System

Abstract

An object of the system according to the embodiment is to extract an important item from a complicated document and present the item to a user in an easy-to-understand manner.SOLUTION: A system includes a scan unit, an extraction unit, and a presentation unit. The scanning unit scans a document. The extraction unit extracts an important item from the document scanned by the scanning unit. The presentation unit presents the important item extracted by the extraction unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of making it difficult to efficiently extract important items from complex documents and present them to users in an easy-to-understand manner.

[0005] The system according to the embodiment aims to extract important items from complex documents and present them to the user in an easy-to-understand manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a scanning unit, an extracting unit, and a presenting unit. The scanning unit scans a document. The extracting unit extracts important items from the document scanned by the scanning unit. The presenting unit presents the important items extracted by the extracting unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can extract important items from complex documents and present them to the user in an easy-to-understand manner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The interpretation support system according to the embodiment of the present invention is a system in which a document is scanned, and a generation AI extracts important items and points requiring attention and presents them to the user, thereby enabling the user to quickly and accurately understand complex documents.

[0029] An interpretation support system according to an embodiment includes a scanning unit, an extraction unit, and a presentation unit. The scanning unit scans documents. For example, the scanning unit scans paper documents using a high-resolution scanner and converts them into digital data. The scanning unit can also directly read electronic documents. For example, the scanning unit can read PDF documents and save them as text data. The scanning unit can also photograph documents using a smartphone camera and save them as image data. The extraction unit extracts important items from documents scanned by the scanning unit. For example, the extraction unit can automatically identify important clauses and conditions within a document using a generation AI. The extraction unit can also extract deadlines and risks within a document using the generation AI. For example, the generation AI can extract important information within a document using natural language processing technology. The extraction unit can also analyze important points within a document using the generation AI. The presentation unit presents the important items extracted by the extraction unit to a user. For example, the presentation unit can highlight important items on the app interface. The presentation unit can also explain important points to the user in an easy-to-understand manner. For example, the presentation unit explains risks to the user based on the information extracted by the generation AI. Furthermore, the presentation unit can present information in a format that is easy for the user to understand. This allows the interpretation support system according to the embodiment to enable the user to quickly and accurately understand complex documents. For example, the user can quickly grasp the contents of a contract and accurately understand their rights and obligations. Furthermore, by understanding points that require attention in advance, risks can be avoided.

[0030] The scanning unit detects the physical state of the document, and the generation AI can correct that state before digitizing it. For example, the scanning unit can detect folds or stains on the document during scanning, and the generation AI can automatically correct them. For example, it can apply algorithms to smooth out folds and remove stains. The scanning unit can also detect tears in the document, and the generation AI can fill in those areas. For example, it can use image processing technology to fill in the torn areas. Furthermore, the scanning unit can detect faded colors in the document, and the generation AI can perform color correction. For example, it can apply algorithms to make faded areas closer to their original color. This allows the physical state of the document to be corrected before digitizing it, making it easier for the generation AI to analyze.

[0031] The scanning unit selects the optimal digitization method depending on the document format, and the generative AI can automatically apply it. For example, the scanning unit automatically identifies the format of the scanned document and selects the optimal digitization method. For example, it applies text extraction to PDF files and OCR to image files. The scanning unit can also combine different digitization methods depending on the document format. For example, it applies OCR to the image portions of a PDF file and text extraction to the text portions. Furthermore, the scanning unit can dynamically change the digitization method depending on the document format. For example, if part of a document is an image file, it applies OCR only to that portion. This allows the generative AI to more accurately analyze the document by applying the optimal digitization method depending on the document format.

[0032] The scanning unit can automatically translate documents into multiple languages ​​to accommodate users of different languages. For example, the scanning unit can automatically translate scanned documents into multiple languages ​​to accommodate users of different languages. For example, translation into English, French, Chinese, etc. The scanning unit can also automatically display the translated document according to a user's language setting. For example, the translated document is displayed based on the language set by the user. Furthermore, the scanning unit can analyze context using generative AI to improve translation accuracy. For example, it applies an algorithm to provide an appropriate translation depending on the content of the document. This allows automatic translation into multiple languages ​​to accommodate users of different languages, thereby enabling more users to understand the document.

