consultation matter," "Please include the name of the target service or media," or "Please include the name of the data you would like to confirm." Step 3: The revision section modifies and supplements the application based on the information provided by the feedback section. The revision section provides an interface for applicants to modify their application based on the feedback, allowing them to add information or correct errors. Step 4: The verification team reviews the application content that has been corrected and supplemented by the revision team. The verification team checks the accuracy and format of the application content and verifies that all necessary information is included. They check the data names and service names listed in the application content and quickly identify any risk areas.

[0063] (Example of form 2) The governance flow efficiency system according to an embodiment of the present invention is a system that uses a generation AI to streamline the application process. In this governance flow efficiency system, the applicant inputs the application content, the generation AI feeds back the necessary information requested by the governance side to the applicant, and the applicant modifies and completes the application content, so that the governance side can quickly make a risk assessment by confirming that the application has all the necessary information. For example, if the applicant inputs content such as "I want to use the data for advertising," the generation AI analyzes that information and feeds back the necessary information. The applicant modifies and completes the application content based on the feedback from the generation AI, and the governance side can quickly make a risk assessment by confirming the modified and completed application content. This mechanism shortens the time from application to approval and accelerates the speed of the application flow. The applicant can quickly submit the application requested by the governance side by modifying the application content while receiving feedback from the generation AI. In addition, the governance side can quickly make a risk assessment by confirming that the application has all the necessary information. As a result, the efficiency of the application flow is improved, and it is expected that planning and data utilization will proceed as scheduled. In this way, the governance flow efficiency system can streamline the application process and accelerate the speed of the application flow.

[0064] The governance flow efficiency system according to this embodiment comprises a reception unit, a feedback unit, a correction unit, and a confirmation unit. The reception unit receives application details from the applicant. Application details include, but are not limited to, document applications, online applications, and patent applications. The reception unit can receive application details using, for example, an online form. The reception unit can also receive input by converting voice input or handwritten input into digital data. The feedback unit uses a generation AI to analyze the application details entered by the reception unit and provides feedback on necessary information. The feedback unit can, for example, identify missing information in the application details and provide specific correction instructions to the applicant. For example, the feedback unit may give instructions such as, "Please include the function name," "Please include the service name," "Please describe the summary of the consultation matter," "Please include the name of the target service or media," or "Please include the name of the data you wish to confirm." The correction unit corrects and supplements the application details based on the information provided by the feedback unit. The correction unit can, for example, provide an interface for the applicant to correct the application details based on the feedback. The correction unit can add to the application details and correct errors. The verification unit confirms the application content that has been corrected and supplemented by the correction unit. For example, the verification unit checks the accuracy and format of the application content and whether all necessary information is included. The verification unit can also quickly identify risk areas by confirming the data names and service names listed in the application content. As a result, the governance flow efficiency system according to this embodiment can streamline the process from application content input to confirmation and accelerate the speed of the application flow.

[0065] The reception desk receives the application details from the applicant. These details include, but are not limited to, paper applications, online applications, and patent applications. The reception desk can, for example, receive applications using online forms. These online forms feature a user-friendly interface designed to allow applicants to easily enter the necessary information. The forms include input assistance features such as dropdown menus, checkboxes, and radio buttons to minimize the risk of applicants entering incorrect information. The reception desk can also accept applications via voice input or handwritten input, converting it into digital data. For voice input, speech recognition technology is used to convert the applicant's voice into text data; for handwritten input, optical character recognition (OCR) technology is used to convert handwritten characters into digital data. This allows applicants to enter their application details in the way that best suits them, significantly reducing the effort required for input. Furthermore, the reception desk has a function to verify the entered data in real time and immediately notify applicants of input errors or incomplete information. For example, if required fields are missing or the input format is incorrect, an error message is displayed prompting the applicant to correct it. This allows the reception department to ensure the accuracy and completeness of the application and to facilitate the subsequent process.

