system

Abstract

The system according to this embodiment aims to automate the checking of document files and reduce errors. [Solution] The system according to the embodiment comprises a reception unit, a checking unit, and a suggestion unit. The reception unit receives document files. The checking unit checks the document files received by the reception unit based on rules that have been programmed in advance. The suggestion unit proposes improvements to the document files checked by the checking unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 prior art, the checking of document files is performed visually, and there is a high risk of many mistakes.

[0005] The system according to the embodiment aims to automate the checking of document files and reduce mistakes.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a checking unit, and a proposal unit. The reception unit receives a document file. The checking unit checks the document file received by the reception unit based on rules memorized in advance. The proposal unit proposes improvement points of the document file checked by the checking unit.

Effects of the Invention

[0007] The system according to this embodiment can automate the checking of document files and reduce errors. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The document checking system according to an embodiment of the present invention is a system that automatically checks document files and suggests areas for improvement. This document checking system receives a document file, checks the document based on pre-programmed rules, and suggests areas for improvement. For example, when a document file is given to an AI, the AI ​​checks the document based on pre-programmed rules. Specifically, it checks whether there is a copyright notice in the document, whether the confidential notice is properly displayed, and whether the address is included in the case of a document for an agent, etc. The AI ​​analyzes and checks the document based on these rules. Next, the AI ​​suggests areas for improvement. For example, if a copyright notice is missing or the confidential notice is inappropriate, the AI ​​will point this out and suggest a correction. This allows the user to quickly correct the document and save the trouble of reissuing it. Furthermore, automating document checking reduces errors and improves document quality. For example, the AI ​​reliably checks even small details that are often overlooked during manual checks, thus improving document quality. In addition, the effort of reissuing and correcting documents is saved, improving work efficiency. For example, the time spent revising documents can be reduced, allowing employees to focus on other tasks. This enables the document checking system to improve work efficiency by automatically checking documents and suggesting improvements.

[0029] The document checking system according to this embodiment comprises a reception unit, a checking unit, and a proposal unit. The reception unit receives document files. Document files include, but are not limited to, PDF, Word, and text formats. The reception unit can receive document files simply by a user uploading them. The checking unit checks the document files based on pre-programmed rules. For example, the checking unit verifies whether a copyright notice is present in the document. The checking unit can also verify whether confidential notices are properly displayed in the document. Furthermore, in the case of documents addressed to agents, etc., the checking unit can verify whether an address is included. For example, the checking unit searches for specific keywords or phrases in the document and performs checks based on the rules. The proposal unit proposes improvements to the document files checked by the checking unit. For example, the proposal unit points out if a copyright notice is missing in the document and proposes a correction. The proposal unit can also point out if confidential notices are inappropriate in the document and propose a correction. Furthermore, the proposal unit can also point out if an address is not included in a document intended for an agent or other party, and propose a revised version. For example, the proposal unit can insert a revised version into a specific location within the document and provide the user with a concrete method for making the revision. As a result, the document checking system according to the embodiment can improve operational efficiency by automatically checking documents and proposing improvements. Some or all of the above-described processes in the checking unit and proposal unit may be performed using AI, for example, or without AI. For example, the checking unit can check a document using an AI model that takes a document file as input and outputs the check results. The proposal unit can propose improvements using an AI model that takes the check results as input and outputs revised versions.

[0030] The checking unit can verify whether a document contains a copyright notice. For example, the checking unit can search for specific keywords or phrases within the document and check for the presence of a copyright notice. For example, the checking unit can check whether the document contains a "c" mark, the copyright holder's name, and the year. The checking unit can also verify whether a copyright notice is located at a specific position within the document. For example, the checking unit can check whether a copyright notice is at the beginning or end of the document. Furthermore, the checking unit can verify whether a copyright notice is displayed in a specific format within the document. For example, the checking unit can check whether a "c" mark, the copyright holder's name, and the year are displayed together. This allows for the automatic verification of the presence or absence of copyright notices within a document. Some or all of the above-described processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can perform document checks using an AI model that takes a document file as input and outputs whether or not a copyright notice is present.

[0031] The checking unit can verify whether confidential information is appropriately displayed within a document. For example, the checking unit can search for specific keywords or phrases within the document and verify that confidential information is appropriately displayed. For instance, the checking unit can check whether the document contains indications such as "Confidential" or "Confidential Information." The checking unit can also verify whether confidential information is present at specific locations within the document. For example, the checking unit can check whether confidential information is present at the beginning or end of the document. Furthermore, the checking unit can verify whether confidential information is displayed in a specific format within the document. For example, the checking unit can check whether confidential information is highlighted in bold or red. This allows for automatic verification of the appropriateness of confidential information within a document. Some or all of the above-described processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can perform document checks using an AI model that takes a document file as input and outputs the appropriateness of the confidential information.

[0032] The checking unit can verify whether an address is included in a document intended for an agent. For example, the checking unit can search for specific keywords or phrases within the document to verify if an address is included in a document intended for an agent. For example, the checking unit can verify whether terms such as "address" or "location" are included in the document. The checking unit can also verify whether an address is located at a specific position within the document. For example, the checking unit can verify whether an address is located at the beginning or end of the document. Furthermore, the checking unit can verify whether the address is presented in a specific format within the document. For example, the checking unit can verify whether the address is written in the correct format. This allows for automatic verification of whether an address is included in a document intended for an agent. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can perform document checks using an AI model that takes a document file as input and outputs whether or not an address is included.

