system

Abstract

The system according to this embodiment aims to automatically extract important information from documents and report any missing information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an extraction unit, and a reporting unit. The collection unit collects documents. The analysis unit analyzes the documents collected by the collection unit. The extraction unit extracts important information from the documents analyzed by the analysis unit. The reporting unit reports any missing information based on the information extracted by the extraction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a small amount of document processing is performed manually, which is laborious and has the problem that it is difficult for the submitter to understand what should be submitted.

[0005] The system according to the embodiment aims to automatically extract important information from documents and report the insufficient content.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an extraction unit, and a reporting unit. The collection unit collects documents. The analysis unit analyzes the documents collected by the collection unit. The extraction unit extracts important information from the documents analyzed by the analysis unit. The reporting unit reports any missing information based on the information extracted by the extraction unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically extract important information from documents and report any missing information. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The document processing system according to an embodiment of the present invention is a system for automating the processing of small amounts of documents. This document processing system aims to automatically extract important information from documents using AI, diagnose requirements and report any deficiencies, and provide improvement suggestions to the submitter. Specifically, it consists of the following steps. First, the submitter submits the documents. Documents are expected to be submitted in various formats, such as text documents, images, and reference URL links. Next, the AI ​​processes the submitted documents. Text documents are used as is, images are converted into text using OCR technology, and URL links are scraped and converted into text. The AI ​​extracts important information from these converted documents and diagnoses the requirements. If necessary parts are missing, the AI ​​compiles the deficiencies into a report and provides improvement suggestions to the submitter. For example, when a submitter submits documents, the document processing system performs appropriate processing according to the document format. For example, text documents are used as is, images are converted into text using OCR technology, and URL links are scraped and converted into text. The AI ​​extracts important information from these converted documents and diagnoses the requirements. For example, AI analyzes the content of a document to check if it contains the necessary information. If necessary parts are missing, the AI ​​compiles a report on the missing parts and provides improvement suggestions to the submitter. This streamlines document processing and clarifies what needs to be submitted for the submitter. Furthermore, because the AI ​​compiles the necessary parts, document processing is standardized and reliance on individual expertise is prevented. As a result, the document processing system can automate the processing of small volumes of documents and provide submitters with efficient document processing.

[0029] The document processing system according to the embodiment comprises a collection unit, an analysis unit, an extraction unit, and a reporting unit. The collection unit collects submitted documents. For example, the collection unit receives documents uploaded by the submitter. The collection unit performs appropriate processing according to the format of the submitted documents. For example, the collection unit receives text documents as they are, and in the case of images, it uses OCR technology to convert them into text. In the case of URL links, the collection unit scrapes the linked destination and converts it into text. The analysis unit analyzes the documents collected by the collection unit. For example, the analysis unit analyzes the content of the collected documents and checks whether the necessary information is included. The analysis unit can use AI to analyze the content of the documents in detail. For example, the analysis unit uses natural language processing technology to analyze the content of the documents. The extraction unit extracts important information from the documents analyzed by the analysis unit. For example, the extraction unit extracts specific keywords or phrases from the documents. The extraction unit can use AI to accurately extract important information. For example, the extraction unit uses a machine learning algorithm to extract important information from the documents. The reporting unit reports on the missing information based on the information extracted by the extraction unit. For example, the reporting unit identifies the missing information based on the extracted information. The reporting unit can use AI to automatically compile the missing information into a report. For example, the reporting unit uses natural language generation technology to compile the missing information into a report. As a result, the document processing system according to this embodiment can improve the efficiency of document processing by automating document collection, analysis, extraction of important information, and reporting of missing information.

[0030] The data collection unit collects submitted documents. For example, the unit receives documents uploaded by the submitter. The unit processes the submitted documents appropriately according to their format. For example, the unit accepts text documents as they are, and in the case of images, it uses OCR technology to convert them into text. Specifically, by using OCR technology, it recognizes characters within images and extracts them as text data. This allows documents in image format to be treated as text data. Furthermore, in the case of URL links, the unit scrapes the linked pages and converts them into text. By using scraping technology, the content of web pages can be automatically obtained and collected as text data. By performing these processes automatically, the data collection unit can efficiently collect data regardless of the format of the submitted documents. The data collection unit also centrally manages the collected data and makes it accessible to the analysis and extraction units. The collected data is stored on a cloud server and can be linked with other systems and departments as needed. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the collection unit to efficiently collect documents in various formats, improving the overall processing efficiency of the system.

[0031] The analysis unit analyzes the documents collected by the collection unit. For example, the analysis unit analyzes the content of the collected documents to verify that they contain the necessary information. The analysis unit can use AI to analyze the content of documents in detail. Specifically, it uses natural language processing technology to analyze the content of documents. By using natural language processing technology, it can understand the context and meaning within the document and accurately extract the necessary information. For example, the analysis unit can detect specific keywords and phrases within the document and analyze how they are used in context. The analysis unit can also analyze the structure of the document and understand the content of each section and paragraph. This allows the analysis unit to understand the content of the entire document in detail and verify that it contains the necessary information. Furthermore, the analysis unit can also evaluate the content of documents by utilizing historical data and statistical information. For example, it can detect specific patterns and trends based on past document data and evaluate the content of new documents. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to quickly and accurately analyze the content of collected documents and verify that they contain the necessary information.

[0032] The extraction unit extracts important information from documents analyzed by the analysis unit. For example, the extraction unit extracts specific keywords or phrases from documents. The extraction unit can accurately extract important information using AI. Specifically, it uses machine learning algorithms to extract important information from documents. By using machine learning algorithms, it can automatically identify and extract important information within documents. For example, the extraction unit can detect specific keywords or phrases within documents and analyze how they are used in context. The extraction unit can also analyze the structure of documents and understand the content of each section and paragraph. This allows the extraction unit to understand the content of the entire document in detail and accurately extract important information. Furthermore, the extraction unit can evaluate the content of documents by utilizing historical data and statistical information. For example, it can detect specific patterns and trends based on past document data and evaluate the content of new documents. The extraction unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the extraction unit to quickly and accurately analyze the content of collected documents and extract important information.

[0033] The reporting unit reports on any missing information based on the information extracted by the extraction unit. For example, the reporting unit identifies missing information based on the extracted information. The reporting unit can use AI to automatically compile the missing information into a report. Specifically, it uses natural language generation technology to compile the missing information into a report. By using natural language generation technology, the reporting unit can automatically generate a report based on the extracted information. For example, the reporting unit identifies missing information based on the extracted information and compiles it into a report. The reporting unit can also automatically update the report content to reflect the latest information. This allows the reporting unit to always provide reports based on the latest information. Furthermore, the reporting unit can centrally manage the report content and collaborate with other systems and departments as needed. This allows the reporting unit to generate reports efficiently and effectively, improving the overall system performance. In addition, the reporting unit can analyze the report content and identify areas for improvement. This allows the reporting unit to continuously improve the report content and improve the overall system performance.

[0034] The OCR unit can extract text from images. For example, the OCR unit can scan submitted image documents and convert them into text data using OCR technology. The OCR unit can extract text from images with high accuracy using AI. For example, the OCR unit can use deep learning algorithms to recognize characters in images and convert them into text data. The OCR unit can extract text from various image formats, including handwritten and printed characters. For example, the OCR unit can scan handwritten documents and convert them into text data using handwritten character recognition technology. The OCR unit can also scan printed documents and convert them into text data using printed character recognition technology. This allows the OCR unit to process image documents as well. Some or all of the above-described processes in the OCR unit may be performed using AI or not. For example, the OCR unit can input image data into a generating AI and have the generating AI generate text data from the image data.

[0035] The scraping unit can collect information from URL links. For example, the scraping unit can analyze submitted URL links and collect information from the linked web pages. The scraping unit can efficiently collect linked information using AI. For example, the scraping unit can extract text data from linked web pages using web scraping technology. The scraping unit can transcribe the linked information into text and provide it to the analysis unit. For example, the scraping unit can convert the content of the linked web page into text data and send it to the analysis unit. This allows the scraping unit to process documents in link format as well. Some or all of the above processing in the scraping unit may be performed using AI or not. For example, the scraping unit can input URL links into a generating AI and have the generating AI collect the linked information.

[0036] The proposal department can make improvement suggestions to the submitter based on the missing information. For example, the proposal department can identify missing information based on the information extracted by the extraction department and make improvement suggestions to the submitter. The proposal department can use AI to generate specific improvement suggestions based on the missing information. For example, the proposal department can use natural language generation technology to make specific improvement suggestions to the submitter. The proposal department makes improvement suggestions to enable the submitter to submit appropriate documents. For example, the proposal department can specifically indicate to the submitter what information is missing and suggest how to improve it. In this way, the proposal department can support the submitter in submitting appropriate documents. 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 the missing information into a generation AI and have the generation AI generate improvement suggestions.