[0066] The Feedback Department uses generative AI to analyze the application content entered by the Reception Department and provide necessary feedback. The generative AI utilizes natural language processing (NLP) technology to analyze the application content in detail and identify any missing or incomplete information provided by the applicant. For example, the generative AI can understand the context of the application content and detect when specific keywords or phrases are missing. The Feedback Department identifies the missing information in the application content and provides specific correction instructions to the applicant. Specifically, it may give instructions such as, "Please include the function name," "Please include the service name," "Please describe the summary of the consultation matter," "Please include the name of the target service or medium," or "Please include the name of the data you wish to confirm." These instructions are automatically generated by the generative AI and provided to the applicant in real time. Furthermore, the Feedback Department can also provide specific examples and templates to help applicants understand the correction instructions. For example, if an applicant has difficulty describing the "function name," the Feedback Department can provide specific examples such as "Example: User authentication function, Data analysis function." This allows applicants to quickly and accurately complete the necessary information. The feedback department plays a role not only in improving the quality of application content, but also in reducing the burden on applicants and increasing the efficiency of the entire application process.

[0067] The revision department modifies and supplements the application content based on the information provided by the feedback department. For example, the revision department provides an interface for applicants to modify their application content based on the feedback. This interface is user-friendly and intuitively designed to allow applicants to easily perform revision work. The revision department can add to the application content and correct errors. For example, if an applicant receives instructions from the feedback department to "please include the function name," the revision department provides a dedicated field for the applicant to enter the function name and validates the input in real time. The revision department also has a function that allows applicants to refer to past application content and related documents while performing revision work. This allows applicants to quickly find the necessary information and make accurate corrections. Furthermore, the revision department can automate the process of sending the data back to the feedback department for re-evaluation after the applicant has completed the revision work. This allows the revision department to ensure the quality of the application content and improve the efficiency of the entire application process.

[0068] The verification department reviews the application content that has been corrected and supplemented by the revision department. For example, the verification department checks the accuracy and format of the application content and whether all necessary information is included. The verification department can quickly identify risk areas by checking the data names and service names listed in the application content. Specifically, the verification department uses a checklist to confirm whether the application content follows the prescribed format and whether all necessary information is included. The verification department also verifies the consistency and coherence of the application content and checks for inconsistencies and errors. For example, it checks whether the data names listed in the application content match those in other parts and whether the service names are accurately listed. Furthermore, the verification department can perform a risk assessment of the application content using algorithms to quickly identify risk areas. This allows the verification department to ensure the quality of the application content and accelerate the speed of the application flow. After the final verification is complete, the verification department can also automate the process of approving or rejecting the application content. This allows the verification department to improve the efficiency of the entire application process and provide rapid feedback to applicants.

[0069] The feedback unit can use a generation AI to identify missing information in the application and provide specific correction instructions to the applicant. For example, the feedback unit can identify missing information in the application and provide specific correction instructions to the applicant. For example, the feedback unit may give instructions such as, "Please include the function name," "Please include the service name," "Please describe the summary of the consultation matter," "Please include the name of the target service or media," or "Please include the name of the data you wish to confirm." By identifying missing information in the application and providing specific correction instructions, the accuracy of the application can be improved. Some or all of the above processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the application content into the generation AI, which identifies the missing information and outputs specific correction instructions.

[0070] The correction unit can modify and supplement the application content based on feedback from the generating AI. For example, the correction unit modifies and supplements the application content based on feedback from the generating AI. For example, the correction unit provides an interface for the applicant to modify the application content based on the feedback. The correction unit can add to the application content and correct errors. This improves the accuracy of the application content by modifying and supplementing it based on feedback from the generating AI. Some or all of the above processing in the correction unit is performed using the generating AI. For example, the correction unit takes feedback from the generating AI as input and causes the generating AI to modify and supplement the application content.

[0071] The verification unit can verify the application content that has been corrected and supplemented by the generation AI. For example, the verification unit verifies the application content that has been corrected and supplemented by the generation AI. For example, the verification unit checks the accuracy and format of the application content and whether all necessary information is included. The verification unit can verify the data names and service names described in the application content and quickly identify risk areas. In this way, the accuracy of the application content can be improved by verifying the application content that has been corrected and supplemented by the generation AI. Some or all of the above processing in the verification unit is performed using the generation AI. For example, the verification unit takes the application content that has been corrected and supplemented by the generation AI as input and has the generation AI perform the verification.

[0072] The feedback unit can provide more accurate feedback by utilizing past application data. For example, the feedback unit analyzes past application data to identify missing information in the application and provides specific correction instructions to the applicant. This improves the accuracy of feedback by utilizing past application data. Some or all of the above processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs past application data into the generation AI, and the generation AI outputs feedback based on the past data.