[0033] The suggestion department can point out when a copyright notice is missing from a document and propose a correction. For example, the suggestion department can insert a correction at a specific location in the document and provide the user with specific instructions on how to make the correction. For example, if a copyright notice is missing from a document, the suggestion department might propose a correction such as "Please add a © mark, the name of the copyright holder, and the year" at that location. The suggestion department can also propose a correction such as "Please write the copyright notice in the correct format" at a location where the copyright notice is inappropriate. Furthermore, if a copyright notice is not located in a specific place in the document, the suggestion department can propose a correction such as "Please add a copyright notice at the beginning or end of the document" at that location. This allows the suggestion department to point out the absence of a copyright notice in a document and propose a correction. Some or all of the above processing by the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can make suggestions for improvement using an AI model that takes check results as input and outputs correction suggestions.

[0034] The proposal department can point out inappropriate use of confidential information within a document and propose corrections. For example, the proposal department can insert a correction suggestion at a specific location within the document and provide the user with specific instructions on how to make the correction. For instance, if confidential information is inappropriately displayed in the document, the proposal department might suggest, "Please write the confidential information in the correct format." It can also suggest, "Please add the confidential information to the beginning or end of the document," if it is missing a specific location. Furthermore, if the confidential information is not presented in a specific format, the proposal department might suggest, "Please highlight the confidential information in bold or red." This allows the proposal department to identify inappropriate confidential information within the document and propose corrections. Some or all of the above processing by the proposal department may be performed using AI, or not. For example, the proposal department can use an AI model that takes check results as input and outputs correction suggestions to propose improvements.

[0035] The reception department can analyze a user's past document submission history and select the optimal reception method. For example, the reception department can store the user's past document submission history in a database and analyze that data. For example, the reception department can prioritize the reception of document formats that the user has frequently submitted in the past. The reception department can also analyze the time periods in which the user has submitted documents in the past and perform reception during those time periods. Furthermore, the reception department can automatically recognize specific document formats from the user's past submission history and simplify the reception process. For example, the reception department can select the optimal reception method based on the format of documents the user has submitted in the past. This allows the reception department to select the optimal reception method based on the user's past submission history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input past submission history data into a generating AI and have the generating AI select the optimal reception method.

[0036] The reception unit can filter document files upon receipt based on the user's current projects and areas of interest. For example, the reception unit can store information about the user's current projects and areas of interest in a database and perform filtering based on that data. For example, the reception unit can prioritize receiving documents related to the user's current projects. The reception unit can also filter and receive highly relevant documents based on the user's areas of interest. Furthermore, the reception unit can automatically select and receive necessary documents according to the user's project progress. For example, the reception unit can select the most suitable documents based on the user's project progress. This allows for filtering of documents based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data about projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0037] The reception unit can prioritize receiving documents that are highly relevant based on the user's geographical location information when receiving document files. For example, the reception unit can obtain the user's geographical location information from GPS data or IP address and select highly relevant documents based on that data. For example, if the user is in a specific region, the reception unit will prioritize receiving documents related to that region. The reception unit can also prioritize receiving documents that follow region-specific rules based on the user's geographical location information. Furthermore, if the user is on the move, the reception unit can prioritize receiving documents that are most relevant to the user's current location. For example, the reception unit will select the most suitable document based on the user's current location. This allows the reception unit to prioritize receiving highly relevant documents based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input geographical location data into a generating AI and have the generating AI perform the selection of highly relevant documents.

[0038] The reception unit can analyze a user's social media activity when receiving document files and accept relevant documents. For example, the reception unit can store the user's social media activity in a database and select relevant documents based on that data. For example, the reception unit can prioritize accepting documents related to the user's current interests based on their social media activity. The reception unit can also prioritize accepting documents related to projects mentioned by the user on social media. Furthermore, the reception unit can analyze the user's social media activity and automatically select and accept highly relevant documents. For example, the reception unit can select the most suitable documents based on the user's social media activity. This allows the reception unit to accept relevant documents based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input social media activity data into a generating AI and have the generating AI perform the selection of relevant documents.

[0039] The checking unit can improve the accuracy of its checks by considering the interrelationships between documents. For example, the checking unit can check citations and references within a document and perform the check while considering their interrelationships. For example, the checking unit can verify whether citations within a document are accurate. The checking unit can also check the consistency between chapters and sections of a document and perform the check while considering their interrelationships. Furthermore, the checking unit can check the consistency of data and statistical information within a document and perform the check while considering their interrelationships. For example, the checking unit can verify whether the data within a document matches that of other documents. This improves the accuracy of the check by considering the interrelationships between documents. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input interrelationship data between documents into a generating AI and have the generating AI perform the improvement of the check's accuracy.

[0040] The checking unit can perform checks while considering the attribute information of the document submitter. For example, the checking unit can store the attribute information of the document submitter in a database and perform checks based on that data. For example, the checking unit can focus on checking technical terms and content based on the submitter's field of expertise. The checking unit can also verify appropriate expressions and honorifics based on the submitter's position and job description. Furthermore, the checking unit can refer to the submitter's past submission history to focus on checking for specific mistakes or patterns. For example, the checking unit can detect specific mistakes based on the submitter's past submission history. This improves the accuracy of the check by considering the attribute information of the document submitter. Some or all of the above processes in the checking unit may be performed using AI, for example, or not using AI. For example, the checking unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of the check.