[0037] The collection unit can analyze a user's past document submission history and select the optimal collection method. For example, the collection unit can analyze the format of documents previously submitted by the user and prompt them to submit documents in a similar format. The collection unit can use AI to analyze a user's past document submission history in detail. For example, the collection unit can use machine learning algorithms to analyze the user's submission history and select the optimal collection method. The collection unit can analyze the frequency of documents previously submitted by the user and suggest the optimal collection frequency. For example, the collection unit can analyze the content of documents previously submitted by the user and prompt them to submit related documents. In this way, the collection unit can select the optimal collection method by analyzing the user's past document submission history. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past submission history data into a generating AI and have the generating AI select the optimal collection method.

[0038] The collection unit can filter documents based on the user's current projects and areas of interest during collection. For example, the collection unit can collect only documents related to the user's current project. The collection unit can use AI to analyze the user's projects and areas of interest and prioritize the collection of relevant documents. For example, the collection unit prioritizes the collection of relevant documents based on the user's areas of interest. The collection unit can collect necessary documents according to the progress of the user's project. For example, the collection unit analyzes the progress of the user's project and collects relevant documents at the appropriate time. This allows the collection unit to collect highly relevant documents by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's project information into a generating AI and have the generating AI perform the filtering of relevant documents.

[0039] The collection unit can prioritize the collection of highly relevant documents by considering the user's geographical location information when collecting documents. For example, if the user is in a specific region, the collection unit will prioritize the collection of documents related to that region. The collection unit can use AI to analyze the user's geographical location information and collect highly relevant documents. For example, if the user is on the move, the collection unit will collect documents related to their current location. If the user is in a specific location, the collection unit can prioritize the collection of documents related to that location. In this way, the collection unit can efficiently collect documents by prioritizing the collection of highly relevant documents by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant documents.

[0040] The data collection unit can analyze a user's social media activity and collect relevant documents when collecting documents. For example, the data collection unit can collect documents related to content mentioned by the user on social media. The data collection unit can use AI to analyze a user's social media activity in detail. For example, the data collection unit can use social media analysis technology to analyze a user's activity and collect relevant documents. The data collection unit can collect documents related to accounts that a user follows on social media. For example, the data collection unit can collect documents related to groups that a user participates in on social media. This allows the data collection unit to efficiently collect relevant documents by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's social media data into a generating AI and have the generating AI perform the collection of relevant documents.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the documents during the analysis. For example, the analysis unit performs a detailed analysis on documents of high importance. The analysis unit can use AI to evaluate the importance of documents and adjust the level of detail of the analysis. For example, the analysis unit performs a simplified analysis on documents of low importance. The analysis unit can adjust the depth of the analysis according to the importance. For example, the analysis unit performs a detailed analysis on documents of high importance and a simplified analysis on documents of low importance. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the documents. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input document importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the document category during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical documents. The analysis unit can use AI to select the optimal analysis algorithm according to the document category. For example, the analysis unit can apply a legal analysis algorithm to legal documents. The analysis unit can apply a financial analysis algorithm to financial documents. This allows the analysis unit to perform highly accurate analysis by applying different analysis algorithms according to the document category. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input document category data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the submission date of the documents during the analysis. For example, the analysis unit may prioritize the analysis of documents with approaching submission deadlines. The analysis unit can use AI to evaluate the submission date of documents and determine the priority of analysis. For example, the analysis unit may adjust the order of analysis according to the submission date. The analysis unit can set the priority of analysis based on the submission date. This allows the analysis unit to perform efficient analysis by determining the priority of analysis based on the submission date of the documents. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit may input document submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the documents during analysis. For example, the analysis unit prioritizes the analysis of highly relevant documents. The analysis unit can use AI to evaluate the relevance of documents and adjust the order of analysis. For example, the analysis unit adjusts the order of analysis according to the relevance. The analysis unit can set analysis priorities based on relevance. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the documents. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input document relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The extraction unit can improve the accuracy of extraction by considering the interrelationships between documents during the extraction process. For example, the extraction unit analyzes the relationships between documents and extracts important information. The extraction unit can use AI to analyze the interrelationships between documents in detail. For example, the extraction unit can use machine learning algorithms to evaluate the interrelationships between documents and improve the accuracy of extraction. The extraction unit can set extraction criteria based on the relationships between documents. For example, the extraction unit improves the accuracy of extraction by considering the interrelationships between documents. As a result, the extraction unit can accurately extract important information by improving the accuracy of extraction by considering the interrelationships between documents. Some or all of the above-described processes in the extraction unit may be performed using AI or not. For example, the extraction unit can input interrelationship data between documents into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0046] The extraction unit can perform extraction while considering the attribute information of the document submitter. For example, the extraction unit can extract important information according to the submitter's job title. The extraction unit can use AI to analyze the submitter's attribute information in detail. For example, the extraction unit can extract relevant information based on the submitter's field of expertise. The extraction unit can set extraction criteria by referring to the submitter's past submission history. For example, the extraction unit can extract important information according to the submitter's job title. In this way, the extraction unit can extract information that is important to the submitter by considering the attribute information of the document submitter. Some or all of the above processing in the extraction unit may be performed using AI or not. For example, the extraction unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the setting of extraction criteria.

[0047] The extraction unit can perform extraction while considering the geographical distribution of documents. For example, the extraction unit can prioritize the extraction of geographically related documents. The extraction unit can use AI to analyze the geographical distribution of documents in detail. For example, the extraction unit can use machine learning algorithms to evaluate the geographical distribution of documents and set extraction criteria. The extraction unit can improve the accuracy of extraction based on geographical distribution. For example, the extraction unit prioritizes the extraction of geographically related documents. This allows the extraction unit to extract highly relevant information by considering the geographical distribution of documents. Some or all of the above processing in the extraction unit may be performed using AI or not. For example, the extraction unit can input geographical distribution data of documents into a generating AI and have the generating AI set the extraction criteria.

[0048] The extraction unit can improve the accuracy of its extraction by referring to related literature in the document during the extraction process. For example, the extraction unit can extract important information by referring to related literature. The extraction unit can use AI to analyze related literature in detail. For example, the extraction unit can use machine learning algorithms to evaluate related literature and set extraction criteria. The extraction unit can improve the accuracy of its extraction based on the related literature. For example, the extraction unit can extract important information by referring to related literature. In this way, the extraction unit can accurately extract important information by improving the accuracy of its extraction by referring to related literature in the document. Some or all of the above processes in the extraction unit may be performed using AI or not. For example, the extraction unit can input related literature data into a generating AI and have the generating AI set the extraction criteria.

[0049] The reporting unit can optimize the current report by referring to past report data when creating a report. For example, the reporting unit can analyze past report data and reflect the findings in the current report. The reporting unit can use AI to analyze past report data in detail. For example, the reporting unit can use machine learning algorithms to evaluate past report data and optimize the current report. The reporting unit can improve the accuracy of reports by referring to past report data. For example, the reporting unit can optimize the report format based on past report data. In this way, the reporting unit can provide highly accurate reports by optimizing the current report by referring to past report data. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input past report data into a generation AI and have the generation AI perform the optimization of the current report.

[0050] The reporting unit can apply different report formats to different document categories when creating reports. For example, it can apply a technical report format to technical documents. The reporting unit can use AI to select the most appropriate report format according to the document category. For example, it can apply a legal report format to legal documents. It can apply a financial report format to financial documents. In this way, the reporting unit can provide reports in the appropriate format by applying different report formats to different document categories. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input document category data into a generating AI and have the generating AI select the most appropriate report format.

[0051] The reporting department can prioritize reports based on the submission dates of the documents when creating them. For example, the reporting department can prioritize reporting on documents with approaching deadlines. The reporting department can use AI to evaluate the submission dates of documents and determine the priority of reports. For example, the reporting department can adjust the order of reports according to the submission dates. The reporting department can set report priorities based on the submission dates. This allows the reporting department to create reports efficiently by determining the priority of reports based on the submission dates of the documents. Some or all of the above processes in the reporting department may be performed using AI or not. For example, the reporting department can input document submission date data into a generating AI and have the generating AI perform the determination of report priorities.

[0052] The reporting unit can create reports by referring to relevant market data in the document during the report creation process. For example, the reporting unit can enrich the report content by referring to relevant market data. The reporting unit can use AI to analyze relevant market data in detail. For example, the reporting unit can use machine learning algorithms to evaluate relevant market data and improve the accuracy of the report. The reporting unit can optimize the report format based on market data. For example, the reporting unit can enrich the report content by referring to relevant market data. This allows the reporting unit to provide highly accurate reports by creating reports by referring to relevant market data in the document. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input relevant market data into a generating AI and have the generating AI create the report.

[0053] The OCR unit can select the optimal OCR algorithm based on image quality during OCR processing. For example, the OCR unit applies a high-precision OCR algorithm to high-quality images. The OCR unit can use AI to evaluate image quality and select the optimal OCR algorithm. For example, the OCR unit applies OCR after noise reduction to low-quality images. The OCR unit can select an appropriate OCR algorithm according to image quality. In this way, the OCR unit can provide highly accurate OCR by selecting the optimal OCR algorithm based on image quality. Some or all of the above processing in the OCR unit may be performed using AI or not. For example, the OCR unit can input image quality data into a generating AI and have the generating AI select the optimal OCR algorithm.