[0073] The feedback unit can identify risky aspects of the application and provide specific instructions for revision to the applicant. For example, the feedback unit can identify legal and technical risks and provide specific instructions for revision to the applicant. This allows for rapid risk assessment by identifying risky aspects of the application and providing specific instructions for revision. Some or all of the above processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the application content into the generation AI, which identifies risky aspects and outputs specific instructions for revision.

[0074] The reception desk can estimate the user's emotions and adjust the application input interface based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick application entry. This ensures that application entry is smooth by adjusting 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 generative AI. For example, the reception desk inputs the user's emotion data into the generative AI, which estimates the emotions and adjusts the input interface.

[0075] The reception desk can analyze the applicant's past application history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the application content that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest application content to be used at specific times based on the user's past application history. In this way, by analyzing past application history, the reception desk can suggest the optimal input method for the applicant. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input past application history into AI, and the AI ​​can suggest the optimal input method.

[0076] The reception system can customize input fields based on the applicant's current projects and areas of interest when they enter their application details. For example, the reception system will prioritize displaying input fields related to the applicant's current projects. It can also automatically suggest relevant input fields based on the applicant's areas of interest. Furthermore, the reception system can customize input fields based on areas the applicant has previously shown interest in. This improves input efficiency by customizing input fields based on the applicant's current projects and areas of interest. Some or all of the above processes in the reception system may be performed using AI or not. For example, the reception system can input the applicant's project data into the AI, which then suggests the most suitable input fields.

[0077] The reception desk can estimate the user's emotions and prioritize input content based on those emotions. For example, if the user is nervous, the reception desk can prioritize displaying important input fields and postpone other fields. If the user is relaxed, the reception desk can also sequentially display detailed input fields. Furthermore, if the user is in a hurry, the reception desk can display only the most important input fields, allowing for quick completion. This allows important information to be entered preferentially by prioritizing input content 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 reception desk is performed using generative AI. For example, the reception desk inputs user emotion data into the generative AI, which estimates the emotions and determines the priority of input content.

[0078] The reception desk can prioritize displaying relevant input fields when an applicant enters application details, taking into account the applicant's geographical location. For example, if the applicant is in a specific region, the reception desk will prioritize displaying input fields related to that region. The reception desk can also automatically suggest relevant input fields based on the applicant's current location. Furthermore, the reception desk can customize input fields based on places the applicant has visited in the past. This allows for the priority display of relevant input fields by considering the applicant's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the applicant's geographical location information into the AI, and the AI ​​suggests relevant input fields.

[0079] The reception desk can analyze the applicant's social media activity when they enter their application details and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on the applicant's social media activity. It can also customize input fields based on information the applicant has shared on social media. Furthermore, the reception desk can automatically suggest input fields based on the applicant's social media interests. This allows the reception desk to suggest relevant input fields by analyzing the applicant's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the applicant's social media data into an AI, which then suggests relevant input fields.

[0080] The feedback unit can estimate the user's emotions and adjust the way the feedback is presented based on the estimated emotions. For example, if the user is nervous, the feedback unit provides simple and easy-to-understand feedback. If the user is relaxed, the feedback unit can also provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can provide concise and rapid feedback. This improves the acceptability of the feedback by adjusting the way it is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 generative AI. For example, the feedback unit inputs the user's emotion data into the generative AI, which estimates the emotions and adjusts the way the feedback is presented.

[0081] The feedback unit can adjust the level of detail in the feedback based on the importance of the application. For example, the feedback unit can provide detailed feedback for important applications. It can also provide concise feedback for general applications. Furthermore, it can provide rapid and detailed feedback for urgent applications. This allows for the provision of appropriate feedback by adjusting the level of detail based on the importance of the application. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit inputs application importance data into the AI, and the AI ​​adjusts the level of detail in the feedback.

[0082] The feedback unit can apply different feedback algorithms depending on the category of the application content when providing feedback. For example, the feedback unit can apply a specialized technical feedback algorithm to technical applications. It can also apply a specialized business feedback algorithm to business-related applications. Furthermore, it can apply a specialized legal feedback algorithm to legal applications. This allows for the provision of appropriate feedback by applying different feedback algorithms depending on the category of the application content. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit inputs the category data of the application content into the AI, and the AI ​​applies the appropriate feedback algorithm.