[0033] The scanning unit automatically extracts document metadata, allowing the generation AI to perform analysis based on that information. For example, the scanning unit automatically extracts document metadata when scanning, allowing the generation AI to perform analysis based on that information. For example, it extracts information such as the creation date and creator. The scanning unit can also extract metadata such as the document's file format and size. For example, it extracts the number of pages and file size for PDF documents. Furthermore, the scanning unit can extract metadata related to the document's content. For example, it extracts the document's title and keywords. This allows the document's metadata to be automatically extracted, allowing the generation AI to perform analysis based on that information, resulting in a more accurate understanding of the document's content.

[0034] The extraction unit can compare the important items extracted by the generation AI with a database of similar past contracts and rank their importance. For example, the extraction unit compares the important items extracted by the generation AI with a database of similar past contracts and ranks their importance. For example, it evaluates the importance based on data from past contracts. The extraction unit can also compare the important items extracted by the generation AI with industry standard contracts and rank their importance. For example, it identifies particularly important items by comparing with industry standard contracts. Furthermore, the extraction unit can rank the important items extracted by the generation AI based on a risk assessment. For example, it sets a high rank to items with high risk. In this way, by comparing with a database of similar past contracts and ranking their importance, it is possible to clarify items that require particular attention from the user.

[0035] The extraction unit can perform a legal risk assessment on the important items extracted by the generation AI and identify high-risk items. For example, the extraction unit can perform a legal risk assessment on the important items extracted by the generation AI and identify high-risk items. For example, the extraction unit can apply an algorithm to calculate a legal risk score. The extraction unit can also evaluate the important items extracted by the generation AI from the perspective of legal compliance. For example, the extraction unit can evaluate the risk of breach of contract. Furthermore, the extraction unit can evaluate the important items extracted by the generation AI by comparing them with past legal cases. For example, the extraction unit can evaluate the risk based on a database of past legal precedents. In this way, by performing a legal risk assessment and identifying high-risk items, it is possible to provide users with information to avoid legal risks.

[0036] The extraction unit can visualize the important items extracted by the generation AI and present them to the user, making them easier to understand visually. For example, the extraction unit can visualize the important items extracted by the generation AI and present them to the user. For example, it can display important clauses in graphs or charts. The extraction unit can also present the important items extracted by the generation AI as infographics. For example, it can visually highlight important parts of a contract. Furthermore, the extraction unit can color-code the important items extracted by the generation AI and present them. For example, it can display high-risk items in red. This allows the user to quickly grasp important information by visualizing the important items and making them easier to understand visually.

[0037] The presentation unit can refer to past precedents and legal cases and explain specific risks when the generation AI presents points to be careful of. For example, the presentation unit can refer to past precedents and legal cases and explain specific risks when the generation AI presents points to be careful of. For example, the presentation unit evaluates risks based on a database of past precedents. The presentation unit can also explain risks based on legal cases. For example, it can explain risks based on cases of breach of contract. Furthermore, the presentation unit can explain specific risks to the user in an easy-to-understand manner based on information extracted by the generation AI. For example, it can explain high-risk items using specific examples. In this way, by referring to past precedents and legal cases and explaining specific risks, the user can more easily understand the risks.

[0038] The presentation unit can provide individually customized advice by taking into account the user's past contract history when the generation AI presents points that require attention. For example, the presentation unit can provide individually customized advice by taking into account the user's past contract history when the generation AI presents points that require attention. For example, the presentation unit can evaluate risks based on the content of past contracts. The presentation unit can also provide customized advice based on the user's industry characteristics. For example, the presentation unit can perform risk assessment specialized for a specific industry. Furthermore, the presentation unit can provide advice on specific risks based on the user's past contract history. For example, the presentation unit can warn of contract clauses that have been problematic in the past. In this way, by providing individually customized advice by taking into account the user's past contract history, it becomes easier for the user to avoid risks.

[0039] The presentation unit can provide information that corresponds to the regulations of different jurisdictions or countries when the generation AI presents points that require attention. For example, the presentation unit provides information that corresponds to the regulations of different jurisdictions or countries when the generation AI presents points that require attention. For example, the presentation unit evaluates risks based on the laws and regulations of each country. The presentation unit can also explain risks based on the regulations of different jurisdictions. For example, it can explain risks based on international law or state law. Furthermore, the presentation unit can provide users with information that corresponds to the regulations of different jurisdictions or countries based on the information extracted by the generation AI. For example, it can provide information on import / export regulations and labor laws and regulations. This makes it easier for users to understand international risks by providing information that corresponds to the regulations of different jurisdictions or countries.