[0054] The OCR unit can apply different OCR methods depending on the image category during OCR processing. For example, the OCR unit applies an OCR method specialized for handwritten character recognition to handwritten characters. The OCR unit can use AI to select the optimal OCR method according to the image category. For example, the OCR unit applies an OCR method specialized for printed character recognition to printed characters. The OCR unit can select the optimal OCR method according to the image category. In this way, the OCR unit can provide highly accurate OCR by applying different OCR methods according to the image category. Some or all of the above processing in the OCR unit may be performed using AI or not. For example, the OCR unit can input image category data into a generating AI and have the generating AI select the optimal OCR method.

[0055] The OCR unit can perform OCR while considering the geographical information of the image. For example, the OCR unit prioritizes the processing of geographically relevant information. The OCR unit can use AI to analyze the geographical information of the image in detail. For example, the OCR unit can use machine learning algorithms to evaluate the geographical information of the image and improve the accuracy of the OCR. The OCR unit can display the OCR results based on the geographical information. For example, the OCR unit prioritizes the processing of geographically relevant information. As a result, the OCR unit can provide highly relevant information by performing OCR while considering the geographical information of the image. Some or all of the above processing in the OCR unit may be performed using AI or not. For example, the OCR unit can input the geographical information data of the image into a generating AI and have the generating AI perform OCR accuracy improvement.

[0056] The OCR unit can improve the accuracy of OCR by referring to related literature in the image during OCR processing. For example, the OCR unit can improve the accuracy of OCR by referring to related literature. The OCR unit can use AI to analyze related literature in detail. For example, the OCR unit can use machine learning algorithms to evaluate related literature and supplement the OCR results. The OCR unit can display the OCR results based on the related literature. For example, the OCR unit can improve the accuracy of OCR by referring to related literature. In this way, the OCR unit can provide highly accurate information by improving the accuracy of OCR by referring to related literature in the image. Some or all of the above processing in the OCR unit may be performed using AI or not using AI. For example, the OCR unit can input related literature data into a generating AI and have the generating AI perform the OCR accuracy improvement.

[0057] The scraping unit can select the optimal scraping method based on the quality of the linked site during scraping. For example, the scraping unit applies a high-precision scraping method to high-quality linked sites. The scraping unit can use AI to evaluate the quality of linked sites and select the optimal scraping method. For example, the scraping unit applies scraping to low-quality linked sites after noise reduction. The scraping unit can select an appropriate scraping method according to the quality of the linked site. As a result, the scraping unit can collect highly accurate information by selecting the optimal scraping method based on the quality of the linked site. Some or all of the above processing in the scraping unit may be performed using AI or not. For example, the scraping unit can input linked site quality data into a generating AI and have the generating AI select the optimal scraping method.

[0058] The scraping unit can apply different scraping methods depending on the category of the linked document during scraping. For example, the scraping unit can apply a technical scraping method to links leading to technical documents. The scraping unit can use AI to select the optimal scraping method according to the category of the linked document. For example, the scraping unit can apply a legal scraping method to links leading to legal documents. The scraping unit can apply a financial scraping method to links leading to financial documents. In this way, the scraping unit can collect highly accurate information by applying different scraping methods according to the category of the linked document. Some or all of the above processing in the scraping unit may be performed using AI or not. For example, the scraping unit can input linked document category data into a generating AI and have the generating AI select the optimal scraping method.

[0059] The scraping unit can perform scraping while considering the geographical information of the linked sites. For example, the scraping unit can prioritize scraping geographically relevant links. The scraping unit can use AI to analyze the geographical information of the linked sites in detail. For example, the scraping unit can use machine learning algorithms to evaluate the geographical information of the linked sites and improve the accuracy of the scraping. The scraping unit can display the scraping results based on the geographical information. For example, the scraping unit prioritizes scraping geographically relevant links. This allows the scraping unit to collect highly relevant information by considering the geographical information of the linked sites during scraping. Some or all of the above processing in the scraping unit may be performed using AI or not. For example, the scraping unit can input geographical information data of the linked sites into a generating AI and have the generating AI perform the task of improving the accuracy of the scraping.

[0060] The scraping unit can improve the accuracy of scraping by referring to related literature linked to during the scraping process. For example, the scraping unit can improve the accuracy of scraping by referring to related literature. The scraping unit can use AI to analyze related literature in detail. For example, the scraping unit can use machine learning algorithms to evaluate related literature and supplement the scraping results. The scraping unit can display the scraping results based on the related literature. For example, the scraping unit can improve the accuracy of scraping by referring to related literature. In this way, the scraping unit can collect highly accurate information by improving the accuracy of scraping by referring to related literature linked to. Some or all of the above processing in the scraping unit may be performed using AI or not. For example, the scraping unit can input related literature data into a generating AI and have the generating AI perform the scraping accuracy improvement.

[0061] The proposal department can adjust the level of detail of a proposal based on the importance of the document. For example, the proposal department will provide a detailed proposal for highly important documents. The proposal department can use AI to evaluate the importance of documents and adjust the level of detail of proposals. For example, the proposal department will provide a simplified proposal for less important documents. The proposal department can adjust the depth of the proposal according to its importance. For example, the proposal department will provide a detailed proposal for highly important documents and a simplified proposal for less important documents. This allows the proposal department to make efficient proposals by adjusting the level of detail of proposals based on the importance of the documents. 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 proposals.

[0062] The proposal department can apply different proposal algorithms depending on the document category when making a proposal. For example, the proposal department can apply a technical proposal algorithm to technical documents. The proposal department can use AI to select the optimal proposal algorithm depending on the document category. For example, the proposal department can apply a legal proposal algorithm to legal documents. The proposal department can apply a financial proposal algorithm to financial documents. This allows the proposal department to make highly accurate proposals by applying different proposal algorithms depending on the document category. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input document category data into a generating AI and have the generating AI select the optimal proposal algorithm.

[0063] The proposal department can determine the priority of proposals based on the submission timing of the documents. For example, the proposal department may prioritize proposals with approaching submission deadlines. The proposal department can use AI to evaluate the submission timing of documents and determine the priority of proposals. For example, the proposal department may adjust the order of proposals according to the submission timing. The proposal department can set the priority of proposals based on the submission timing. This allows the proposal department to make efficient proposals by determining the priority of proposals based on the submission timing of documents. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department may input document submission timing data into a generating AI and have the generating AI perform the determination of proposal priority.

[0064] The proposal department can adjust the order of proposals based on the relevance of the documents during the proposal process. For example, the proposal department can prioritize proposing documents with high relevance. The proposal department can use AI to evaluate the relevance of documents and adjust the order of proposals. For example, the proposal department can adjust the order of proposals according to their relevance. The proposal department can set priority for proposals based on their relevance. This allows the proposal department to make efficient proposals by adjusting the order of proposals based on the relevance of the documents. 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 relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The collection unit can analyze a user's past document submission history and select the optimal collection method. For example, the collection unit can analyze the format of documents previously submitted by the user and prompt them to submit documents in a similar format. The collection unit can use AI to analyze a user's past document submission history in detail. For example, the collection unit can use machine learning algorithms to analyze the user's submission history and select the optimal collection method. The collection unit can analyze the frequency of documents previously submitted by the user and suggest the optimal collection frequency. For example, the collection unit can analyze the content of documents previously submitted by the user and prompt them to submit related documents. In this way, the collection unit can select the optimal collection method by analyzing the user's past document submission history. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past submission history data into a generating AI and have the generating AI select the optimal collection method.

[0067] The OCR unit can select the optimal OCR algorithm based on image quality during OCR processing. For example, the OCR unit applies a high-precision OCR algorithm to high-quality images. The OCR unit can use AI to evaluate image quality and select the optimal OCR algorithm. For example, the OCR unit applies OCR after noise reduction to low-quality images. The OCR unit can select an appropriate OCR algorithm according to image quality. In this way, the OCR unit can provide highly accurate OCR by selecting the optimal OCR algorithm based on image quality. Some or all of the above processing in the OCR unit may be performed using AI or not. For example, the OCR unit can input image quality data into a generating AI and have the generating AI select the optimal OCR algorithm.

[0068] The scraping unit can select the optimal scraping method based on the quality of the linked site during scraping. For example, the scraping unit applies a high-precision scraping method to high-quality linked sites. The scraping unit can use AI to evaluate the quality of linked sites and select the optimal scraping method. For example, the scraping unit applies scraping to low-quality linked sites after noise reduction. The scraping unit can select an appropriate scraping method according to the quality of the linked site. As a result, the scraping unit can collect highly accurate information by selecting the optimal scraping method based on the quality of the linked site. Some or all of the above processing in the scraping unit may be performed using AI or not. For example, the scraping unit can input linked site quality data into a generating AI and have the generating AI select the optimal scraping method.

[0069] The proposal department can adjust the level of detail of a proposal based on the importance of the document. For example, the proposal department will provide a detailed proposal for highly important documents. The proposal department can use AI to evaluate the importance of documents and adjust the level of detail of proposals. For example, the proposal department will provide a simplified proposal for less important documents. The proposal department can adjust the depth of the proposal according to its importance. For example, the proposal department will provide a detailed proposal for highly important documents and a simplified proposal for less important documents. This allows the proposal department to make efficient proposals by adjusting the level of detail of proposals based on the importance of the documents. 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 proposals.