[0041] The checking unit can perform checks while considering the geographical distribution of documents. For example, the checking unit can store the geographical distribution of documents in a database and perform checks based on that data. For example, if the checking unit relates to a specific region, it can perform checks while considering the rules and regulations specific to that region. The checking unit can also perform checks while considering the characteristics and culture of each region based on the geographical distribution of documents. Furthermore, if the checking unit relates to multiple regions, it can perform checks while considering the rules and regulations of each region. For example, the checking unit can select the optimal checking method based on the geographical distribution of documents. This improves the accuracy of the check by considering the geographical distribution of documents. Some or all of the above processing in the checking unit may be performed using AI, for example, or without using AI. For example, the checking unit can input geographical distribution data into a generating AI and have the generating AI perform the task of improving the accuracy of the check.

[0042] The checking unit can improve the accuracy of its checks by referring to relevant literature in the document during the checking process. For example, the checking unit can refer to relevant literature to verify whether citations and references within the document are accurate. The checking unit can also refer to relevant literature to verify whether the content of the document is based on the latest research and data. Furthermore, the checking unit can refer to relevant literature to verify whether the claims and conclusions in the document are consistent with other literature. For example, the checking unit can select the optimal checking method based on the relevant literature in the document. This improves the accuracy of the check by referring to relevant literature in the document. Some or all of the above processes in the checking unit may be performed using AI, for example, or not using AI. For example, the checking unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the check.

[0043] The proposal department can adjust the level of detail of a proposal based on the importance of the document. For example, the proposal department can store the document's importance in a database and adjust the level of detail of the proposal based on that data. For example, for important documents, the proposal department can provide detailed proposals and clearly indicate all areas for improvement. For less important documents, the proposal department can provide concise proposals and indicate only the main areas for improvement. Furthermore, the proposal department can adjust the level of detail of the proposal in stages according to the importance of the document. For example, the proposal department can select the optimal proposal method based on the document's importance. This allows the level of detail of the proposal to be adjusted according to the document's importance. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input document importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0044] The proposal department can apply different proposal algorithms depending on the document category when making a proposal. For example, the proposal department can store the document category in a database and select a proposal algorithm based on that data. For example, in the case of a technical document, the proposal department can apply a specialized proposal algorithm to suggest technical improvements. In the case of a business document, the proposal department can also apply a business-specific proposal algorithm to suggest business improvements. Furthermore, in the case of a legal document, the proposal department can apply a legal-specific proposal algorithm to suggest legal improvements. For example, the proposal department selects the optimal proposal algorithm based on the document category. This allows the appropriate proposal algorithm to be applied according to the document category. Some or all of the above processing in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input document category data into a generating AI and have the generating AI select a proposal algorithm.

[0045] The proposal department can determine the priority of proposals based on the document submission timing at the time of proposal submission. For example, the proposal department can store the document submission timing in a database and determine the priority of proposals based on that data. For example, the proposal department can prioritize proposals for documents with approaching deadlines. Conversely, the proposal department can also propose documents with later submission timings using the normal priority system. Furthermore, the proposal department can adjust the priority of proposals in stages according to the submission timing. For example, the proposal department can select the optimal proposal method based on the document submission timing. This allows the proposal priority to be determined according to the document submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0046] The proposal unit can adjust the order of proposals based on the relevance of the documents during the proposal process. For example, the proposal unit can store the relevance of documents in a database and adjust the order of proposals based on that data. For example, the proposal unit can prioritize proposals when the relevance of documents is high. Conversely, the proposal unit can also propose in the normal order when the relevance of documents is low. Furthermore, the proposal unit can adjust the order of proposals in stages according to the relevance of the documents. For example, the proposal unit can select the optimal proposal method based on the relevance of the documents. This allows the order of proposals to be adjusted according to the relevance of the documents. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input document relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The reception department can analyze a user's past document submission history and select the optimal reception method. For example, the reception department can store the user's past document submission history in a database and analyze that data. For example, the reception department can prioritize the reception of document formats that the user has frequently submitted in the past. The reception department can also analyze the time periods in which the user has submitted documents in the past and perform reception during those time periods. Furthermore, the reception department can automatically recognize specific document formats from the user's past submission history and simplify the reception process. For example, the reception department can select the optimal reception method based on the format of documents the user has submitted in the past. This allows the reception department to select the optimal reception method based on the user's past submission history. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input past submission history data into a generating AI and have the generating AI select the optimal reception method.

[0049] The checking unit can improve the accuracy of its checks by considering the interrelationships between documents. For example, the checking unit can check citations and references within a document and perform the check while considering their interrelationships. For example, the checking unit can verify whether citations within a document are accurate. The checking unit can also check the consistency between chapters and sections within a document and perform the check while considering their interrelationships. Furthermore, the checking unit can check the consistency of data and statistical information within a document and perform the check while considering their interrelationships. For example, the checking unit can verify whether the data within a document matches that of other documents. This improves the accuracy of the check by considering the interrelationships between documents. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input interrelationship data between documents into a generating AI and have the generating AI perform the improvement of the checking accuracy.

[0050] The proposal department can adjust the level of detail of its proposals based on the importance of the document. For example, the proposal department can store the importance of the document in a database and adjust the level of detail of the proposals based on that data. For example, for important documents, the proposal department can provide detailed proposals and clearly indicate all areas for improvement. For less important documents, the proposal department can provide concise proposals and indicate only the main areas for improvement. Furthermore, the proposal department can adjust the level of detail of its proposals in stages according to the importance of the document. For example, the proposal department can select the optimal proposal method based on the importance of the document. This allows the level of detail of the proposals to be adjusted according to the importance of the document. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input document importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0051] The reception desk can prioritize receiving documents that are highly relevant based on the user's geographical location information. For example, the reception desk can obtain the user's geographical location information from GPS data or IP address and select highly relevant documents based on that data. For example, if the user is in a specific region, the reception desk will prioritize receiving documents related to that region. The reception desk can also prioritize receiving documents that follow region-specific rules based on the user's geographical location information. Furthermore, if the user is on the move, the reception desk can prioritize receiving documents that are most relevant to their current location. For example, the reception desk will select the most suitable document based on the user's current location. This allows the reception desk to prioritize receiving highly relevant documents based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input geographical location data into a generating AI and have the generating AI select highly relevant documents.