[0040] The presentation unit can adjust the level of detail of the explanation according to the user's level of expertise when the generation AI presents points that require attention. For example, the presentation unit adjusts the level of detail of the explanation according to the user's level of expertise when the generation AI presents points that require attention. For example, by replacing technical terms with simpler words. The presentation unit can also adjust the level of detail of the explanation based on the user's work experience. For example, it can provide explanations for beginners and detailed explanations for experts. Furthermore, the presentation unit can adjust the level of detail of the explanation according to whether the user has qualifications. For example, it can provide specialized explanations to qualified users and basic explanations to unqualified users. In this way, adjusting the level of detail of the explanation according to the user's level of expertise makes it easier for the user to understand points that require attention.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The interpretation support system can further include a suggestion unit that automatically suggests related documents based on the user's past search history. For example, the system can analyze keywords searched for by the user in the past and the contents of documents viewed by the user to suggest new related documents. The suggestion unit can also suggest related documents based on the user's industry or job role. For example, a user working in the legal field can be suggested documents related to the latest legal changes. Furthermore, the suggestion unit can suggest customized documents based on the user's fields of interest. For example, a user interested in a particular technical field can be suggested the latest research papers related to that field. This allows the user to quickly obtain the information they need.

[0043] The interpretation support system can further include a feedback collection unit that collects user feedback and improves the accuracy of the system. For example, it provides an interface for users to evaluate the information presented, and the generative AI algorithm is improved based on that evaluation. The feedback collection unit can also analyze specific comments provided by users and identify areas for improvement in the system. For example, it can analyze errors or unclear points pointed out by users and reflect them in the next analysis. Furthermore, the feedback collection unit can monitor user usage and evaluate system performance. For example, it can analyze which functions users use frequently and use this information to improve those functions. This allows the accuracy of the system to be improved based on user feedback.

[0044] The interpretation support system can further include a schedule management unit that automatically analyzes documents based on the user's schedule. For example, by linking with the user's calendar, document analysis can be performed to coincide with important meetings or deadlines. The schedule management unit can also schedule document analysis based on reminders set by the user. For example, document analysis can be started at a specific date and time based on a reminder set by the user. The schedule management unit can also analyze documents to coincide with the user's working hours. For example, the user can set the system to analyze documents outside of working hours. This allows document analysis to be performed automatically to coincide with the user's schedule, making it possible to efficiently obtain information.

[0045] The interpretation support system may further include a health management unit that monitors the user's health condition and encourages breaks at appropriate times. For example, it may analyze the user's heart rate and stress level and suggest breaks at appropriate times. The health management unit may also monitor the user's posture and eye strain and suggest appropriate exercises and stretches. For example, it may suggest exercises to rest the eyes after working for a long period of time. The health management unit may also monitor the user's food and water intake and encourage the user to eat and hydrate at appropriate times. For example, it may display reminders to encourage hydration at regular intervals. This allows the user's health condition to be monitored and the user to take breaks at appropriate times, thereby improving work efficiency.

[0046] The interpretation support system can further include a learning support unit that suggests related learning content based on the user's learning history. For example, it can analyze what the user has learned in the past and suggest new related learning content. The learning support unit can also suggest appropriate learning content based on the user's learning progress. For example, if the user is currently studying a specific topic, it can suggest in-depth learning content related to that topic. Furthermore, the learning support unit can suggest learning content customized according to the user's learning style. For example, it can suggest visual content to a user who prefers visual learning. This allows the user to progress efficiently by suggesting related learning content based on the user's learning history.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The scanning unit scans a document. For example, the scanning unit scans a paper document with a high-resolution scanner and converts it into digital data. The scanning unit can also directly read electronic documents. For example, it can read a document in PDF format and save it as text data. Furthermore, the scanning unit can also take a photo of a document using a smartphone camera and save it as image data. Step 2: The extraction unit extracts important items from the document scanned by the scanning unit. For example, the extraction unit uses the generation AI to automatically identify important clauses and conditions within the document. The extraction unit can also use the generation AI to extract deadlines and risks within the document. Furthermore, the extraction unit can also use the generation AI to analyze points that require attention within the document. Step 3: The presentation unit presents the important items extracted by the extraction unit to the user. For example, the presentation unit highlights important items on the interface of the app. The presentation unit can also explain points that require attention to the user in an easy-to-understand manner. Furthermore, the presentation unit can present information in a format that is easy for the user to understand.