[0070] The reporting unit can optimize the current report by referring to past report data when creating a report. For example, the reporting unit can analyze past report data and reflect the findings in the current report. The reporting unit can use AI to analyze past report data in detail. For example, the reporting unit can use machine learning algorithms to evaluate past report data and optimize the current report. The reporting unit can improve the accuracy of reports by referring to past report data. For example, the reporting unit can optimize the report format based on past report data. In this way, the reporting unit can provide highly accurate reports by optimizing the current report by referring to past report data. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input past report data into a generation AI and have the generation AI perform the optimization of the current report.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The collection unit collects the submitted documents. The collection unit receives documents uploaded by the submitter, for example, and processes them appropriately according to their format. Text documents are received as they are, images are converted into text using OCR technology, and URL links are scraped and converted into text. Step 2: The analysis unit analyzes the documents collected by the collection unit. The analysis unit analyzes the contents of the collected documents and verifies whether they contain the necessary information. The analysis unit uses AI and natural language processing technology to analyze the contents of the documents in detail. Step 3: The extraction unit extracts important information from the documents analyzed by the analysis unit. The extraction unit extracts specific keywords and phrases and uses AI and machine learning algorithms to accurately extract important information. Step 4: The reporting unit reports on the missing information based on the information extracted by the extraction unit. The reporting unit identifies the missing information based on the extracted information and automatically compiles the missing information into a report using AI and natural language generation technology.

[0073] (Example of form 2) The document processing system according to an embodiment of the present invention is a system for automating the processing of small amounts of documents. This document processing system aims to automatically extract important information from documents using AI, diagnose requirements and report any deficiencies, and provide improvement suggestions to the submitter. Specifically, it consists of the following steps. First, the submitter submits the documents. Documents are expected to be submitted in various formats, such as text documents, images, and reference URL links. Next, the AI ​​processes the submitted documents. Text documents are used as is, images are converted into text using OCR technology, and URL links are scraped and converted into text. The AI ​​extracts important information from these converted documents and diagnoses the requirements. If necessary parts are missing, the AI ​​compiles the deficiencies into a report and provides improvement suggestions to the submitter. For example, when a submitter submits documents, the document processing system performs appropriate processing according to the document format. For example, text documents are used as is, images are converted into text using OCR technology, and URL links are scraped and converted into text. The AI ​​extracts important information from these converted documents and diagnoses the requirements. For example, AI analyzes the content of a document to check if it contains the necessary information. If necessary parts are missing, the AI ​​compiles a report on the missing parts and provides improvement suggestions to the submitter. This streamlines document processing and clarifies what needs to be submitted for the submitter. Furthermore, because the AI ​​compiles the necessary parts, document processing is standardized and reliance on individual expertise is prevented. As a result, the document processing system can automate the processing of small volumes of documents and provide submitters with efficient document processing.

[0074] The document processing system according to the embodiment comprises a collection unit, an analysis unit, an extraction unit, and a reporting unit. The collection unit collects submitted documents. For example, the collection unit receives documents uploaded by the submitter. The collection unit performs appropriate processing according to the format of the submitted documents. For example, the collection unit receives text documents as they are, and in the case of images, it uses OCR technology to convert them into text. In the case of URL links, the collection unit scrapes the linked destination and converts it into text. The analysis unit analyzes the documents collected by the collection unit. For example, the analysis unit analyzes the content of the collected documents and checks whether the necessary information is included. The analysis unit can use AI to analyze the content of the documents in detail. For example, the analysis unit uses natural language processing technology to analyze the content of the documents. The extraction unit extracts important information from the documents analyzed by the analysis unit. For example, the extraction unit extracts specific keywords or phrases from the documents. The extraction unit can use AI to accurately extract important information. For example, the extraction unit uses a machine learning algorithm to extract important information from the documents. The reporting unit reports on the missing information based on the information extracted by the extraction unit. For example, the reporting unit identifies the missing information based on the extracted information. The reporting unit can use AI to automatically compile the missing information into a report. For example, the reporting unit uses natural language generation technology to compile the missing information into a report. As a result, the document processing system according to this embodiment can improve the efficiency of document processing by automating document collection, analysis, extraction of important information, and reporting of missing information.

[0075] The data collection unit collects submitted documents. For example, the unit receives documents uploaded by the submitter. The unit processes the submitted documents appropriately according to their format. For example, the unit accepts text documents as they are, and in the case of images, it uses OCR technology to convert them into text. Specifically, by using OCR technology, it recognizes characters within images and extracts them as text data. This allows documents in image format to be treated as text data. Furthermore, in the case of URL links, the unit scrapes the linked pages and converts them into text. By using scraping technology, the content of web pages can be automatically obtained and collected as text data. By performing these processes automatically, the data collection unit can efficiently collect data regardless of the format of the submitted documents. The data collection unit also centrally manages the collected data and makes it accessible to the analysis and extraction units. The collected data is stored on a cloud server and can be linked with other systems and departments as needed. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the collection unit to efficiently collect documents in various formats, improving the overall processing efficiency of the system.

[0076] The analysis unit analyzes the documents collected by the collection unit. For example, the analysis unit analyzes the content of the collected documents to verify that they contain the necessary information. The analysis unit can use AI to analyze the content of documents in detail. Specifically, it uses natural language processing technology to analyze the content of documents. By using natural language processing technology, it can understand the context and meaning within the document and accurately extract the necessary information. For example, the analysis unit can detect specific keywords and phrases within the document and analyze how they are used in context. The analysis unit can also analyze the structure of the document and understand the content of each section and paragraph. This allows the analysis unit to understand the content of the entire document in detail and verify that it contains the necessary information. Furthermore, the analysis unit can also evaluate the content of documents by utilizing historical data and statistical information. For example, it can detect specific patterns and trends based on past document data and evaluate the content of new documents. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to quickly and accurately analyze the content of collected documents and verify that they contain the necessary information.

[0077] The extraction unit extracts important information from documents analyzed by the analysis unit. For example, the extraction unit extracts specific keywords or phrases from documents. The extraction unit can accurately extract important information using AI. Specifically, it uses machine learning algorithms to extract important information from documents. By using machine learning algorithms, it can automatically identify and extract important information within documents. For example, the extraction unit can detect specific keywords or phrases within documents and analyze how they are used in context. The extraction unit can also analyze the structure of documents and understand the content of each section and paragraph. This allows the extraction unit to understand the content of the entire document in detail and accurately extract important information. Furthermore, the extraction unit can evaluate the content of documents by utilizing historical data and statistical information. For example, it can detect specific patterns and trends based on past document data and evaluate the content of new documents. The extraction unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the extraction unit to quickly and accurately analyze the content of collected documents and extract important information.

[0078] The reporting unit reports on any missing information based on the information extracted by the extraction unit. For example, the reporting unit identifies missing information based on the extracted information. The reporting unit can use AI to automatically compile the missing information into a report. Specifically, it uses natural language generation technology to compile the missing information into a report. By using natural language generation technology, the reporting unit can automatically generate a report based on the extracted information. For example, the reporting unit identifies missing information based on the extracted information and compiles it into a report. The reporting unit can also automatically update the report content to reflect the latest information. This allows the reporting unit to always provide reports based on the latest information. Furthermore, the reporting unit can centrally manage the report content and collaborate with other systems and departments as needed. This allows the reporting unit to generate reports efficiently and effectively, improving the overall system performance. In addition, the reporting unit can analyze the report content and identify areas for improvement. This allows the reporting unit to continuously improve the report content and improve the overall system performance.

[0079] The OCR unit can extract text from images. For example, the OCR unit can scan submitted image documents and convert them into text data using OCR technology. The OCR unit can extract text from images with high accuracy using AI. For example, the OCR unit can use deep learning algorithms to recognize characters in images and convert them into text data. The OCR unit can extract text from various image formats, including handwritten and printed characters. For example, the OCR unit can scan handwritten documents and convert them into text data using handwritten character recognition technology. The OCR unit can also scan printed documents and convert them into text data using printed character recognition technology. This allows the OCR unit to process image documents as well. Some or all of the above-described processes in the OCR unit may be performed using AI or not. For example, the OCR unit can input image data into a generating AI and have the generating AI generate text data from the image data.

[0080] The scraping unit can collect information from URL links. For example, the scraping unit can analyze submitted URL links and collect information from the linked web pages. The scraping unit can efficiently collect linked information using AI. For example, the scraping unit can extract text data from linked web pages using web scraping technology. The scraping unit can transcribe the linked information into text and provide it to the analysis unit. For example, the scraping unit can convert the content of the linked web page into text data and send it to the analysis unit. This allows the scraping unit to process documents in link format as well. Some or all of the above processing in the scraping unit may be performed using AI or not. For example, the scraping unit can input URL links into a generating AI and have the generating AI collect the linked information.