[0052] The checking unit can improve the accuracy of its checks by referring to relevant literature in the document. For example, the checking unit can refer to relevant literature to verify whether citations and references within the document are accurate. It can also refer to relevant literature to verify whether the content of the document is based on the latest research and data. Furthermore, it can refer to relevant literature to verify whether the claims and conclusions in the document are consistent with other literature. For example, the checking unit can select the optimal checking method based on the relevant literature in the document. This improves the accuracy of the check by referring to relevant literature in the document. Some or all of the above processes in the checking unit may be performed using AI, for example, or not. For example, the checking unit can input relevant literature data into a generating AI and have the generating AI perform the check accuracy improvement.

[0053] The proposal unit can apply different proposal algorithms depending on the document category. For example, the proposal unit stores the document category in a database and selects a proposal algorithm based on that data. For example, in the case of a technical document, the proposal unit can apply a specialized proposal algorithm to suggest technical improvements. In the case of a business document, the proposal unit can also apply a business-specific proposal algorithm to suggest business improvements. Furthermore, in the case of a legal document, the proposal unit can apply a legal-specific proposal algorithm to suggest legal improvements. For example, the proposal unit selects the optimal proposal algorithm based on the document category. This allows the appropriate proposal algorithm to be applied according to the document category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input document category data into a generating AI and have the generating AI select a proposal algorithm.

[0054] The checking unit can perform checks while considering the attribute information of the document submitter. For example, the checking unit can store the attribute information of the document submitter in a database and perform checks based on that data. For example, the checking unit can focus on checking technical terms and content based on the submitter's field of expertise. The checking unit can also verify appropriate expressions and honorifics based on the submitter's position and job description. Furthermore, the checking unit can refer to the submitter's past submission history to focus on checking for specific mistakes or patterns. For example, the checking unit can detect specific mistakes based on the submitter's past submission history. This improves the accuracy of the check by considering the attribute information of the document submitter. Some or all of the above processes in the checking unit may be performed using AI, for example, or not using AI. For example, the checking unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of the check.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk accepts document files. Document files include, but are not limited to, PDF, Word, and text formats. The reception desk can accept document files simply by the user uploading them. Step 2: The checking unit checks the document file based on pre-programmed rules. For example, it checks whether there is a copyright notice in the document, whether confidentiality notices are properly displayed, and whether an address is included in documents intended for agents, etc. The checking unit searches for specific keywords or phrases within the document and performs checks based on the rules. Step 3: The proposal team proposes improvements to the document file checked by the checking team. For example, if the document lacks a copyright notice, they will point this out and propose a correction. Similarly, if the confidential notice is inappropriate or the address is missing, they will point this out and propose a correction. The proposal team will insert the proposed corrections into specific locations within the document and provide the user with concrete instructions on how to make the corrections.

[0057] (Example of form 2) The document checking system according to an embodiment of the present invention is a system that automatically checks document files and suggests areas for improvement. This document checking system receives a document file, checks the document based on pre-programmed rules, and suggests areas for improvement. For example, when a document file is given to an AI, the AI ​​checks the document based on pre-programmed rules. Specifically, it checks whether there is a copyright notice in the document, whether the confidential notice is properly displayed, and whether the address is included in the case of a document for an agent, etc. The AI ​​analyzes and checks the document based on these rules. Next, the AI ​​suggests areas for improvement. For example, if a copyright notice is missing or the confidential notice is inappropriate, the AI ​​will point this out and suggest a correction. This allows the user to quickly correct the document and save the trouble of reissuing it. Furthermore, automating document checking reduces errors and improves document quality. For example, the AI ​​reliably checks even small details that are often overlooked during manual checks, thus improving document quality. In addition, the effort of reissuing and correcting documents is saved, improving work efficiency. For example, the time spent revising documents can be reduced, allowing employees to focus on other tasks. This enables the document checking system to improve work efficiency by automatically checking documents and suggesting improvements.

[0058] The document checking system according to this embodiment comprises a reception unit, a checking unit, and a proposal unit. The reception unit receives document files. Document files include, but are not limited to, PDF, Word, and text formats. The reception unit can receive document files simply by a user uploading them. The checking unit checks the document files based on pre-programmed rules. For example, the checking unit verifies whether a copyright notice is present in the document. The checking unit can also verify whether confidential notices are properly displayed in the document. Furthermore, in the case of documents addressed to agents, etc., the checking unit can verify whether an address is included. For example, the checking unit searches for specific keywords or phrases in the document and performs checks based on the rules. The proposal unit proposes improvements to the document files checked by the checking unit. For example, the proposal unit points out if a copyright notice is missing in the document and proposes a correction. The proposal unit can also point out if confidential notices are inappropriate in the document and propose a correction. Furthermore, the proposal unit can also point out if an address is not included in a document intended for an agent or other party, and propose a revised version. For example, the proposal unit can insert a revised version into a specific location within the document and provide the user with a concrete method for making the revision. As a result, the document checking system according to the embodiment can improve operational efficiency by automatically checking documents and proposing improvements. Some or all of the above-described processes in the checking unit and proposal unit may be performed using AI, for example, or without AI. For example, the checking unit can check a document using an AI model that takes a document file as input and outputs the check results. The proposal unit can propose improvements using an AI model that takes the check results as input and outputs revised versions.