[0049] (Example 2) The interpretation support system according to the embodiment of the present invention is a system in which a document is scanned, and a generation AI extracts important items and points requiring attention and presents them to the user, thereby enabling the user to quickly and accurately understand complex documents.

[0050] An interpretation support system according to an embodiment includes a scanning unit, an extraction unit, and a presentation unit. The scanning unit scans documents. For example, the scanning unit scans paper documents using a high-resolution scanner and converts them into digital data. The scanning unit can also directly read electronic documents. For example, the scanning unit can read PDF documents and save them as text data. The scanning unit can also photograph documents using a smartphone camera and save them as image data. The extraction unit extracts important items from documents scanned by the scanning unit. For example, the extraction unit can automatically identify important clauses and conditions within a document using a generation AI. The extraction unit can also extract deadlines and risks within a document using the generation AI. For example, the generation AI can extract important information within a document using natural language processing technology. The extraction unit can also analyze important points within a document using the generation AI. The presentation unit presents the important items extracted by the extraction unit to a user. For example, the presentation unit can highlight important items on the app interface. The presentation unit can also explain important points to the user in an easy-to-understand manner. For example, the presentation unit explains risks to the user based on the information extracted by the generation AI. Furthermore, the presentation unit can present information in a format that is easy for the user to understand. This allows the interpretation support system according to the embodiment to enable the user to quickly and accurately understand complex documents. For example, the user can quickly grasp the contents of a contract and accurately understand their rights and obligations. Furthermore, by understanding points that require attention in advance, risks can be avoided.

[0051] The scanning unit detects the physical state of the document, and the generation AI can correct that state before digitizing it. For example, the scanning unit can detect folds or stains on the document during scanning, and the generation AI can automatically correct them. For example, it can apply algorithms to smooth out folds and remove stains. The scanning unit can also detect tears in the document, and the generation AI can fill in those areas. For example, it can use image processing technology to fill in the torn areas. Furthermore, the scanning unit can detect faded colors in the document, and the generation AI can perform color correction. For example, it can apply algorithms to make faded areas closer to their original color. This allows the physical state of the document to be corrected before digitizing it, making it easier for the generation AI to analyze.

[0052] The scanning unit selects the optimal digitization method depending on the document format, and the generative AI can automatically apply it. For example, the scanning unit automatically identifies the format of the scanned document and selects the optimal digitization method. For example, it applies text extraction to PDF files and OCR to image files. The scanning unit can also combine different digitization methods depending on the document format. For example, it applies OCR to the image portions of a PDF file and text extraction to the text portions. Furthermore, the scanning unit can dynamically change the digitization method depending on the document format. For example, if part of a document is an image file, it applies OCR only to that portion. This allows the generative AI to more accurately analyze the document by applying the optimal digitization method depending on the document format.

[0053] The scanning unit can use the emotion estimation function to provide an interface for reducing the stress and anxiety the user feels when being scanned. The scanning unit, for example, uses the emotion estimation function to provide an interface for reducing the stress the user feels when being scanned. For example, the scanning unit may analyze the user's facial expression and display guidance for relaxation. The scanning unit can also analyze the user's voice and provide advice for reducing stress. For example, the scanning unit may analyze the user's tone of voice and play music for relaxation. Furthermore, the scanning unit can analyze the user's biometric data and provide feedback for reducing stress. For example, the scanning unit may analyze heart rate fluctuations and suggest breathing techniques for relaxation. This provides an interface that reduces the user's stress and anxiety, making the scanning process more comfortable.

