system

Abstract

We provide the system. [Solution] A means of receiving a document and converting it into text data according to its data format, A method for analyzing multiple data formats and extracting risk items and important information using generation AI technology, A means of performing a risk assessment from the extracted results and generating a report in which the results can be visualized, A means of providing relevant information in response to user inquiries through interactive question-and-answer sessions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern enterprises, the analysis of complex documents such as contracts and patent drawings has become a heavy burden for legal and intellectual property departments. In particular, for small and medium-sized enterprises and startups operated with limited staff, the review of such documents takes time and effort, and there is a high possibility of insufficient risk management and information oversight. Also, in the case of documents in which text information and drawing information are mixed, there are also technical problems for integrating and analyzing them. It is required to solve these problems.

Means for Solving the Problems

[0005] This invention provides a means for automatically analyzing various data formats using generative AI technology after receiving a document, by performing appropriate data conversion according to the format of the document. Specifically, the received data is converted into text using OCR technology, and the generative AI quickly extracts risk clauses in contracts and technical elements of patent drawings. Furthermore, a means for performing a risk assessment based on these results and generating a visualized report has been developed. In addition, an interactive question-answering function is provided, allowing users to obtain additional information related to the analysis process in real time, thereby supporting effective decision-making.

[0006] A "document" refers to a medium, such as a paper document or an electronic file, on which information is written, including contracts and patent drawings.

[0007] "Generative AI technology" is a technology that uses artificial intelligence to acquire information from text and images, and then performs analysis and predictions.

[0008] A "risk item" is information identified as a potential risk or problem within a contract or document.

[0009] "Important information" refers to information contained in a document that is likely to influence decision-making.

[0010] "Interactive question answering" is a feature that allows users to input questions into the system and receive answers related to those questions from the system in real time.

[0011] "OCR technology" refers to optical character recognition, a technology that converts images and handwritten characters into text data.

[0012] "Natural language processing" is a technology that enables artificial intelligence to understand, interpret, and generate human language.

[0013] "Image recognition technology" is a technology that identifies and understands specific features and patterns from image data.

[0014] A "report" is a document that summarizes the results of an analysis, including the detected information and evaluation results. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Implementing the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0019] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention provides a system for efficiently analyzing contracts and patent drawings. The system's implementation involves three entities: a server, a terminal, and a user.

[0037] First, the user uploads the document to the analysis system. Since the uploaded document may contain a mix of text and image formats, the server first converts these documents into the appropriate data format. For example, OCR technology is used to extract text data from image data. This conversion process prepares the system for processing various data formats in a unified manner.

[0038] Next, the server uses generative AI technology to analyze the document's content. The AI ​​model extracts key information from risk items within the contract and patent drawings, identifying which parts clearly demonstrate business risks. These analysis results serve as foundational data for streamlining subsequent risk management.

[0039] Furthermore, the server generates a visualized report based on the information obtained through analysis, making it easy for users to quickly understand. This report includes all detected risk items and key information, as well as a risk assessment based on them. In addition, the system incorporates an interactive question-answering function for user convenience. When a user asks a question about a specific analysis, the server uses AI to search for the relevant information and provides an immediate answer. This function enables users to make decisions based on the latest information at all times.

[0040] As a concrete example, consider a scenario where a user uploads a supply chain contract and its associated patent drawings to the system. The server analyzes these documents, extracting risk-related information from the contract, such as clauses regarding delivery delays and measures to be taken in case of quality defects. Regarding the patent drawings, it can identify key technical features and point out differences and deficiencies compared to competitors' technologies. All of this information is integrated into a report to support the user's decision-making.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users upload contracts and patent drawings to the system via their terminals. The system can handle uploads even if the documents consist of multiple formats (e.g., PDF, image files, etc.).

[0044] Step 2:

[0045] The terminal receives the uploaded document and sends it to the server. At this time, it adds the document's metadata and source information, preparing it for processing on the server.

[0046] Step 3:

[0047] The server manages the received documents. First, it identifies the document format and extracts text data using OCR technology for images and PDF files. Then, it converts the acquired text data into a format that can be analyzed.