[0059] The checking unit can verify whether a document contains a copyright notice. For example, the checking unit can search for specific keywords or phrases within the document and check for the presence of a copyright notice. For example, the checking unit can check whether the document contains a "c" mark, the copyright holder's name, and the year. The checking unit can also verify whether a copyright notice is located at a specific position within the document. For example, the checking unit can check whether a copyright notice is at the beginning or end of the document. Furthermore, the checking unit can verify whether a copyright notice is displayed in a specific format within the document. For example, the checking unit can check whether a "c" mark, the copyright holder's name, and the year are displayed together. This allows for the automatic verification of the presence or absence of copyright notices within a document. Some or all of the above-described processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can perform document checks using an AI model that takes a document file as input and outputs whether or not a copyright notice is present.

[0060] The checking unit can verify whether confidential information is appropriately displayed within a document. For example, the checking unit can search for specific keywords or phrases within the document and verify that confidential information is appropriately displayed. For instance, the checking unit can check whether the document contains indications such as "Confidential" or "Confidential Information." The checking unit can also verify whether confidential information is present at specific locations within the document. For example, the checking unit can check whether confidential information is present at the beginning or end of the document. Furthermore, the checking unit can verify whether confidential information is displayed in a specific format within the document. For example, the checking unit can check whether confidential information is highlighted in bold or red. This allows for automatic verification of the appropriateness of confidential information within a document. Some or all of the above-described processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can perform document checks using an AI model that takes a document file as input and outputs the appropriateness of the confidential information.

[0061] The checking unit can verify whether an address is included in a document intended for an agent. For example, the checking unit can search for specific keywords or phrases within the document to verify if an address is included in a document intended for an agent. For example, the checking unit can verify whether terms such as "address" or "location" are included in the document. The checking unit can also verify whether an address is located at a specific position within the document. For example, the checking unit can verify whether an address is located at the beginning or end of the document. Furthermore, the checking unit can verify whether the address is presented in a specific format within the document. For example, the checking unit can verify whether the address is written in the correct format. This allows for automatic verification of whether an address is included in a document intended for an agent. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can perform document checks using an AI model that takes a document file as input and outputs whether or not an address is included.

[0062] The suggestion department can point out when a copyright notice is missing from a document and propose a correction. For example, the suggestion department can insert a correction at a specific location in the document and provide the user with specific instructions on how to make the correction. For example, if a copyright notice is missing from a document, the suggestion department might propose a correction such as "Please add a © mark, the name of the copyright holder, and the year" at that location. The suggestion department can also propose a correction such as "Please write the copyright notice in the correct format" at a location where the copyright notice is inappropriate. Furthermore, if a copyright notice is not located in a specific place in the document, the suggestion department can propose a correction such as "Please add a copyright notice at the beginning or end of the document" at that location. This allows the suggestion department to point out the absence of a copyright notice in a document and propose a correction. Some or all of the above processing by the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can make suggestions for improvement using an AI model that takes check results as input and outputs correction suggestions.

[0063] The proposal department can point out inappropriate use of confidential information within a document and propose corrections. For example, the proposal department can insert a correction suggestion at a specific location within the document and provide the user with specific instructions on how to make the correction. For instance, if confidential information is inappropriately displayed in the document, the proposal department might suggest, "Please write the confidential information in the correct format." It can also suggest, "Please add the confidential information to the beginning or end of the document," if it is missing a specific location. Furthermore, if the confidential information is not presented in a specific format, the proposal department might suggest, "Please highlight the confidential information in bold or red." This allows the proposal department to identify inappropriate confidential information within the document and propose corrections. Some or all of the above processing by the proposal department may be performed using AI, or not. For example, the proposal department can use an AI model that takes check results as input and outputs correction suggestions to propose improvements.

[0064] The reception unit can estimate the user's emotions and adjust the timing of document file reception based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions and adjust the reception timing. The reception unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the reception timing. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate and adjust the reception timing. This allows the timing of document file reception to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0065] The reception department can analyze a user's past document submission history and select the optimal reception method. For example, the reception department can store the user's past document submission history in a database and analyze that data. For example, the reception department can prioritize the reception of document formats that the user has frequently submitted in the past. The reception department can also analyze the time periods in which the user has submitted documents in the past and perform reception during those time periods. Furthermore, the reception department can automatically recognize specific document formats from the user's past submission history and simplify the reception process. For example, the reception department can select the optimal reception method based on the format of documents the user has submitted in the past. This allows the reception department to select the optimal reception method based on the user's past submission history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input past submission history data into a generating AI and have the generating AI select the optimal reception method.

[0066] The reception unit can filter document files upon receipt based on the user's current projects and areas of interest. For example, the reception unit can store information about the user's current projects and areas of interest in a database and perform filtering based on that data. For example, the reception unit can prioritize receiving documents related to the user's current projects. The reception unit can also filter and receive highly relevant documents based on the user's areas of interest. Furthermore, the reception unit can automatically select and receive necessary documents according to the user's project progress. For example, the reception unit can select the most suitable documents based on the user's project progress. This allows for filtering of documents based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data about projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0067] The reception unit can estimate the user's emotions and determine the priority of document files to be received based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions and determine the priority of document files. The reception unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of document files. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of document files. This allows the priority of document files to be determined according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using 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-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0068] The reception unit can prioritize receiving documents that are highly relevant based on the user's geographical location information when receiving document files. For example, the reception unit can obtain the user's geographical location information from GPS data or IP address and select highly relevant documents based on that data. For example, if the user is in a specific region, the reception unit will prioritize receiving documents related to that region. The reception unit can also prioritize receiving documents that follow region-specific rules based on the user's geographical location information. Furthermore, if the user is on the move, the reception unit can prioritize receiving documents that are most relevant to the user's current location. For example, the reception unit will select the most suitable document based on the user's current location. This allows the reception unit to prioritize receiving highly relevant documents based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input geographical location data into a generating AI and have the generating AI perform the selection of highly relevant documents.