[0054] The scanning unit can automatically translate documents into multiple languages ​​to accommodate users of different languages. For example, the scanning unit can automatically translate scanned documents into multiple languages ​​to accommodate users of different languages. For example, translation into English, French, Chinese, etc. The scanning unit can also automatically display the translated document according to a user's language setting. For example, the translated document is displayed based on the language set by the user. Furthermore, the scanning unit can analyze context using generative AI to improve translation accuracy. For example, it applies an algorithm to provide an appropriate translation depending on the content of the document. This allows automatic translation into multiple languages ​​to accommodate users of different languages, thereby enabling more users to understand the document.

[0055] The scanning unit automatically extracts document metadata, allowing the generation AI to perform analysis based on that information. For example, the scanning unit automatically extracts document metadata when scanning, allowing the generation AI to perform analysis based on that information. For example, it extracts information such as the creation date and creator. The scanning unit can also extract metadata such as the document's file format and size. For example, it extracts the number of pages and file size for PDF documents. Furthermore, the scanning unit can extract metadata related to the document's content. For example, it extracts the document's title and keywords. This allows the document's metadata to be automatically extracted, allowing the generation AI to perform analysis based on that information, resulting in a more accurate understanding of the document's content.

[0056] The scanning unit can use the emotion estimation function to analyze the user's emotion regarding the scanned document in real time and provide positive feedback. The scanning unit can, for example, use the emotion estimation function to analyze the user's emotion regarding the scanned document in real time and provide positive feedback. For example, the scanning unit can analyze the user's facial expression and display an encouraging message. The scanning unit can also analyze the user's voice and provide positive feedback. For example, the scanning unit can analyze the user's tone of voice and display encouraging words. Furthermore, the scanning unit can analyze the user's biometric data and provide positive feedback. For example, the scanning unit can analyze heart rate fluctuations and display a positive message. In this way, the user's emotion can be analyzed in real time and positive feedback can be provided, thereby improving the user's motivation.

[0057] The extraction unit can compare the important items extracted by the generation AI with a database of similar past contracts and rank their importance. For example, the extraction unit compares the important items extracted by the generation AI with a database of similar past contracts and ranks their importance. For example, it evaluates the importance based on data from past contracts. The extraction unit can also compare the important items extracted by the generation AI with industry standard contracts and rank their importance. For example, it identifies particularly important items by comparing with industry standard contracts. Furthermore, the extraction unit can rank the important items extracted by the generation AI based on a risk assessment. For example, it sets a high rank to items with high risk. In this way, by comparing with a database of similar past contracts and ranking their importance, it is possible to clarify items that require particular attention from the user.

[0058] The extraction unit can perform a legal risk assessment on the important items extracted by the generation AI and identify high-risk items. For example, the extraction unit can perform a legal risk assessment on the important items extracted by the generation AI and identify high-risk items. For example, the extraction unit can apply an algorithm to calculate a legal risk score. The extraction unit can also evaluate the important items extracted by the generation AI from the perspective of legal compliance. For example, the extraction unit can evaluate the risk of breach of contract. Furthermore, the extraction unit can evaluate the important items extracted by the generation AI by comparing them with past legal cases. For example, the extraction unit can evaluate the risk based on a database of past legal precedents. In this way, by performing a legal risk assessment and identifying high-risk items, it is possible to provide users with information to avoid legal risks.

[0059] The extraction unit can use the emotion estimation function to analyze the emotion of the user when checking an important item and provide supplemental information to improve comprehension. For example, the extraction unit can use the emotion estimation function to analyze the emotion of the user when checking an important item and provide supplemental information to improve comprehension. For example, additional explanations can be displayed depending on the user's emotional state. The extraction unit can also analyze the user's voice and provide supplemental information to improve comprehension. For example, the extraction unit can analyze the user's tone of voice and display additional explanations. Furthermore, the extraction unit can analyze the user's biometric data and provide supplemental information to improve comprehension. For example, the extraction unit can analyze heart rate fluctuations and display additional explanations. In this way, the user can gain a deeper understanding of the important item by analyzing the user's emotion and providing supplemental information to improve comprehension.