[0081] The proposal department can make improvement suggestions to the submitter based on the missing information. For example, the proposal department can identify missing information based on the information extracted by the extraction department and make improvement suggestions to the submitter. The proposal department can use AI to generate specific improvement suggestions based on the missing information. For example, the proposal department can use natural language generation technology to make specific improvement suggestions to the submitter. The proposal department makes improvement suggestions to enable the submitter to submit appropriate documents. For example, the proposal department can specifically indicate to the submitter what information is missing and suggest how to improve it. In this way, the proposal department can support the submitter in submitting appropriate documents. 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 the missing information into a generation AI and have the generation AI generate improvement suggestions.

[0082] The collection unit can estimate the user's emotions and adjust the timing of document collection based on the estimated emotions. For example, the collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The collection unit can use AI to estimate the user's emotions in real time. For example, the collection unit can use facial recognition technology to estimate the user's emotions. Based on the estimated emotions, the collection unit adjusts the timing of document collection. For example, if the user is stressed, the collection unit can delay the collection timing to allow the user to submit documents in a relaxed state. Conversely, if the user is in a hurry, the collection unit can speed up the collection timing to collect documents quickly. Furthermore, if the user is focused, the collection unit can collect documents at that time to process them efficiently. In this way, the collection unit can reduce the user's burden by adjusting the timing of document collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0083] The collection unit can analyze a user's past document submission history and select the optimal collection method. For example, the collection unit can analyze the format of documents previously submitted by the user and prompt them to submit documents in a similar format. The collection unit can use AI to analyze a user's past document submission history in detail. For example, the collection unit can use machine learning algorithms to analyze the user's submission history and select the optimal collection method. The collection unit can analyze the frequency of documents previously submitted by the user and suggest the optimal collection frequency. For example, the collection unit can analyze the content of documents previously submitted by the user and prompt them to submit related documents. In this way, the collection unit can select the optimal collection method by analyzing the user's past document submission history. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past submission history data into a generating AI and have the generating AI select the optimal collection method.

[0084] The collection unit can filter documents based on the user's current projects and areas of interest during collection. For example, the collection unit can collect only documents related to the user's current project. The collection unit can use AI to analyze the user's projects and areas of interest and prioritize the collection of relevant documents. For example, the collection unit prioritizes the collection of relevant documents based on the user's areas of interest. The collection unit can collect necessary documents according to the progress of the user's project. For example, the collection unit analyzes the progress of the user's project and collects relevant documents at the appropriate time. This allows the collection unit to collect highly relevant documents by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's project information into a generating AI and have the generating AI perform the filtering of relevant documents.

[0085] The data collection unit can estimate the user's emotions and determine the priority of documents to collect based on the estimated emotions. For example, the unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can use AI to estimate the user's emotions in real time. For example, the unit can use facial recognition technology to estimate the user's emotions. Based on the estimated emotions, the data collection unit determines the priority of documents to collect. For example, if the user is stressed, the data collection unit will postpone collecting less important documents. Conversely, if the user is relaxed, the data collection unit can prioritize collecting more important documents. Furthermore, if the user is focused, the data collection unit can prioritize collecting complex documents. This allows the data collection unit to reduce the user's burden by prioritizing documents according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0086] The collection unit can prioritize the collection of highly relevant documents by considering the user's geographical location information when collecting documents. For example, if the user is in a specific region, the collection unit will prioritize the collection of documents related to that region. The collection unit can use AI to analyze the user's geographical location information and collect highly relevant documents. For example, if the user is on the move, the collection unit will collect documents related to their current location. If the user is in a specific location, the collection unit can prioritize the collection of documents related to that location. In this way, the collection unit can efficiently collect documents by prioritizing the collection of highly relevant documents by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant documents.

[0087] The data collection unit can analyze a user's social media activity and collect relevant documents when collecting documents. For example, the data collection unit can collect documents related to content mentioned by the user on social media. The data collection unit can use AI to analyze a user's social media activity in detail. For example, the data collection unit can use social media analysis technology to analyze a user's activity and collect relevant documents. The data collection unit can collect documents related to accounts that a user follows on social media. For example, the data collection unit can collect documents related to groups that a user participates in on social media. This allows the data collection unit to efficiently collect relevant documents by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's social media data into a generating AI and have the generating AI perform the collection of relevant documents.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can estimate the user's emotions in real time using AI. For example, the analysis unit can estimate the user's emotions using facial recognition technology. The analysis unit adjusts the presentation of the analysis based on the estimated emotions of the user. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. Also, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the documents during the analysis. For example, the analysis unit performs a detailed analysis on documents of high importance. The analysis unit can use AI to evaluate the importance of documents and adjust the level of detail of the analysis. For example, the analysis unit performs a simplified analysis on documents of low importance. The analysis unit can adjust the depth of the analysis according to the importance. For example, the analysis unit performs a detailed analysis on documents of high importance and a simplified analysis on documents of low importance. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the documents. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input document importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the document category during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical documents. The analysis unit can use AI to select the optimal analysis algorithm according to the document category. For example, the analysis unit can apply a legal analysis algorithm to legal documents. The analysis unit can apply a financial analysis algorithm to financial documents. This allows the analysis unit to perform highly accurate analysis by applying different analysis algorithms according to the document category. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input document category data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can use AI to estimate the user's emotions in real time. For example, the analysis unit can use facial recognition technology to estimate the user's emotions. The analysis unit adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. In this way, the analysis unit can provide the user with an analysis result of an appropriate length by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0092] The analysis unit can determine the priority of analysis based on the submission date of the documents during the analysis. For example, the analysis unit may prioritize the analysis of documents with approaching submission deadlines. The analysis unit can use AI to evaluate the submission date of documents and determine the priority of analysis. For example, the analysis unit may adjust the order of analysis according to the submission date. The analysis unit can set the priority of analysis based on the submission date. This allows the analysis unit to perform efficient analysis by determining the priority of analysis based on the submission date of the documents. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit may input document submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the documents during analysis. For example, the analysis unit prioritizes the analysis of highly relevant documents. The analysis unit can use AI to evaluate the relevance of documents and adjust the order of analysis. For example, the analysis unit adjusts the order of analysis according to the relevance. The analysis unit can set analysis priorities based on relevance. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the documents. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input document relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0094] The extraction unit can estimate the user's emotions and adjust the extraction criteria based on the estimated emotions. For example, the extraction unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The extraction unit can estimate the user's emotions in real time using AI. For example, the extraction unit can estimate the user's emotions using facial recognition technology. The extraction unit adjusts the extraction criteria based on the estimated emotions of the user. For example, if the user is tense, the extraction unit will extract only important information. Also, if the user is relaxed, the extraction unit can extract detailed information. Furthermore, if the user is in a hurry, the extraction unit can extract concise information. In this way, the extraction unit can extract information appropriate for the user by adjusting the extraction criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using AI or not. For example, the extraction unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0095] The extraction unit can improve the accuracy of extraction by considering the interrelationships between documents during the extraction process. For example, the extraction unit analyzes the relationships between documents and extracts important information. The extraction unit can use AI to analyze the interrelationships between documents in detail. For example, the extraction unit can use machine learning algorithms to evaluate the interrelationships between documents and improve the accuracy of extraction. The extraction unit can set extraction criteria based on the relationships between documents. For example, the extraction unit improves the accuracy of extraction by considering the interrelationships between documents. As a result, the extraction unit can accurately extract important information by improving the accuracy of extraction by considering the interrelationships between documents. Some or all of the above-described processes in the extraction unit may be performed using AI or not. For example, the extraction unit can input interrelationship data between documents into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0096] The extraction unit can perform extraction while considering the attribute information of the document submitter. For example, the extraction unit can extract important information according to the submitter's job title. The extraction unit can use AI to analyze the submitter's attribute information in detail. For example, the extraction unit can extract relevant information based on the submitter's field of expertise. The extraction unit can set extraction criteria by referring to the submitter's past submission history. For example, the extraction unit can extract important information according to the submitter's job title. In this way, the extraction unit can extract information that is important to the submitter by considering the attribute information of the document submitter. Some or all of the above processing in the extraction unit may be performed using AI or not. For example, the extraction unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the setting of extraction criteria.

[0097] The extraction unit can estimate the user's emotions and adjust the order in which the extraction results are displayed based on the estimated emotions. For example, the extraction unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The extraction unit can estimate the user's emotions in real time using AI. For example, the extraction unit can estimate the user's emotions using facial recognition technology. The extraction unit adjusts the order in which the extraction results are displayed based on the estimated emotions of the user. For example, if the user is tense, the extraction unit can display important information first. Also, if the user is relaxed, the extraction unit can display detailed information sequentially. Furthermore, if the user is in a hurry, the extraction unit can display concise information first. In this way, the extraction unit can provide information that is easy for the user to understand by adjusting the order in which the extraction results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using AI or not. For example, the extraction unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0098] The extraction unit can perform extraction while considering the geographical distribution of documents. For example, the extraction unit can prioritize the extraction of geographically related documents. The extraction unit can use AI to analyze the geographical distribution of documents in detail. For example, the extraction unit can use machine learning algorithms to evaluate the geographical distribution of documents and set extraction criteria. The extraction unit can improve the accuracy of extraction based on geographical distribution. For example, the extraction unit prioritizes the extraction of geographically related documents. This allows the extraction unit to extract highly relevant information by considering the geographical distribution of documents. Some or all of the above processing in the extraction unit may be performed using AI or not. For example, the extraction unit can input geographical distribution data of documents into a generating AI and have the generating AI set the extraction criteria.