[0069] The reception unit can analyze a user's social media activity when receiving document files and accept relevant documents. For example, the reception unit can store the user's social media activity in a database and select relevant documents based on that data. For example, the reception unit can prioritize accepting documents related to the user's current interests based on their social media activity. The reception unit can also prioritize accepting documents related to projects mentioned by the user on social media. Furthermore, the reception unit can analyze the user's social media activity and automatically select and accept highly relevant documents. For example, the reception unit can select the most suitable documents based on the user's social media activity. This allows the reception unit to accept relevant documents based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input social media activity data into a generating AI and have the generating AI perform the selection of relevant documents.

[0070] The checking unit can estimate the user's emotions and adjust the checking criteria based on the estimated emotions. For example, the checking unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the checking unit can calculate an emotion score based on changes in facial expressions and adjust the checking criteria. The checking unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the checking unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the checking criteria. The checking unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the checking unit can calculate an emotion score based on fluctuations in heart rate and adjust the checking criteria. This allows the checking criteria to be adjusted 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0071] The checking unit can improve the accuracy of its checks by considering the interrelationships between documents. For example, the checking unit can check citations and references within a document and perform the check while considering their interrelationships. For example, the checking unit can verify whether citations within a document are accurate. The checking unit can also check the consistency between chapters and sections of a document and perform the check while considering their interrelationships. Furthermore, the checking unit can check the consistency of data and statistical information within a document and perform the check while considering their interrelationships. For example, the checking unit can verify whether the data within a document matches that of other documents. This improves the accuracy of the check by considering the interrelationships between documents. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input interrelationship data between documents into a generating AI and have the generating AI perform the improvement of the check's accuracy.

[0072] The checking unit can perform checks while considering the attribute information of the document submitter. For example, the checking unit can store the attribute information of the document submitter in a database and perform checks based on that data. For example, the checking unit can focus on checking technical terms and content based on the submitter's field of expertise. The checking unit can also verify appropriate expressions and honorifics based on the submitter's position and job description. Furthermore, the checking unit can refer to the submitter's past submission history to focus on checking for specific mistakes or patterns. For example, the checking unit can detect specific mistakes based on the submitter's past submission history. This improves the accuracy of the check by considering the attribute information of the document submitter. Some or all of the above processes in the checking unit may be performed using AI, for example, or not using AI. For example, the checking unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of the check.

[0073] The checking unit can estimate the user's emotions and adjust the order in which the check results are displayed based on the estimated emotions. For example, the checking unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the checking unit can calculate an emotion score based on changes in facial expressions and adjust the order in which the check results are displayed. The checking unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the checking unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the order in which the check results are displayed. The checking unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the checking unit can calculate an emotion score based on fluctuations in heart rate and adjust the order in which the check results are displayed. This allows the order in which the check results are displayed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0074] The checking unit can perform checks while considering the geographical distribution of documents. For example, the checking unit can store the geographical distribution of documents in a database and perform checks based on that data. For example, if the checking unit relates to a specific region, it can perform checks while considering the rules and regulations specific to that region. The checking unit can also perform checks while considering the characteristics and culture of each region based on the geographical distribution of documents. Furthermore, if the checking unit relates to multiple regions, it can perform checks while considering the rules and regulations of each region. For example, the checking unit can select the optimal checking method based on the geographical distribution of documents. This improves the accuracy of the check by considering the geographical distribution of documents. Some or all of the above processing in the checking unit may be performed using AI, for example, or without using AI. For example, the checking unit can input geographical distribution data into a generating AI and have the generating AI perform the task of improving the accuracy of the check.

[0075] The checking unit can improve the accuracy of its checks by referring to relevant literature in the document during the checking process. For example, the checking unit can refer to relevant literature to verify whether citations and references within the document are accurate. The checking unit can also refer to relevant literature to verify whether the content of the document is based on the latest research and data. Furthermore, the checking unit can refer to relevant literature to verify whether the claims and conclusions in the document are consistent with other literature. For example, the checking unit can select the optimal checking method based on the relevant literature in the document. This improves the accuracy of the check by referring to relevant literature in the document. Some or all of the above processes in the checking unit may be performed using AI, for example, or not using AI. For example, the checking unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the check.

[0076] The proposal unit can estimate the user's emotions and adjust the way the proposal is presented based on the estimated emotions. For example, the proposal unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the proposal unit can calculate an emotion score based on changes in facial expressions and adjust the way the proposal is presented. The proposal unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the proposal unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the way the proposal is presented. The proposal unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the proposal unit can calculate an emotion score based on fluctuations in heart rate and adjust the way the proposal is presented. This allows the proposal to be presented in accordance with the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0077] The proposal department can adjust the level of detail of a proposal based on the importance of the document. For example, the proposal department can store the document's importance in a database and adjust the level of detail of the proposal based on that data. For example, for important documents, the proposal department can provide detailed proposals and clearly indicate all areas for improvement. For less important documents, the proposal department can provide concise proposals and indicate only the main areas for improvement. Furthermore, the proposal department can adjust the level of detail of the proposal in stages according to the importance of the document. For example, the proposal department can select the optimal proposal method based on the document's importance. This allows the level of detail of the proposal to be adjusted according to the document's importance. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input document importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposal.