[0060] The extraction unit can visualize the important items extracted by the generation AI and present them to the user, making them easier to understand visually. For example, the extraction unit can visualize the important items extracted by the generation AI and present them to the user. For example, it can display important clauses in graphs or charts. The extraction unit can also present the important items extracted by the generation AI as infographics. For example, it can visually highlight important parts of a contract. Furthermore, the extraction unit can color-code the important items extracted by the generation AI and present them. For example, it can display high-risk items in red. This allows the user to quickly grasp important information by visualizing the important items and making them easier to understand visually.

[0061] The extraction unit can use the emotion estimation function to prioritize displaying items of particular interest based on the emotion the user feels toward the important items. The extraction unit, for example, uses the emotion estimation function to prioritize displaying items of particular interest based on the emotion the user feels toward the important items. For example, items with high importance are highlighted according to the user's emotion score. The extraction unit can also analyze the user's click history and prioritize displaying items of particular interest. For example, items that the user has frequently checked in the past are prioritized. Furthermore, the extraction unit can also prioritize displaying items of particular interest based on the results of a user survey. For example, items in which the user has shown particular interest are prioritized. This allows the user to efficiently check important information by prioritized displaying items of particular interest based on the user's emotion.

[0062] The presentation unit can refer to past precedents and legal cases and explain specific risks when the generation AI presents points to be careful of. For example, the presentation unit can refer to past precedents and legal cases and explain specific risks when the generation AI presents points to be careful of. For example, the presentation unit evaluates risks based on a database of past precedents. The presentation unit can also explain risks based on legal cases. For example, it can explain risks based on cases of breach of contract. Furthermore, the presentation unit can explain specific risks to the user in an easy-to-understand manner based on information extracted by the generation AI. For example, it can explain high-risk items using specific examples. In this way, by referring to past precedents and legal cases and explaining specific risks, the user can more easily understand the risks.

[0063] The presentation unit can provide individually customized advice by taking into account the user's past contract history when the generation AI presents points that require attention. For example, the presentation unit can provide individually customized advice by taking into account the user's past contract history when the generation AI presents points that require attention. For example, the presentation unit can evaluate risks based on the content of past contracts. The presentation unit can also provide customized advice based on the user's industry characteristics. For example, the presentation unit can perform risk assessment specialized for a specific industry. Furthermore, the presentation unit can provide advice on specific risks based on the user's past contract history. For example, the presentation unit can warn of contract clauses that have been problematic in the past. In this way, by providing individually customized advice by taking into account the user's past contract history, it becomes easier for the user to avoid risks.

[0064] The presentation unit can use the emotion estimation function to analyze the user's emotion when checking points that require attention, and provide supplemental information to improve comprehension. For example, the presentation unit can use the emotion estimation function to analyze the user's emotion when checking points that require attention, and provide supplemental information to improve comprehension. For example, additional explanations can be displayed depending on the user's emotional state. The presentation unit can also analyze the user's voice and provide supplemental information to improve comprehension. For example, the presentation unit can analyze the user's tone of voice and display additional explanations. Furthermore, the presentation unit can analyze the user's biometric data and provide supplemental information to improve comprehension. For example, the presentation unit can analyze heart rate fluctuations and display additional explanations. In this way, by analyzing the user's emotion and providing supplemental information to improve comprehension, the user can gain a deeper understanding of points that require attention.

[0065] The presentation unit can provide information that corresponds to the regulations of different jurisdictions or countries when the generation AI presents points that require attention. For example, the presentation unit provides information that corresponds to the regulations of different jurisdictions or countries when the generation AI presents points that require attention. For example, the presentation unit evaluates risks based on the laws and regulations of each country. The presentation unit can also explain risks based on the regulations of different jurisdictions. For example, it can explain risks based on international law or state law. Furthermore, the presentation unit can provide users with information that corresponds to the regulations of different jurisdictions or countries based on the information extracted by the generation AI. For example, it can provide information on import / export regulations and labor laws and regulations. This makes it easier for users to understand international risks by providing information that corresponds to the regulations of different jurisdictions or countries.

[0066] The presentation unit can adjust the level of detail of the explanation according to the user's level of expertise when the generation AI presents points that require attention. For example, the presentation unit adjusts the level of detail of the explanation according to the user's level of expertise when the generation AI presents points that require attention. For example, by replacing technical terms with simpler words. The presentation unit can also adjust the level of detail of the explanation based on the user's work experience. For example, it can provide explanations for beginners and detailed explanations for experts. Furthermore, the presentation unit can adjust the level of detail of the explanation according to whether the user has qualifications. For example, it can provide specialized explanations to qualified users and basic explanations to unqualified users. In this way, adjusting the level of detail of the explanation according to the user's level of expertise makes it easier for the user to understand points that require attention.