[0083] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit can provide short, concise feedback. If the user is relaxed, the feedback unit can provide longer feedback with more detailed explanations. Furthermore, if the user is in a hurry, the feedback unit can provide quick and concise feedback. This allows for the provision of appropriate feedback by adjusting the length of the 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 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 generative AI. For example, the feedback unit inputs user emotion data into the generative AI, which estimates the emotions and adjusts the length of the feedback.

[0084] The feedback unit can prioritize feedback based on the submission date of the application. For example, it can prioritize urgent applications. It can also provide prompt feedback to applications with approaching deadlines. Furthermore, it can provide regular feedback to applications with ample time before submission. By prioritizing feedback based on the submission date of the application, feedback can be provided at the appropriate time. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit inputs application submission date data into the AI, and the AI ​​determines the feedback priority.

[0085] The feedback unit can adjust the order of feedback based on the relevance of the application content. For example, if an application is related to other applications, the feedback unit will provide feedback in order of relevance. The feedback unit can also provide feedback individually if the application content is independent. Furthermore, if an application spans multiple categories, the feedback unit can provide feedback for each category. This allows the feedback to be provided in an appropriate order by adjusting the order of feedback based on the relevance of the application content. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit inputs relevance data of the application content into the AI, and the AI ​​adjusts the order of feedback.

[0086] The editing unit can estimate the user's emotions and adjust the editing method based on the estimated emotions. For example, if the user is nervous, the editing unit can provide a simple and easy-to-understand editing method. If the user is relaxed, the editing unit can also provide detailed editing options. Furthermore, if the user is in a hurry, the editing unit can provide a method that allows for quick editing completion. In this way, by adjusting the editing method according to the user's emotions, an appropriate editing method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit is performed using generative AI. For example, the editing unit inputs the user's emotion data into the generative AI, which estimates the emotion and adjusts the editing method.

[0087] The revision unit can select the optimal revision method by referring to the past revision history of the application content when making revisions. For example, the revision unit can propose the optimal revision method based on the history of similar application content revisions in the past. The revision unit can also identify parts that frequently require revision from the past revision history and propose revisions in advance. Furthermore, the revision unit can analyze the past revision history and select the most efficient revision method. In this way, the optimal revision method can be selected by referring to the past revision history. Some or all of the above processes in the revision unit may be performed using AI or not. For example, the revision unit inputs past revision history data into the AI, and the AI ​​selects the optimal revision method.

[0088] The revision unit can customize the means of revision based on the current status of the application content when revisions are made. For example, if the application content relates to an ongoing project, the revision unit can propose a revision method appropriate to that status. Furthermore, if the application content relates to a new project, the revision unit can propose a revision method suitable for the initial stages. In addition, if the application content relates to a project nearing completion, the revision unit can propose a revision method suitable for final confirmation. This allows for the provision of appropriate revision methods by customizing the means of revision based on the current status of the application content. Some or all of the above-described processes in the revision unit may be performed using AI or not. For example, the revision unit inputs current status data of the application content into the AI, and the AI ​​proposes the optimal revision method.

[0089] The editing unit can estimate the user's emotions and determine the priority of corrections based on the estimated emotions. For example, if the user is stressed, the editing unit will prioritize important corrections. If the user is relaxed, the editing unit can also sequentially make detailed corrections. Furthermore, if the user is in a hurry, the editing unit can prioritize only the most important corrections. In this way, important corrections can be prioritized by determining the priority of corrections according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit is performed using generative AI. For example, the editing unit inputs user emotion data into the generative AI, which estimates the emotions and determines the priority of corrections.

[0090] The revision unit can select the optimal revision method when making revisions, taking into account the geographical distribution of the application content. For example, if the application content relates to a specific region, the revision unit can propose a revision method that is appropriate for the characteristics of that region. Furthermore, if the application content relates to multiple regions, the revision unit can propose a revision method suitable for each region. In addition, if the application content relates to an international project, the revision unit can propose a revision method from an international perspective. This allows for the selection of the optimal revision method by considering the geographical distribution of the application content. Some or all of the above processing in the revision unit may be performed using AI, or not. For example, the revision unit inputs geographical distribution data of the application content into the AI, and the AI ​​selects the optimal revision method.