[0048] Step 4:

[0049] The server uses generative AI technology to analyze the content of documents. It extracts risk-related clauses from contracts and identifies technical features from patent drawings. These are efficiently detected using AI's natural language processing and image recognition technologies.

[0050] Step 5:

[0051] The server performs a risk assessment based on the analysis results. It evaluates the identified risk items and deficiencies in the drawings, and creates a report based on their importance and frequency. At that time, it visualizes the data in a format requested by the user, preparing to support the user's decision-making.

[0052] Step 6:

[0053] Users review the generated report on their device. Furthermore, they can use the interactive question-and-answer function to ask the server questions about any unclear points or items of interest regarding the analysis results or report.

[0054] Step 7:

[0055] The server analyzes user questions and generates answers based on existing data and analysis results. The answers are immediately returned to the user, allowing them to make more informed decisions based on the additional information they receive.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In recent years, there has been a growing need to efficiently and accurately analyze the contents of electronic files such as contracts and patent drawings. However, these files are often stored in different formats, making consistent analysis difficult. Furthermore, extracting risk items and important information requires advanced expertise, and doing so manually is time-consuming and laborious. In addition, there is a demand for visualized information that can be quickly understood and used to aid in decision-making. To address these challenges, technology is needed that can handle various data formats and perform advanced analysis automatically.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for receiving electronic files and converting them into character data according to the information format; means for analyzing multiple types of information formats and identifying risk items and important information using generation AI technology; and means for performing a risk assessment from the identified results and creating a report with visualized results. This makes it possible to efficiently extract necessary information from electronic files of different formats and to quickly provide visualized analysis results.

[0061] An "electronic file" refers to documents and images that are digital data stored on a computer.

[0062] "Information format" refers to the specific format in which digital data is stored, such as text format or image format.

[0063] "Text data" refers to text information extracted from digital data in a format that humans can read.

[0064] "Generative AI technology" refers to technology that uses artificial intelligence to analyze digital data and generate specific information or patterns.

[0065] A "risk item" refers to an element related to a potential problem or threat identified within the digital data being analyzed.

[0066] "Important information" refers to particularly noteworthy data elements contained within the digital data being analyzed.

[0067] A "visualizable report" refers to a document created to aid understanding by visually representing analysis results in the form of graphs, tables, and other visual formats.

[0068] "Interactive question answering" refers to an interface that provides immediate and relevant information in response to inquiries made by users to the system.

[0069] A "user interface" refers to a mechanism that provides screens and input methods for users to interact with a system.

[0070] A "computer" refers to an electronic device used for data processing, and can particularly function as a server.

[0071] This invention provides a system for efficiently extracting risk items and important information by analyzing electronic files. The system's implementation primarily involves three entities: a server, a terminal, and a user. Specifically, the invention is implemented as follows.

[0072] Users upload electronic files such as contracts and patent drawings to the system via their terminal. They select and send files through the user interface. Users can also ask questions about the analysis results along the way, utilizing an interactive question-and-answer function.

[0073] The server first converts electronic files received from users into text data according to their format. For image files, OCR technology is used to extract text information. After converting to a unified text data format, the server performs analysis using a generative AI model. This AI model combines natural language processing and image recognition technologies to identify risk items and technically important information within the document. The analysis results are visualized in a way that is easily understandable to the user and generated as a report. Furthermore, the server includes a question-answering function that can immediately provide relevant information in response to user inquiries.

[0074] As a concrete example, consider a case where a user uploads a manufacturing contract and its associated drawings. In this case, the server extracts information related to "supply obligations" and "quality assurance" from the manufacturing contract. Meanwhile, it can analyze the drawings to identify the product's unique technical characteristics. All of these analysis results are integrated into a report and provided to the user.

[0075] An example of a prompt might be, "Please extract the risk items that require particular attention within the contract." The system works by using this prompt to allow a generating AI model to analyze the data and provide the user with the necessary information.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users upload the electronic files to be analyzed to the system via a terminal. Using a dedicated interface on the terminal, they select the necessary files and press the send button, which transfers the files to the server. The input consists of electronic files in multiple data formats selected by the user. The output is the storage of these files in the system.