[0078] The proposal department can apply different proposal algorithms depending on the document category when making a proposal. For example, the proposal department can store the document category in a database and select a proposal algorithm based on that data. For example, in the case of a technical document, the proposal department can apply a specialized proposal algorithm to suggest technical improvements. In the case of a business document, the proposal department can also apply a business-specific proposal algorithm to suggest business improvements. Furthermore, in the case of a legal document, the proposal department can apply a legal-specific proposal algorithm to suggest legal improvements. For example, the proposal department selects the optimal proposal algorithm based on the document category. This allows the appropriate proposal algorithm to be applied according to the document category. Some or all of the above processing in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input document category data into a generating AI and have the generating AI select a proposal algorithm.

[0079] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in facial expressions and adjust the length of the suggestion. The suggestion unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the length of the suggestion. The suggestion unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on fluctuations in heart rate and adjust the length of the suggestion. This allows the length of the suggestion to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0080] The proposal department can determine the priority of proposals based on the document submission timing at the time of proposal submission. For example, the proposal department can store the document submission timing in a database and determine the priority of proposals based on that data. For example, the proposal department can prioritize proposals for documents with approaching deadlines. Conversely, the proposal department can also propose documents with later submission timings using the normal priority system. Furthermore, the proposal department can adjust the priority of proposals in stages according to the submission timing. For example, the proposal department can select the optimal proposal method based on the document submission timing. This allows the proposal priority to be determined according to the document submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input submission timing data into a generating AI and have the generating AI determine the priority of proposals.

[0081] The proposal unit can adjust the order of proposals based on the relevance of the documents during the proposal process. For example, the proposal unit can store the relevance of documents in a database and adjust the order of proposals based on that data. For example, the proposal unit can prioritize proposals when the relevance of documents is high. Conversely, the proposal unit can also propose in the normal order when the relevance of documents is low. Furthermore, the proposal unit can adjust the order of proposals in stages according to the relevance of the documents. For example, the proposal unit can select the optimal proposal method based on the relevance of the documents. This allows the order of proposals to be adjusted according to the relevance of the documents. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input document relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, checking unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives document files when a user uploads them. The checking unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and checks the document files based on pre-programmed rules. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes improvements to the document files checked by the checking unit. Some or all of the processing of the checking unit and the proposal unit may be implemented, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, checking unit, and suggestion 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 control unit 46A of the smart glasses 214 and receives document files when a user uploads them. The checking unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and checks the document files based on pre-programmed rules. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and suggests improvements to the document files checked by the checking unit. Some or all of the processing of the checking unit and the suggestion unit may be implemented, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, checking unit, and suggestion unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives document files when a user uploads them. The checking unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and checks the document files based on pre-programmed rules. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and suggests improvements to the document files checked by the checking unit. Some or all of the processing of the checking unit and the suggestion unit may be implemented, for example, by the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, checking unit, and suggestion 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 control unit 46A of the robot 414 and receives document files when a user uploads them. The checking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and checks the document files based on pre-programmed rules. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and suggests improvements to the document files checked by the checking unit. Some or all of the processing of the checking unit and the suggestion unit may be implemented by, for example, the control unit 46A of the robot 414.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The reception department can analyze a user's past document submission history and select the optimal reception method. For example, the reception department can store the user's past document submission history in a database and analyze that data. For example, the reception department can prioritize the reception of document formats that the user has frequently submitted in the past. The reception department can also analyze the time periods in which the user has submitted documents in the past and perform reception during those time periods. Furthermore, the reception department can automatically recognize specific document formats from the user's past submission history and simplify the reception process. For example, the reception department can select the optimal reception method based on the format of documents the user has submitted in the past. This allows the reception department to select the optimal reception method based on the user's past submission history. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input past submission history data into a generating AI and have the generating AI select the optimal reception method.

[0084] The checking unit can improve the accuracy of its checks by considering the interrelationships between documents. For example, the checking unit can check citations and references within a document and perform the check while considering their interrelationships. For example, the checking unit can verify whether citations within a document are accurate. The checking unit can also check the consistency between chapters and sections within a document and perform the check while considering their interrelationships. Furthermore, the checking unit can check the consistency of data and statistical information within a document and perform the check while considering their interrelationships. For example, the checking unit can verify whether the data within a document matches that of other documents. This improves the accuracy of the check by considering the interrelationships between documents. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input interrelationship data between documents into a generating AI and have the generating AI perform the improvement of the checking accuracy.