[0091] The revision unit can improve the accuracy of revisions by referring to relevant literature related to the application content during the revision process. For example, the revision unit can automatically refer to literature related to the application content and use it as a reference for revisions. Furthermore, if the application content relates to a specific field, the revision unit can also make revisions by referring to the latest literature in that field. In addition, if the application content relates to multiple fields, the revision unit can make revisions by referring to literature in each field. This improves the accuracy of revisions by referring to relevant literature related to the application content. Some or all of the above processes in the revision unit may be performed using AI or not. For example, the revision unit can input the relevant literature data for the application content into the AI, and the AI ​​can improve the accuracy of the revisions.

[0092] The confirmation unit can estimate the user's emotions and adjust the display method of the confirmation based on the estimated emotions. For example, if the user is nervous, the confirmation unit can provide a simple and highly visible display method. If the user is relaxed, the confirmation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the confirmation unit can provide a concise display method. By adjusting the display method of the confirmation according to the user's emotions, the visibility of the confirmation is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the confirmation unit is performed using generative AI. For example, the confirmation unit inputs the user's emotion data into the generative AI, the generative AI estimates the emotion, and adjusts the display method of the confirmation.

[0093] The verification unit can select the optimal verification method by referring to the past verification history of the application content during the verification process. For example, the verification unit can propose the optimal verification method based on the history of similar application content being verified in the past. The verification unit can also identify parts that frequently require verification from the past verification history and propose verification in advance. Furthermore, the verification unit can analyze the past verification history and select the most efficient verification method. In this way, the optimal verification method can be selected by referring to the past verification history. Some or all of the above processes in the verification unit may be performed using AI or not. For example, the verification unit inputs past verification history data into the AI, and the AI ​​selects the optimal verification method.

[0094] The verification unit can customize the verification process based on the current status of the application during the verification process. For example, if the application relates to an ongoing project, the verification unit can propose a verification method appropriate to that situation. Furthermore, if the application relates to a new project, the verification unit can propose a verification method suitable for the initial stages. Additionally, if the application relates to a project nearing completion, the verification unit can propose a verification method suitable for final verification. This allows for the provision of an appropriate verification method by customizing the verification process based on the current status of the application. Some or all of the above-described processes in the verification unit may be performed using AI or not. For example, the verification unit inputs current status data of the application into the AI, which then proposes the optimal verification method.

[0095] The verification unit can estimate the user's emotions and determine the priority of verifications based on the estimated emotions. For example, if the user is nervous, the verification unit will prioritize important verifications. If the user is relaxed, the verification unit can also perform detailed verifications sequentially. Furthermore, if the user is in a hurry, the verification unit can prioritize only the most important verifications. In this way, important verifications can be prioritized by determining the priority of verifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit is performed using generative AI. For example, the verification unit inputs the user's emotion data into the generative AI, which estimates the emotions and determines the priority of verifications.

[0096] The verification unit can select the optimal verification method during the verification process, taking into account the geographical distribution of the application content. For example, if the application content relates to a specific region, the verification unit can propose a verification method appropriate to the characteristics of that region. Furthermore, if the application content relates to multiple regions, the verification unit can propose a verification method suitable for each region. In addition, if the application content relates to an international project, the verification unit can propose a verification method from an international perspective. This allows for the selection of the optimal verification method by considering the geographical distribution of the application content. Some or all of the above-described processes in the verification unit may be performed using AI, or they may not. For example, the verification unit inputs geographical distribution data of the application content into the AI, and the AI ​​selects the optimal verification method.

[0097] The verification unit can improve the accuracy of its verification by referring to relevant literature related to the application content during the verification process. For example, the verification unit can automatically refer to literature related to the application content and use it as a reference for verification. Furthermore, if the application content relates to a specific field, the verification unit can also perform verification by referring to the latest literature in that field. In addition, if the application content relates to multiple fields, the verification unit can perform verification by referring to literature in each field. This improves the accuracy of verification by referring to relevant literature related to the application content. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can input the relevant literature data for the application content into the AI, and the AI ​​can improve the accuracy of the verification.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can analyze the applicant's input in real time and immediately point out input errors or deficiencies. For example, if an applicant enters a date in the wrong format, the reception desk will immediately provide the correct format. Also, if an applicant attempts to proceed without entering required fields, the reception desk can display a warning and prompt them to enter the information. Furthermore, if an applicant enters information that contradicts information they have previously entered, the reception desk can point out the inconsistency and prompt them to correct it. This allows applicants to reduce input errors and proceed with their applications smoothly.