[0079] Step 2:

[0080] The server converts received electronic files into text data according to their format. For image files, OCR technology is used to extract text from the image. Inputs include images and PDF files, and the output is a text file in a standardized character data format. Data processing involves evaluating the file format and performing appropriate text extraction.

[0081] Step 3:

[0082] The server analyzes text data using a generative AI model. By applying the generative AI model and utilizing natural language processing, it identifies risk items and important information from documents. A text file in a unified character data format is used as input, and the output is a list of identified risk items and important information. Here, language analysis is performed as a data calculation.

[0083] Step 4:

[0084] The server performs a risk assessment based on the analysis results and creates a visualized report. It utilizes data visualization tools, including the creation of pie charts and bar graphs, to clearly present the analysis results. The input is a list of analysis results obtained in step 3, and the output is a graphical report based on this list. Data processing involves structuring and visualizing the data based on the analysis results.

[0085] Step 5:

[0086] Users review the generated report and utilize an interactive question-answering system as needed. Through this system, they can obtain additional information about the analysis results and inquire about specific items in more detail. Input consists of natural language questions from the user, and output is an answer containing relevant information to the question. Data processing involves analyzing the question and retrieving related information.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] In modern business operations, quickly and accurately identifying risks hidden within legal and technical documents is crucial for maintaining business safety and efficiency. However, these documents often come in multiple formats and contain a wealth of information, making manual analysis time-consuming and labor-intensive. Furthermore, identifying and evaluating risk areas relies on human judgment, leading to risks of oversight and misinterpretation. To address these challenges, there is a need for technology that automatically analyzes documents, identifies and evaluates risks, and rapidly provides visualized information.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes means for receiving information and converting it into character data according to the information format; means for analyzing multiple types of information formats and extracting risk items and important information using machine learning technology; means for performing a risk assessment from the extraction results and generating the results as a report that can be visualized; means for providing relevant information in response to inquiries from users through interactive question and answer; and means including optical character recognition technology for acquiring documents as images using a camera and extracting characters from the images. This makes it possible to efficiently identify and assess risks contained in legal and technical documents and to support users in making quick decisions.

[0092] "Information" is a general term for various types of content, such as documents, images, and audio, that are recognized as data.

[0093] "Character data" refers to digital information that has been converted into a format that can be processed as text.

[0094] "Machine learning technology" is a technique for automatically learning features from data and identifying specific patterns or rules.

[0095] A "risk item" is information that indicates potential business or legal problems or vulnerabilities.

[0096] "Important information" refers to high-value data that should be given particular priority in decision-making.

[0097] A "visualizable report" is a document that presents data in an intuitively understandable format using charts, graphs, and other visual aids.

[0098] "Interactive question answering" is a function that answers user inquiries in a real-time, conversational format.

[0099] A "photography device" is a device used to capture objects or documents as images.

[0100] "Optical character recognition technology" is a technology that analyzes characters in an image and converts them into text data.

[0101] This invention provides a system for efficiently processing information. The server receives information in various formats uploaded by users and converts it into text data. This conversion process utilizes optical character recognition (OCR), such as Tesseract. This allows documents uploaded as images to also be converted into a parseable text format.

[0102] The converted text data is analyzed on the server using machine learning techniques, specifically natural language processing (NLP) and image analysis. This process utilizes generative AI models to extract risk items and important information. Possible AI models used include OpenAI's GPT model. Based on these results, the system generates a visualized report and provides it to the user.

[0103] Furthermore, users can access detailed information about each risk item and key information listed in the report through an interactive question-and-answer function. This function operates in real time to support user decision-making.

[0104] As a concrete example, consider a case where a user takes a picture of a commercial contract with their smartphone camera and uploads it to this system. The system automatically extracts risk factors such as "confidentiality clauses" and "terms and conditions" from the contract and provides the user with this information quickly and clearly visualized. In this way, users can make appropriate business decisions based on a rapid and accurate risk assessment.

[0105] An example of a prompt message supplied to a generating AI model is: "Identify business risks from the following text. Pay particular attention to clauses regarding delivery delays, poor quality, and confidentiality."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user selects and uploads the document they want to analyze via the terminal's interface. The input provided is either a digital image or scanned data of the document. To ensure secure transmission of the received data to the server, encryption technology is used during data transfer.