[0067] The presentation unit can use the emotion estimation function to prioritize displaying points of particular interest based on the emotion the user feels toward points that require attention. The presentation unit, for example, uses the emotion estimation function to prioritize displaying points of particular interest based on the emotion the user feels toward points that require attention. For example, the presentation unit highlights points of high importance according to the user's emotion score. The presentation unit can also analyze the user's click history and prioritize displaying points of particular interest. For example, points that the user has frequently checked in the past are prioritized. Furthermore, the presentation unit can also prioritize displaying points of particular interest based on the results of a user survey. For example, points in which the user has shown particular interest are prioritized. In this way, by prioritized displaying points of particular interest based on the user's emotion, the user can efficiently check important information.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The interpretation support system can further include a suggestion unit that automatically suggests related documents based on the user's past search history. For example, the system can analyze keywords searched for by the user in the past and the contents of documents viewed by the user to suggest new related documents. The suggestion unit can also suggest related documents based on the user's industry or job role. For example, a user working in the legal field can be suggested documents related to the latest legal changes. Furthermore, the suggestion unit can suggest customized documents based on the user's fields of interest. For example, a user interested in a particular technical field can be suggested the latest research papers related to that field. This allows the user to quickly obtain the information they need.

[0070] The interpretation support system can further include a feedback collection unit that collects user feedback and improves the accuracy of the system. For example, it provides an interface for users to evaluate the information presented, and the generative AI algorithm is improved based on that evaluation. The feedback collection unit can also analyze specific comments provided by users and identify areas for improvement in the system. For example, it can analyze errors or unclear points pointed out by users and reflect them in the next analysis. Furthermore, the feedback collection unit can monitor user usage and evaluate system performance. For example, it can analyze which functions users use frequently and use this information to improve those functions. This allows the accuracy of the system to be improved based on user feedback.

[0071] The interpretation support system can further include a schedule management unit that automatically analyzes documents based on the user's schedule. For example, by linking with the user's calendar, document analysis can be performed to coincide with important meetings or deadlines. The schedule management unit can also schedule document analysis based on reminders set by the user. For example, document analysis can be started at a specific date and time based on a reminder set by the user. The schedule management unit can also analyze documents to coincide with the user's working hours. For example, the user can set the system to analyze documents outside of working hours. This allows document analysis to be performed automatically to coincide with the user's schedule, making it possible to efficiently obtain information.

[0072] The interpretation support system may further include a health management unit that monitors the user's health condition and encourages breaks at appropriate times. For example, it may analyze the user's heart rate and stress level and suggest breaks at appropriate times. The health management unit may also monitor the user's posture and eye strain and suggest appropriate exercises and stretches. For example, it may suggest exercises to rest the eyes after working for a long period of time. The health management unit may also monitor the user's food and water intake and encourage the user to eat and hydrate at appropriate times. For example, it may display reminders to encourage hydration at regular intervals. This allows the user's health condition to be monitored and the user to take breaks at appropriate times, thereby improving work efficiency.

[0073] The interpretation support system can further include a learning support unit that suggests related learning content based on the user's learning history. For example, it can analyze what the user has learned in the past and suggest new related learning content. The learning support unit can also suggest appropriate learning content based on the user's learning progress. For example, if the user is currently studying a specific topic, it can suggest in-depth learning content related to that topic. Furthermore, the learning support unit can suggest learning content customized according to the user's learning style. For example, it can suggest visual content to a user who prefers visual learning. This allows the user to progress efficiently by suggesting related learning content based on the user's learning history.

[0074] The interpretation support system can further analyze the user's emotions and provide a customized interface according to the emotions. For example, if the user is feeling stressed, the color tone of the interface can be changed to a calmer one. Also, if the user is concentrating, it can provide settings to minimize notifications. For example, it can automatically enable focus mode and block unnecessary notifications. Furthermore, if the user is tired, it can provide settings to improve visibility, such as increasing the font size of the interface. For example, it can automatically increase the font size after working for a long time. In this way, by providing a customized interface according to the user's emotions, it is possible to improve work efficiency.