[0100] The feedback system can refer to the applicant's past feedback history and provide individually customized feedback. For example, if an applicant has received specific feedback in the past, it will take that into consideration when providing new feedback. The feedback system can also learn specific patterns from the applicant's past feedback history and provide more effective feedback. Furthermore, based on the feedback the applicant has received in the past, the feedback system can proactively point out potential problems and encourage corrections. This allows applicants to receive more accurate feedback and improve the precision of their applications.

[0101] The revision unit can analyze the applicant's input and automatically suggest similar application content. For example, if the applicant enters a specific keyword, it will suggest past application content containing similar keywords. The revision unit can also automatically complete related application content based on the applicant's input. Furthermore, the revision unit can refer to the revision history of similar past application content and suggest the most suitable revision method for the content entered by the applicant. This allows applicants to efficiently revise and complete their application content.

[0102] The verification unit can select the most suitable verification method by referring to the applicant's past verification history when verifying application content. For example, it can propose the most suitable verification method based on the history of similar applications being verified in the past. Furthermore, the verification unit can identify frequently required verification sections from past verification history and propose verification in advance. In addition, the verification unit can analyze past verification history and select the most efficient verification method. Thus, by referring to past verification history, the optimal verification method can be selected.

[0103] The feedback unit can identify risky aspects of the application and provide specific correction instructions to the applicant. For example, it can identify legal and technical risks and provide specific correction instructions to the applicant. This allows for rapid risk assessment by identifying risky aspects of the application and providing specific correction instructions. Some or all of the above processing in the feedback unit is performed using a generation AI. For example, the feedback unit inputs the application content into the generation AI, which identifies risky aspects and outputs specific correction instructions.

[0104] The reception desk can estimate the user's emotions and adjust the application input interface based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick application entry. In this way, by adjusting the input interface according to the user's emotions, the application entry process can be made smoother.

[0105] The feedback unit can estimate the user's emotions and adjust the way feedback is presented based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand feedback. If the user is relaxed, it can provide more detailed feedback. Furthermore, if the user is in a hurry, it can provide concise and rapid feedback. By adjusting the way feedback is presented according to the user's emotions, the ease with which feedback is received is improved.

[0106] The correction unit can estimate the user's emotions and adjust the correction method based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand correction method. If the user is relaxed, it can also provide detailed correction options. Furthermore, if the user is in a hurry, it can provide a method that allows for quick correction completion. In this way, by adjusting the correction method according to the user's emotions, the appropriate correction method can be provided.

[0107] The confirmation unit can estimate the user's emotions and adjust the display method of the confirmation based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. By adjusting the display method of the confirmation according to the user's emotions, the visibility of the confirmation is improved.