[0109] Step 2:

[0110] The server converts the received document into a format that can be analyzed. The input is image data received from the terminal, and the output is text data. The server uses optical character recognition (OCR) technology such as Tesseract to convert the text information in the image into text format. During this process, OCR processing is used to distinguish between text and non-text areas in the image and extract the text.

[0111] Step 3:

[0112] The server analyzes text data to extract important information. The input is the text data obtained in step 2, and the output is the analysis results, including risk items and important information. The server uses a generative AI model to analyze the text and identify risks and key technical features within the contract. The analysis includes a process that leverages natural language processing to understand the context of the document and the meaning of words.

[0113] Step 4:

[0114] The server converts the analysis results into a visualized report. The input is the analysis results from step 3, and the output is the visualized report. In this step, extracted risk items and key information are displayed in a user-friendly format using graphs and charts. A data processing program is used for visualization, and the information is appropriately formatted.

[0115] Step 5:

[0116] Users can review reports and ask additional questions. The server provides interactive responses to these questions. The input is the user's question, and the output is the server's answer. The server uses a generative AI model to understand the intent of the question and quickly provide relevant information. The question-answering process utilizes natural language processing techniques to analyze the context of the question and select appropriate information.

[0117] Throughout this entire process, users can efficiently and accurately identify risk items in documents and obtain information to support decision-making based on those items.

[0118] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0119] This invention is a system that combines document analysis with emotion recognition technology, enabling more effective analysis of contracts and patent drawings. The system consists of a server, a terminal, and a user, and by using an emotion engine, it incorporates the user's emotional state into the analysis process, providing a more intuitive and user-centered interface.

[0120] First, users upload contracts and patent drawings to the system via a terminal. The terminal is equipped with a user interface to facilitate document uploads. This interface is designed to be intuitive to enhance user convenience.

[0121] The device uses its camera and microphone to analyze the user's facial expressions and voice along with the uploaded documents, collecting user emotion data. This allows monitoring of the user's emotions while they are analyzing the documents. The emotion engine analyzes this data in real time to identify the user's emotional state.

[0122] Next, the server receives the document and analyzes its contents using OCR and generative AI technologies. It extracts key technical features from risk items within the contract and patent drawings, and performs a risk assessment based on these. The analysis results are generated as a report and presented to the user.

[0123] The emotion engine recognizes the user's emotional state, and the server adjusts how the analysis results are presented according to the user's emotions. For example, if the user is confused, the analysis results can be presented in more detail and in an easy-to-understand manner. Furthermore, based on the user's emotions, risk items and important information deemed to require particular attention are prioritized for display.

[0124] Furthermore, in addition to presenting reports, users can use their devices to engage in interactive question-and-answer sessions about the analysis results. The server identifies relevant information in response to the user's questions and adjusts the tone and content of the responses based on feedback from the sentiment engine. This provides users with more appropriate answers and improves the overall user experience.

[0125] For example, if a user uploads an intellectual property agreement and related technical drawings, and the emotion engine detects a cautious attitude from the user's facial expressions, the server will present a detailed assessment of risk items and provide supplementary information anticipating any questions the user might have. This information helps the user make important decisions with confidence.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] Users upload documents, including contracts and patent drawings, to the system interface using their terminals. The interface includes file selection and drag-and-drop functions, making it easy to send documents.

[0129] Step 2:

[0130] As the device uploads a document, it simultaneously collects emotional data from the user's facial expressions and voice using its built-in camera and microphone. The collected data is analyzed by an emotion engine, which identifies the user's emotional state in real time.

[0131] Step 3:

[0132] The server receives the uploaded documents. It automatically determines the document format and converts image data into text data using OCR technology. This process prepares all data for analysis in a unified format.

[0133] Step 4:

[0134] The server uses generative AI technology to analyze contracts and patent drawings. It combines natural language processing and image recognition technologies to extract risk items and key technical information. The analysis results are stored in an internal database and used in subsequent processes.

[0135] Step 5:

[0136] Based on feedback from the emotion engine, the server adjusts how it presents analysis results according to the user's emotional state. If the emotion is determined to be anxiety, it prepares to present a risk report in a detailed and easy-to-understand format.