[0099] The extraction unit can improve the accuracy of its extraction by referring to related literature in the document during the extraction process. For example, the extraction unit can extract important information by referring to related literature. The extraction unit can use AI to analyze related literature in detail. For example, the extraction unit can use machine learning algorithms to evaluate related literature and set extraction criteria. The extraction unit can improve the accuracy of its extraction based on the related literature. For example, the extraction unit can extract important information by referring to related literature. In this way, the extraction unit can accurately extract important information by improving the accuracy of its extraction by referring to related literature in the document. Some or all of the above processes in the extraction unit may be performed using AI or not. For example, the extraction unit can input related literature data into a generating AI and have the generating AI set the extraction criteria.

[0100] The reporting unit can estimate the user's emotions and adjust how the report is displayed based on the estimated emotions. For example, the reporting unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reporting unit can use AI to estimate the user's emotions in real time. For example, the reporting unit can use facial recognition technology to estimate the user's emotions. The reporting unit adjusts how the report is displayed based on the estimated emotions. For example, if the user is tense, the reporting unit can provide a simple and easy-to-read report. If the user is relaxed, the reporting unit can provide a detailed report. Furthermore, if the user is in a hurry, the reporting unit can provide a concise report. In this way, the reporting unit can provide reports that are easy for the user to understand by adjusting how the report is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reporting section may be performed using AI or not. For example, the reporting section can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0101] The reporting unit can optimize the current report by referring to past report data when creating a report. For example, the reporting unit can analyze past report data and reflect the findings in the current report. The reporting unit can use AI to analyze past report data in detail. For example, the reporting unit can use machine learning algorithms to evaluate past report data and optimize the current report. The reporting unit can improve the accuracy of reports by referring to past report data. For example, the reporting unit can optimize the report format based on past report data. In this way, the reporting unit can provide highly accurate reports by optimizing the current report by referring to past report data. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input past report data into a generation AI and have the generation AI perform the optimization of the current report.

[0102] The reporting unit can apply different report formats to different document categories when creating reports. For example, it can apply a technical report format to technical documents. The reporting unit can use AI to select the most appropriate report format according to the document category. For example, it can apply a legal report format to legal documents. It can apply a financial report format to financial documents. In this way, the reporting unit can provide reports in the appropriate format by applying different report formats to different document categories. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input document category data into a generating AI and have the generating AI select the most appropriate report format.

[0103] The reporting unit can estimate the user's emotions and adjust the importance of the report based on those emotions. For example, the reporting unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reporting unit can use AI to estimate the user's emotions in real time. For example, the reporting unit can use facial recognition technology to estimate the user's emotions. Based on the estimated user emotions, the reporting unit adjusts the importance of the report. For example, if the user is tense, the reporting unit will postpone less important information. Conversely, if the user is relaxed, the reporting unit can prioritize displaying more important information. Furthermore, if the user is in a hurry, the reporting unit can prioritize displaying concise information. In this way, the reporting unit can prioritize providing information that is important to the user by adjusting the importance of the report according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reporting section may be performed using AI or not. For example, the reporting section can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0104] The reporting department can prioritize reports based on the submission dates of the documents when creating them. For example, the reporting department can prioritize reporting on documents with approaching deadlines. The reporting department can use AI to evaluate the submission dates of documents and determine the priority of reports. For example, the reporting department can adjust the order of reports according to the submission dates. The reporting department can set report priorities based on the submission dates. This allows the reporting department to create reports efficiently by determining the priority of reports based on the submission dates of the documents. Some or all of the above processes in the reporting department may be performed using AI or not. For example, the reporting department can input document submission date data into a generating AI and have the generating AI perform the determination of report priorities.

[0105] The reporting unit can create reports by referring to relevant market data in the document during the report creation process. For example, the reporting unit can enrich the report content by referring to relevant market data. The reporting unit can use AI to analyze relevant market data in detail. For example, the reporting unit can use machine learning algorithms to evaluate relevant market data and improve the accuracy of the report. The reporting unit can optimize the report format based on market data. For example, the reporting unit can enrich the report content by referring to relevant market data. This allows the reporting unit to provide highly accurate reports by creating reports by referring to relevant market data in the document. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input relevant market data into a generating AI and have the generating AI create the report.

[0106] The OCR unit can estimate the user's emotions and adjust the accuracy of the OCR based on the estimated emotions. For example, the OCR unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The OCR unit can estimate the user's emotions in real time using AI. For example, the OCR unit can estimate the user's emotions using facial recognition technology. The OCR unit adjusts the accuracy of the OCR based on the estimated emotions. For example, if the user is nervous, the OCR unit can apply high-accuracy OCR. Also, if the user is relaxed, the OCR unit can apply standard OCR. Furthermore, if the user is in a hurry, the OCR unit can apply rapid OCR. In this way, the OCR unit can provide the user with OCR of appropriate accuracy by adjusting the accuracy of the OCR according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the OCR unit may be performed using AI, or they may not be performed using AI. For example, the OCR unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0107] The OCR unit can select the optimal OCR algorithm based on image quality during OCR processing. For example, the OCR unit applies a high-precision OCR algorithm to high-quality images. The OCR unit can use AI to evaluate image quality and select the optimal OCR algorithm. For example, the OCR unit applies OCR after noise reduction to low-quality images. The OCR unit can select an appropriate OCR algorithm according to image quality. In this way, the OCR unit can provide highly accurate OCR by selecting the optimal OCR algorithm based on image quality. Some or all of the above processing in the OCR unit may be performed using AI or not. For example, the OCR unit can input image quality data into a generating AI and have the generating AI select the optimal OCR algorithm.

[0108] The OCR unit can apply different OCR methods depending on the image category during OCR processing. For example, the OCR unit applies an OCR method specialized for handwritten character recognition to handwritten characters. The OCR unit can use AI to select the optimal OCR method according to the image category. For example, the OCR unit applies an OCR method specialized for printed character recognition to printed characters. The OCR unit can select the optimal OCR method according to the image category. In this way, the OCR unit can provide highly accurate OCR by applying different OCR methods according to the image category. Some or all of the above processing in the OCR unit may be performed using AI or not. For example, the OCR unit can input image category data into a generating AI and have the generating AI select the optimal OCR method.

[0109] The OCR unit can estimate the user's emotions and adjust the order in which the OCR results are displayed based on the estimated emotions. For example, the OCR unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The OCR unit can estimate the user's emotions in real time using AI. For example, the OCR unit can estimate the user's emotions using facial recognition technology. The OCR unit adjusts the order in which the OCR results are displayed based on the estimated emotions of the user. For example, if the user is nervous, the OCR unit can display important information first. Also, if the user is relaxed, the OCR unit can display detailed information sequentially. Furthermore, if the user is in a hurry, the OCR unit can display concise information first. In this way, the OCR unit can provide information that is easy for the user to understand by adjusting the order in which the OCR results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the OCR unit may be performed using AI, or they may not be performed using AI. For example, the OCR unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0110] The OCR unit can perform OCR while considering the geographical information of the image. For example, the OCR unit prioritizes the processing of geographically relevant information. The OCR unit can use AI to analyze the geographical information of the image in detail. For example, the OCR unit can use machine learning algorithms to evaluate the geographical information of the image and improve the accuracy of the OCR. The OCR unit can display the OCR results based on the geographical information. For example, the OCR unit prioritizes the processing of geographically relevant information. As a result, the OCR unit can provide highly relevant information by performing OCR while considering the geographical information of the image. Some or all of the above processing in the OCR unit may be performed using AI or not. For example, the OCR unit can input the geographical information data of the image into a generating AI and have the generating AI perform OCR accuracy improvement.

[0111] The OCR unit can improve the accuracy of OCR by referring to related literature in the image during OCR processing. For example, the OCR unit can improve the accuracy of OCR by referring to related literature. The OCR unit can use AI to analyze related literature in detail. For example, the OCR unit can use machine learning algorithms to evaluate related literature and supplement the OCR results. The OCR unit can display the OCR results based on the related literature. For example, the OCR unit can improve the accuracy of OCR by referring to related literature. In this way, the OCR unit can provide highly accurate information by improving the accuracy of OCR by referring to related literature in the image. Some or all of the above processing in the OCR unit may be performed using AI or not using AI. For example, the OCR unit can input related literature data into a generating AI and have the generating AI perform the OCR accuracy improvement.