[0075] The interpretation support system can further analyze the user's emotions and provide reminders according to the emotions. For example, if the user is feeling stressed, a reminder to relax can be displayed. Also, if the user is concentrating, a setting to be reminded of important tasks can be provided. For example, a reminder for important tasks can be displayed during concentration mode. Furthermore, if the user is tired, a reminder to take a break can be provided. For example, a reminder to take a break can be displayed after working for a long time. In this way, work efficiency can be improved by providing reminders according to the user's emotions.

[0076] The interpretation support system can further analyze the user's emotions and provide feedback according to the emotions. For example, if the user is feeling anxious, an encouraging message can be displayed. Also, if the user is satisfied, positive feedback can be provided. For example, if the user feels a sense of accomplishment, a message of praise can be displayed. Furthermore, if the user is confused, additional explanation can be provided. For example, if the user is having difficulty understanding, a detailed explanation can be displayed. In this way, by providing feedback according to the user's emotions, user satisfaction can be improved.

[0077] The interpretation support system can further analyze the user's emotions and provide learning content that corresponds to the emotions. For example, if the user is interested, related learning content is suggested. Also, if the user is tired, lighter learning content can be provided. For example, if the user is tired, content that can be learned in a short time is suggested. Furthermore, if the user is feeling stressed, learning content for relaxation can be provided. For example, if the user is feeling stressed, video content for relaxation is suggested. In this way, learning content that corresponds to the user's emotions can be provided, allowing for efficient learning.

[0078] The interpretation support system can further analyze the user's emotions and provide customized notifications according to the emotions. For example, if the user is feeling stressed, a setting to minimize notifications can be provided. Also, if the user is concentrating, a setting to display only important notifications can be provided. For example, only important notifications can be displayed during concentration mode. Furthermore, if the user is tired, a notification urging the user to take a break can be provided. For example, a notification urging the user to take a break can be displayed after working for a long time. In this way, work efficiency can be improved by providing customized notifications according to the user's emotions.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The scanning unit scans a document. For example, the scanning unit scans a paper document with a high-resolution scanner and converts it into digital data. The scanning unit can also directly read electronic documents. For example, it can read a document in PDF format and save it as text data. Furthermore, the scanning unit can also take a photo of a document using a smartphone camera and save it as image data. Step 2: The extraction unit extracts important items from the document scanned by the scanning unit. For example, the extraction unit uses the generation AI to automatically identify important clauses and conditions within the document. The extraction unit can also use the generation AI to extract deadlines and risks within the document. Furthermore, the extraction unit can also use the generation AI to analyze points that require attention within the document. Step 3: The presentation unit presents the important items extracted by the extraction unit to the user. For example, the presentation unit highlights important items on the interface of the app. The presentation unit can also explain points that require attention to the user in an easy-to-understand manner. Furthermore, the presentation unit can present information in a format that is easy for the user to understand.

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] 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.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a scanning unit for scanning a document; an extraction unit that extracts important items from the document scanned by the scanning unit; a presentation unit that presents the important items extracted by the extraction unit to a user. A system characterized by:

2. The scanning unit The physical state of the document is detected, and the generation AI corrects that state and digitizes it.

2. The system of claim 1.

3. The scanning unit The document is automatically translated into multiple languages ​​to accommodate users of different languages.

2. The system of claim 1.

4. The extraction unit The important items extracted by the generation AI are compared with a database of similar past contracts and ranked by importance.

2. The system of claim 1.

5. The presentation unit When presenting points to be aware of, the generative AI will refer to past precedents and legal cases to explain specific risks.

2. The system of claim 1.

6. The scanning unit Using emotion estimation functionality, an interface is provided to reduce the stress and anxiety felt by the user during scanning.

2. The system of claim 1.

7. The extraction unit Using an emotion estimation function, the emotion of the user when checking the important item is analyzed, and supplementary information is provided to improve comprehension.

2. The system of claim 1.

8. The presentation unit Using the emotion estimation function, the emotion expressed by the user when checking points that require attention is analyzed, and supplementary information is provided to improve comprehension.

2. The system of claim 1.

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