[0108] The verification unit can estimate the user's emotions and determine the priority of verifications based on those emotions. For example, if the user is nervous, important verifications will be prioritized. If the user is relaxed, detailed verifications can be performed sequentially. Furthermore, if the user is in a hurry, only the most important verifications can be prioritized. In this way, by determining the priority of verifications according to the user's emotions, important verifications can be prioritized.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives the application details from the applicant. These details include paper applications, online applications, and patent applications. The reception desk can input online forms, voice input, and handwritten input, converting them into digital data. Step 2: The Feedback Department uses a generation AI to analyze the application content entered by the Reception Department and provides feedback on the necessary information. The Feedback Department identifies the missing information in the application and gives specific correction instructions to the applicant. For example, it may give instructions such as, "Please include the function name," "Please include the service name," "Please describe the summary of the consultation matter," "Please include the name of the target service or media," or "Please include the name of the data you would like to confirm." Step 3: The revision section modifies and supplements the application based on the information provided by the feedback section. The revision section provides an interface for applicants to modify their application based on the feedback, allowing them to add information or correct errors. Step 4: The verification team reviews the application content that has been corrected and supplemented by the revision team. The verification team checks the accuracy and format of the application content and verifies that all necessary information is included. They check the data names and service names listed in the application content and quickly identify any risk areas.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, feedback unit, modification unit, and confirmation 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, allowing the applicant to input the application details. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the application details using generating AI and provides feedback on the necessary information. The modification unit is implemented by the control unit 46A of the smart device 14, which provides an interface for the applicant to modify the application details based on the feedback. The confirmation unit is implemented by the specific processing unit 290 of the data processing unit 12, which confirms the modified and supplemented application details. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, feedback unit, modification unit, and confirmation unit, is implemented, for example, 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, allowing the applicant to input the application details by voice. The feedback unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the application details using generating AI and provides feedback on the necessary information. The modification unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides an interface for the applicant to modify the application details based on the feedback. The confirmation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which confirms the modified and supplemented application details. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, feedback unit, modification unit, and confirmation 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, allowing the applicant to input the application details by voice. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the application details using generating AI and provides feedback on the necessary information. The modification unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides an interface for the applicant to modify the application details based on the feedback. The confirmation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which confirms the modified and supplemented application details. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the reception unit, feedback unit, modification unit, and confirmation 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, allowing the applicant to input the application details by voice. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the application details using generating AI and provides feedback on the necessary information. The modification unit is implemented by, for example, the control unit 46A of the robot 414, which provides an interface for the applicant to modify the application details based on the feedback. The confirmation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which confirms the modified and supplemented application details. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The reception area where you enter the application details, The aforementioned reception unit analyzes the application details entered and provides feedback with necessary information, A modification unit modifies and supplements the application content based on the information provided by the aforementioned feedback unit, The system includes a confirmation unit that verifies the application content modified and supplemented by the aforementioned modification unit. A system characterized by the following features. (Note 2) The aforementioned feedback unit is The AI ​​generates information to identify missing details in the application and provides specific correction instructions to the applicant. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned modification section is, The application content will be modified and supplemented based on feedback from the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned verification unit is Review the application content corrected and supplemented by the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is By utilizing past application data, we can provide more accurate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Identify the risky aspects of the application and provide the applicant with specific instructions for revision. 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 application input interface based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the applicant's past application history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering application details, the input fields are customized based on the applicant's current projects 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 prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering application details, the system prioritizes displaying the most relevant input fields based on the applicant's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When you enter your application details, the system will analyze your social media activity and suggest relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the importance of the application. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the category of the application. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned feedback unit is When providing feedback, we prioritize feedback based on when the application was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned feedback unit is When providing feedback, we adjust the order of feedback based on the relevance of the application content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned modification section is, It estimates the user's emotions and adjusts the correction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned modification section is, When making revisions, the most suitable revision method is selected by referring to the past revision history of the application. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned modification section is, When making revisions, customize the method of revision based on the current status of the application. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned modification section is, It estimates user sentiment and determines the priority of modifications based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned modification section is, When making revisions, the most suitable revision method will be selected considering the geographical distribution of the application content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned modification section is, When making revisions, refer to relevant documents related to the application content to improve the accuracy of the revisions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned verification unit is The system estimates the user's emotions and adjusts how confirmations are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned verification unit is During the verification process, the most suitable verification method will be selected by referring to the past verification history of the application. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned verification unit is During the verification process, the verification method will be customized based on the current status of the application. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned verification unit is The system estimates the user's emotions and determines the priority of confirmations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned verification unit is During the verification process, the most appropriate verification method will be selected, taking into account the geographical distribution of the application content. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned verification unit is During the verification process, we will refer to relevant literature related to the application content to improve the accuracy of the verification. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 area where you enter the application details, The aforementioned reception unit analyzes the application details entered and provides feedback with necessary information, A modification unit modifies and supplements the application content based on the information provided by the aforementioned feedback unit, The system includes a confirmation unit that verifies the application content modified and supplemented by the aforementioned modification unit. A system characterized by the following features.

2. The aforementioned feedback unit is The AI ​​generates the application to identify missing information and provides the applicant with specific instructions for correction. The system according to feature 1.

3. The aforementioned modification section is, The application content will be modified and supplemented based on feedback from the generating AI. The system according to feature 1.

4. The aforementioned verification unit is Review the application content that has been corrected and supplemented by the generating AI. The system according to feature 1.

5. The aforementioned feedback unit is By utilizing past application data, we can provide more accurate feedback. The system according to feature 1.

6. The aforementioned feedback unit is Identify the risky aspects of the application and provide the applicant with specific instructions for revision. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the application input interface based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the applicant's past application history and suggest the optimal input method. The system according to feature 1.

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