[0112] The scraping unit can estimate the user's emotions and adjust the scraping frequency based on the estimated emotions. For example, the scraping unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The scraping unit can estimate the user's emotions in real time using AI. For example, the scraping unit can estimate the user's emotions using facial recognition technology. The scraping unit adjusts the scraping frequency based on the estimated emotions. For example, if the user is nervous, the scraping unit can reduce the scraping frequency. Conversely, if the user is relaxed, the scraping unit can increase the scraping frequency. Furthermore, if the user is in a hurry, the scraping unit can adjust the scraping frequency. In this way, the scraping unit can collect information at an appropriate frequency for the user by adjusting the scraping frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to these examples. Some or all of the processing described above in the scraping unit may be performed using AI or not. For example, the scraping unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0113] The scraping unit can select the optimal scraping method based on the quality of the linked site during scraping. For example, the scraping unit applies a high-precision scraping method to high-quality linked sites. The scraping unit can use AI to evaluate the quality of linked sites and select the optimal scraping method. For example, the scraping unit applies scraping to low-quality linked sites after noise reduction. The scraping unit can select an appropriate scraping method according to the quality of the linked site. As a result, the scraping unit can collect highly accurate information by selecting the optimal scraping method based on the quality of the linked site. Some or all of the above processing in the scraping unit may be performed using AI or not. For example, the scraping unit can input linked site quality data into a generating AI and have the generating AI select the optimal scraping method.

[0114] The scraping unit can apply different scraping methods depending on the category of the linked document during scraping. For example, the scraping unit can apply a technical scraping method to links leading to technical documents. The scraping unit can use AI to select the optimal scraping method according to the category of the linked document. For example, the scraping unit can apply a legal scraping method to links leading to legal documents. The scraping unit can apply a financial scraping method to links leading to financial documents. In this way, the scraping unit can collect highly accurate information by applying different scraping methods according to the category of the linked document. Some or all of the above processing in the scraping unit may be performed using AI or not. For example, the scraping unit can input linked document category data into a generating AI and have the generating AI select the optimal scraping method.

[0115] The scraping unit can estimate the user's emotions and adjust the order in which the scraping results are displayed based on the estimated emotions. For example, the scraping unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The scraping unit can estimate the user's emotions in real time using AI. For example, the scraping unit can estimate the user's emotions using facial recognition technology. The scraping unit adjusts the order in which the scraping results are displayed based on the estimated emotions. For example, if the user is tense, the scraping unit will display important information first. Also, if the user is relaxed, the scraping unit can display detailed information sequentially. Furthermore, if the user is in a hurry, the scraping unit can display concise information first. In this way, the scraping unit can provide information that is easy for the user to understand by adjusting the order in which the scraping results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function that utilizes an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to these examples. Some or all of the processing described above in the scraping unit may be performed using AI or not. For example, the scraping unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0116] The scraping unit can perform scraping while considering the geographical information of the linked sites. For example, the scraping unit can prioritize scraping geographically relevant links. The scraping unit can use AI to analyze the geographical information of the linked sites in detail. For example, the scraping unit can use machine learning algorithms to evaluate the geographical information of the linked sites and improve the accuracy of the scraping. The scraping unit can display the scraping results based on the geographical information. For example, the scraping unit prioritizes scraping geographically relevant links. This allows the scraping unit to collect highly relevant information by considering the geographical information of the linked sites during scraping. Some or all of the above processing in the scraping unit may be performed using AI or not. For example, the scraping unit can input geographical information data of the linked sites into a generating AI and have the generating AI perform the task of improving the accuracy of the scraping.

[0117] The scraping unit can improve the accuracy of scraping by referring to related literature linked to during the scraping process. For example, the scraping unit can improve the accuracy of scraping by referring to related literature. The scraping unit can use AI to analyze related literature in detail. For example, the scraping unit can use machine learning algorithms to evaluate related literature and supplement the scraping results. The scraping unit can display the scraping results based on the related literature. For example, the scraping unit can improve the accuracy of scraping by referring to related literature. In this way, the scraping unit can collect highly accurate information by improving the accuracy of scraping by referring to related literature linked to. Some or all of the above processing in the scraping unit may be performed using AI or not. For example, the scraping unit can input related literature data into a generating AI and have the generating AI perform the scraping accuracy improvement.

[0118] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit can use AI to estimate the user's emotions in real time. For example, the suggestion unit can use facial recognition technology to estimate the user's emotions. The suggestion unit adjusts the way it presents suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can make simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit can make detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can make concise suggestions. In this way, the suggestion unit can provide suggestions that are easy for the user to understand by adjusting the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposed function may be performed using AI or not. For example, the proposed function can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0119] The proposal department can adjust the level of detail of a proposal based on the importance of the document. For example, the proposal department will provide a detailed proposal for highly important documents. The proposal department can use AI to evaluate the importance of documents and adjust the level of detail of proposals. For example, the proposal department will provide a simplified proposal for less important documents. The proposal department can adjust the depth of the proposal according to its importance. For example, the proposal department will provide a detailed proposal for highly important documents and a simplified proposal for less important documents. This allows the proposal department to make efficient proposals by adjusting the level of detail of proposals based on the importance of the documents. 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 proposals.

[0120] The proposal department can apply different proposal algorithms depending on the document category when making a proposal. For example, the proposal department can apply a technical proposal algorithm to technical documents. The proposal department can use AI to select the optimal proposal algorithm depending on the document category. For example, the proposal department can apply a legal proposal algorithm to legal documents. The proposal department can apply a financial proposal algorithm to financial documents. This allows the proposal department to make highly accurate proposals by applying different proposal algorithms depending on the document category. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input document category data into a generating AI and have the generating AI select the optimal proposal algorithm.

[0121] 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 expression with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can estimate the user's emotions in real time using AI. For example, the suggestion unit can estimate the user's emotions using facial recognition technology. The suggestion unit adjusts the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will make a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the user is excited, the suggestion unit can make a visually stimulating suggestion. In this way, the suggestion unit can provide suggestions of an appropriate length for the user by adjusting the length of the suggestion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, 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 proposed function may be performed using AI or not. For example, the proposed function can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0122] The proposal department can determine the priority of proposals based on the submission timing of the documents. For example, the proposal department may prioritize proposals with approaching submission deadlines. The proposal department can use AI to evaluate the submission timing of documents and determine the priority of proposals. For example, the proposal department may adjust the order of proposals according to the submission timing. The proposal department can set the priority of proposals based on the submission timing. This allows the proposal department to make efficient proposals by determining the priority of proposals based on the submission timing of documents. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department may input document submission timing data into a generating AI and have the generating AI perform the determination of proposal priority.

[0123] The proposal department can adjust the order of proposals based on the relevance of the documents during the proposal process. For example, the proposal department can prioritize proposing documents with high relevance. The proposal department can use AI to evaluate the relevance of documents and adjust the order of proposals. For example, the proposal department can adjust the order of proposals according to their relevance. The proposal department can set priority for proposals based on their relevance. This allows the proposal department to make efficient proposals by adjusting the order of proposals based on the relevance of the documents. 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 relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The collection unit can analyze a user's past document submission history and select the optimal collection method. For example, the collection unit can analyze the format of documents previously submitted by the user and prompt them to submit documents in a similar format. The collection unit can use AI to analyze a user's past document submission history in detail. For example, the collection unit can use machine learning algorithms to analyze the user's submission history and select the optimal collection method. The collection unit can analyze the frequency of documents previously submitted by the user and suggest the optimal collection frequency. For example, the collection unit can analyze the content of documents previously submitted by the user and prompt them to submit related documents. In this way, the collection unit can select the optimal collection method by analyzing the user's past document submission history. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past submission history data into a generating AI and have the generating AI select the optimal collection method.

[0126] The OCR unit can select the optimal OCR algorithm based on image quality during OCR processing. For example, the OCR unit applies a high-precision OCR algorithm to high-quality images. The OCR unit can use AI to evaluate image quality and select the optimal OCR algorithm. For example, the OCR unit applies OCR after noise reduction to low-quality images. The OCR unit can select an appropriate OCR algorithm according to image quality. In this way, the OCR unit can provide highly accurate OCR by selecting the optimal OCR algorithm based on image quality. Some or all of the above processing in the OCR unit may be performed using AI or not. For example, the OCR unit can input image quality data into a generating AI and have the generating AI select the optimal OCR algorithm.

[0127] The scraping unit can select the optimal scraping method based on the quality of the linked site during scraping. For example, the scraping unit applies a high-precision scraping method to high-quality linked sites. The scraping unit can use AI to evaluate the quality of linked sites and select the optimal scraping method. For example, the scraping unit applies scraping to low-quality linked sites after noise reduction. The scraping unit can select an appropriate scraping method according to the quality of the linked site. As a result, the scraping unit can collect highly accurate information by selecting the optimal scraping method based on the quality of the linked site. Some or all of the above processing in the scraping unit may be performed using AI or not. For example, the scraping unit can input linked site quality data into a generating AI and have the generating AI select the optimal scraping method.

[0128] The proposal department can adjust the level of detail of a proposal based on the importance of the document. For example, the proposal department will provide a detailed proposal for highly important documents. The proposal department can use AI to evaluate the importance of documents and adjust the level of detail of proposals. For example, the proposal department will provide a simplified proposal for less important documents. The proposal department can adjust the depth of the proposal according to its importance. For example, the proposal department will provide a detailed proposal for highly important documents and a simplified proposal for less important documents. This allows the proposal department to make efficient proposals by adjusting the level of detail of proposals based on the importance of the documents. 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 proposals.