[0085] The proposal department can adjust the level of detail of its proposals based on the importance of the document. For example, the proposal department can store the importance of the document in a database and adjust the level of detail of the proposals based on that data. For example, for important documents, the proposal department can provide detailed proposals and clearly indicate all areas for improvement. For less important documents, the proposal department can provide concise proposals and indicate only the main areas for improvement. Furthermore, the proposal department can adjust the level of detail of its proposals in stages according to the importance of the document. For example, the proposal department can select the optimal proposal method based on the importance of the document. This allows the level of detail of the proposals to be adjusted according to the importance of the document. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input document importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0086] The checking unit can estimate the user's emotions and adjust the checking criteria based on the estimated emotions. For example, the checking unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the checking unit can calculate an emotion score based on changes in facial expressions and adjust the checking criteria. The checking unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the checking unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the checking criteria. The checking unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the checking unit can calculate an emotion score based on fluctuations in heart rate and adjust the checking criteria. This allows the checking criteria to be adjusted 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0087] The proposal unit can estimate the user's emotions and adjust the way the proposal is presented based on the estimated emotions. For example, the proposal unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the proposal unit can calculate an emotion score based on changes in facial expressions and adjust the way the proposal is presented. The proposal unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the proposal unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the way the proposal is presented. The proposal unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the proposal unit can calculate an emotion score based on fluctuations in heart rate and adjust the way the proposal is presented. This allows the proposal to be presented in accordance with the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0088] The reception desk can prioritize receiving documents that are highly relevant based on the user's geographical location information. For example, the reception desk can obtain the user's geographical location information from GPS data or IP address and select highly relevant documents based on that data. For example, if the user is in a specific region, the reception desk will prioritize receiving documents related to that region. The reception desk can also prioritize receiving documents that follow region-specific rules based on the user's geographical location information. Furthermore, if the user is on the move, the reception desk can prioritize receiving documents that are most relevant to their current location. For example, the reception desk will select the most suitable document based on the user's current location. This allows the reception desk to prioritize receiving highly relevant documents based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input geographical location data into a generating AI and have the generating AI select highly relevant documents.

[0089] The checking unit can improve the accuracy of its checks by referring to relevant literature in the document. For example, the checking unit can refer to relevant literature to verify whether citations and references within the document are accurate. It can also refer to relevant literature to verify whether the content of the document is based on the latest research and data. Furthermore, it can refer to relevant literature to verify whether the claims and conclusions in the document are consistent with other literature. For example, the checking unit can select the optimal checking method based on the relevant literature in the document. This improves the accuracy of the check by referring to relevant literature in the document. Some or all of the above processes in the checking unit may be performed using AI, for example, or not. For example, the checking unit can input relevant literature data into a generating AI and have the generating AI perform the check accuracy improvement.

[0090] The proposal unit can apply different proposal algorithms depending on the document category. For example, the proposal unit stores the document category in a database and selects a proposal algorithm based on that data. For example, in the case of a technical document, the proposal unit can apply a specialized proposal algorithm to suggest technical improvements. In the case of a business document, the proposal unit can also apply a business-specific proposal algorithm to suggest business improvements. Furthermore, in the case of a legal document, the proposal unit can apply a legal-specific proposal algorithm to suggest legal improvements. For example, the proposal unit selects the optimal proposal algorithm based on the document category. This allows the appropriate proposal algorithm to be applied according to the document category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input document category data into a generating AI and have the generating AI select a proposal algorithm.

[0091] The reception unit can estimate the user's emotions and determine the priority of document files to be received based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions and determine the priority of document files. The reception unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of document files. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of document files. This allows the priority of document files to be determined according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using 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-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0092] The checking unit can perform checks while considering the attribute information of the document submitter. For example, the checking unit can store the attribute information of the document submitter in a database and perform checks based on that data. For example, the checking unit can focus on checking technical terms and content based on the submitter's field of expertise. The checking unit can also verify appropriate expressions and honorifics based on the submitter's position and job description. Furthermore, the checking unit can refer to the submitter's past submission history to focus on checking for specific mistakes or patterns. For example, the checking unit can detect specific mistakes based on the submitter's past submission history. This improves the accuracy of the check by considering the attribute information of the document submitter. Some or all of the above processes in the checking unit may be performed using AI, for example, or not using AI. For example, the checking unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of the check.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The reception desk accepts document files. Document files include, but are not limited to, PDF, Word, and text formats. The reception desk can accept document files simply by the user uploading them. Step 2: The checking unit checks the document file based on pre-programmed rules. For example, it checks whether there is a copyright notice in the document, whether confidentiality notices are properly displayed, and whether an address is included in documents intended for agents, etc. The checking unit searches for specific keywords or phrases within the document and performs checks based on the rules. Step 3: The proposal team proposes improvements to the document file checked by the checking team. For example, if the document lacks a copyright notice, they will point this out and propose a correction. Similarly, if the confidential notice is inappropriate or the address is missing, they will point this out and propose a correction. The proposal team will insert the proposed corrections into specific locations within the document and provide the user with concrete instructions on how to make the corrections.

[0095] 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.

[0096] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0097] 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.

[0098] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0099] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.).

[0111] 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.

[0112] 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. 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.

[0113] 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.

[0114] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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. 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 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.

[0130] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] 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.

[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 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.

[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 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).

[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] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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. 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.

[0146] 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.

[0147] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] [Explanation of symbols]

[0167] 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. A reception desk that accepts document files, A checking unit checks the document files received by the aforementioned receiving unit based on pre-programmed rules, A suggestion unit that proposes improvements to the document file checked by the aforementioned checking unit, Equipped with A system characterized by the following features.

2. The aforementioned checking unit is Check if there is a copyright notice in the document. The system according to feature 1.

3. The aforementioned checking unit is Verify that confidentiality is properly indicated within the document. The system according to feature 1.

4. The aforementioned checking unit is If the document is for an agency, check if it includes an address. The system according to feature 1.

5. The aforementioned proposal section is, If a document lacks a copyright notice, point this out and propose a corrected version. The system according to feature 1.

6. The aforementioned proposal section is, If the confidential designation in the document is inappropriate, point it out and propose a corrective action. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of document file acceptance based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past document submission history and select the appropriate submission method. The system according to feature 1.

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