[0129] The reporting unit can optimize the current report by referring to past report data when creating a report. For example, the reporting unit can analyze past report data and reflect the findings in the current report. The reporting unit can use AI to analyze past report data in detail. For example, the reporting unit can use machine learning algorithms to evaluate past report data and optimize the current report. The reporting unit can improve the accuracy of reports by referring to past report data. For example, the reporting unit can optimize the report format based on past report data. In this way, the reporting unit can provide highly accurate reports by optimizing the current report by referring to past report data. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input past report data into a generation AI and have the generation AI perform the optimization of the current report.

[0130] The collection unit can estimate the user's emotions and adjust the timing of document collection based on the estimated emotions. For example, the collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The collection unit can use AI to estimate the user's emotions in real time. For example, the collection unit can use facial recognition technology to estimate the user's emotions. Based on the estimated emotions, the collection unit adjusts the timing of document collection. For example, if the user is stressed, the collection unit can delay the collection timing to allow the user to submit documents in a relaxed state. Conversely, if the user is in a hurry, the collection unit can speed up the collection timing to collect documents quickly. Furthermore, if the user is focused, the collection unit can collect documents at that time to process them efficiently. In this way, the collection unit can reduce the user's burden by adjusting the timing of document collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0131] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can estimate the user's emotions in real time using AI. For example, the analysis unit can estimate the user's emotions using facial recognition technology. The analysis unit adjusts the presentation of the analysis based on the estimated emotions of the user. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. Also, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0132] The extraction unit can estimate the user's emotions and adjust the extraction criteria based on the estimated emotions. For example, the extraction unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The extraction unit can estimate the user's emotions in real time using AI. For example, the extraction unit can estimate the user's emotions using facial recognition technology. The extraction unit adjusts the extraction criteria based on the estimated emotions of the user. For example, if the user is tense, the extraction unit will extract only important information. Also, if the user is relaxed, the extraction unit can extract detailed information. Furthermore, if the user is in a hurry, the extraction unit can extract concise information. In this way, the extraction unit can extract information appropriate for the user by adjusting the extraction criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using AI or not. For example, the extraction unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0133] The reporting unit can estimate the user's emotions and adjust how the report is displayed based on the estimated emotions. For example, the reporting unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reporting unit can use AI to estimate the user's emotions in real time. For example, the reporting unit can use facial recognition technology to estimate the user's emotions. The reporting unit adjusts how the report is displayed based on the estimated emotions. For example, if the user is tense, the reporting unit can provide a simple and easy-to-read report. If the user is relaxed, the reporting unit can provide a detailed report. Furthermore, if the user is in a hurry, the reporting unit can provide a concise report. In this way, the reporting unit can provide reports that are easy for the user to understand by adjusting how the report is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reporting section may be performed using AI or not. For example, the reporting section can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0134] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit can use AI to estimate the user's emotions in real time. For example, the suggestion unit can use facial recognition technology to estimate the user's emotions. The suggestion unit adjusts the way it presents suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can make simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit can make detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can make concise suggestions. In this way, the suggestion unit can provide suggestions that are easy for the user to understand by adjusting the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposed function may be performed using AI or not. For example, the proposed function can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The collection unit collects the submitted documents. The collection unit receives documents uploaded by the submitter, for example, and processes them appropriately according to their format. Text documents are received as they are, images are converted into text using OCR technology, and URL links are scraped and converted into text. Step 2: The analysis unit analyzes the documents collected by the collection unit. The analysis unit analyzes the contents of the collected documents and verifies whether they contain the necessary information. The analysis unit uses AI and natural language processing technology to analyze the contents of the documents in detail. Step 3: The extraction unit extracts important information from the documents analyzed by the analysis unit. The extraction unit extracts specific keywords and phrases and uses AI and machine learning algorithms to accurately extract important information. Step 4: The reporting unit reports on the missing information based on the information extracted by the extraction unit. The reporting unit identifies the missing information based on the extracted information and automatically compiles the missing information into a report using AI and natural language generation technology.

[0137] 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.

[0138] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, extraction unit, and reporting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects submitted documents. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected documents. The extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts important information from the analyzed documents. The reporting unit is implemented by the control unit 46A of the smart device 14 and reports any missing information based on the extracted information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, extraction unit, and reporting unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects submitted documents. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected documents. The extraction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and extracts important information from the analyzed documents. The reporting unit is implemented, for example, by the control unit 46A of the smart glasses 214 and reports any missing information based on the extracted information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] 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.

[0172] Each of the multiple elements described above, including the collection unit, analysis unit, extraction unit, and reporting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects submitted documents. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected documents. The extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts important information from the analyzed documents. The reporting unit is implemented by the control unit 46A of the headset terminal 314 and reports any missing information based on the extracted information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0188] 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.

[0189] Each of the multiple elements described above, including the collection unit, analysis unit, extraction unit, and reporting unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects submitted documents. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected documents. The extraction unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and extracts important information from the analyzed documents. The reporting unit is implemented by, for example, the control unit 46A of the robot 414 and reports any missing information based on the extracted information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) The collection department that collects documents, An analysis unit analyzes the documents collected by the aforementioned collection unit, An extraction unit extracts important information from documents analyzed by the aforementioned analysis unit, A reporting unit reports the missing information based on the information extracted by the extraction unit, Equipped with A system characterized by the following features. (Note 2) It features an OCR unit that extracts text from images. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a scraping unit that collects information from the URL link destination. The system described in Appendix 1, characterized by the features described herein. (Note 4) The department includes a proposal section that provides improvement suggestions to the submitter based on the missing information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of document collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past document submission history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting documents, filter them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and determines the priority of documents to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting documents, the system prioritizes collecting highly relevant documents by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting documents, analyze the user's social media activity and collect relevant documents. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the documents. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the document category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on the submission date of the documents. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the documents. The system described in Appendix 1, characterized by the features described herein. (Note 17) The extraction unit is We estimate the user's emotions and adjust the extraction criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The extraction unit is When extracting data, consider the interrelationships between documents to improve extraction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 19) The extraction unit is During the extraction process, the attribute information of the document submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The extraction unit is It estimates the user's sentiment and adjusts the order in which the extraction results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The extraction unit is During the extraction process, the geographical distribution of the documents will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The extraction unit is During extraction, refer to related documents to improve extraction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned report section is, It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned report section is, When creating a report, refer to past report data to optimize the current report. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned report section is, When creating a report, apply different report formats to each document category. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned report section is, We estimate user sentiment and adjust the importance of the report based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned report section is, When preparing reports, prioritize them based on their submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned report section is, When creating a report, refer to relevant market data in the document to create the report. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned OCR unit, The system estimates the user's emotions and adjusts the accuracy of the OCR based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned OCR unit, During OCR processing, the optimal OCR algorithm is selected based on the image quality. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned OCR unit, During OCR processing, different OCR methods are applied depending on the image category. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned OCR unit, It estimates the user's emotions and adjusts the order in which the OCR results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned OCR unit, During OCR processing, the geographical information of the image is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned OCR unit, During OCR processing, referencing related literature for the image improves the accuracy of OCR. The system described in Appendix 1, characterized by the features described herein. (Note 35) The scraping unit is, It estimates the user's sentiment and adjusts the scraping frequency based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The scraping unit is, During web scraping, the optimal scraping method is selected based on the quality of the linked website. The system described in Appendix 1, characterized by the features described herein. (Note 37) The scraping unit is, When scraping, apply different scraping techniques depending on the category of the linked page. The system described in Appendix 1, characterized by the features described herein. (Note 38) The scraping unit is, It estimates the user's sentiment and adjusts the order in which the scraping results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The scraping unit is, When scraping, take into account the geographical information of the linked website. The system described in Appendix 1, characterized by the features described herein. (Note 40) The scraping unit is, When scraping, refer to related literature linked to improve the accuracy of the scraping. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the documents. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned proposal section is, When submitting a proposal, different proposal algorithms are applied depending on the document category. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned proposal section is, When submitting a proposal, we will prioritize the proposals based on the timing of document submission. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned proposal section is, When making a proposal, adjust the order of the proposals based on the relevance of the documents. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department that collects documents, An analysis unit analyzes the documents collected by the aforementioned collection unit, An extraction unit extracts important information from documents analyzed by the aforementioned analysis unit, A reporting unit reports the missing information based on the information extracted by the extraction unit, Equipped with A system characterized by the following features.

2. It features an OCR unit that extracts text from images. The system according to feature 1.

3. It includes a scraping unit that collects information from the URL link destination. The system according to feature 1.

4. The department includes a proposal section that provides improvement suggestions to the submitter based on the missing information. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of document collection based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past document submission history and select the optimal collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting documents, filter them based on the user's current projects and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and determines the priority of documents to collect based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting documents, the system prioritizes collecting highly relevant documents by considering the user's geographical location. The system according to feature 1.

10. The aforementioned collection unit is When collecting documents, analyze the user's social media activity and collect relevant documents. The system according to feature 1.

11. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

Images