[0137] Step 6:

[0138] The server generates a visualized report using the analysis results. The report includes prominent risk items and key information. Prioritized information based on sentiment recognition data is also reflected.

[0139] Step 7:

[0140] Users can view the generated reports using their devices. If they have questions about the analysis results, they can use the interactive question-and-answer function to request additional information or detailed explanations from the server.

[0141] Step 8:

[0142] The server receives the user's question and retrieves the necessary information from the relevant database. It constructs a response in a tone that reflects the user's emotions and presents the answer to the user via the terminal. This allows the user to gain a clearer understanding.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] Conventional document analysis systems provide analysis results without considering the user's emotions, resulting in a problem where they cannot present appropriate information according to the user's level of understanding or emotional state. Furthermore, the supplementation of analyzed information and risk assessment may be insufficient, making it difficult for users to obtain the information necessary for decision-making.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes means for receiving documents and converting them into information according to the data format; means for analyzing multiple types of data formats and extracting risk information and important elements using generative AI technology; and means for identifying the user's emotional state using emotion recognition technology and adjusting the presentation method of the analysis results based on the user's emotions. This makes it possible to provide appropriate information according to the user's emotional state, enabling the user to efficiently acquire the information necessary for decision-making.

[0148] A "document" is a written document containing information in text or drawing format, including contracts and technical drawings.

[0149] "Data format" refers to the form or structure in which information is represented or stored, and includes text, images, videos, and so on.

[0150] An "information processing device" is a device that analyzes received data, extracts specific information, and outputs the results.

[0151] "Generative AI technology" is a technology that uses artificial intelligence to generate new information and insights from text data and image data.

[0152] "Risk information" refers to information that contains potential risks related to contracts and drawings, and this is a point of caution for users.

[0153] "Key elements" refer to particularly noteworthy information or technical details included within a document.

[0154] "Emotion recognition technology" is a technology that detects and identifies a user's emotional state from their facial expressions and voice.

[0155] "Information presentation" refers to the act of providing the user with the analyzed results visually or audibly.

[0156] "Interactive question answering" refers to a two-way communication system where the user asks a question to the system and the system provides an appropriate answer.

[0157] This system is a document analysis system that combines emotion recognition technology to more effectively analyze contracts and patent drawings. The system consists of three components: a server, terminals, and users.

[0158] Users upload contracts and patent drawings via their devices. These devices feature an intuitive user interface with file selection buttons and drag-and-drop functionality, making document uploading easy for users.

[0159] The device collects the user's facial expressions and voice using its built-in camera and microphone, along with uploaded documents. This allows the device to acquire data in real time for analyzing the user's emotional state. By applying emotion recognition technology, it identifies what emotions the user is experiencing.

[0160] The server analyzes received documents using OCR technology and a generative AI model. The OCR technology converts the document into text data, and the generative AI model analyzes that text data to identify risk information and key elements. Based on these analysis results, the server performs a risk assessment and generates a report with visualized results.

[0161] Once the emotion engine identifies the user's emotional state, the server adjusts how the analysis results are presented based on the user's emotions. For example, if the user is confused, the server can provide more detailed and easily understandable information by adding explanations to the analysis results.

[0162] Users can use their devices to ask questions about the analysis results. Interactive question-and-answer sessions allow users to obtain detailed information on areas of interest. The server identifies relevant information in response to the user's questions and adjusts its responses based on sentiment feedback to provide accurate answers.

[0163] For example, if a user uploads an intellectual property contract and their facial expression indicates a cautious attitude, the server will present a detailed assessment of the risk items and provide supplementary information that anticipates the user's questions. As a result, the user can make decisions with confidence.

[0164] An example of a prompt message is, "Please provide a detailed explanation of the specific risk items in the contract and offer examples to alleviate user confusion."

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] Users upload contracts and patent drawings using a terminal. The terminal's user interface is intuitive, allowing users to select files using buttons and drag-and-drop functionality. Input is the document file selected by the user, and output is its transmission to the server.

[0168] Step 2:

[0169] The device records the user's facial expressions and voice using its built-in camera and microphone, simultaneously with the uploaded documents. This data is collected to determine the user's emotional state. The input is the user's real-time facial expressions and voice, and the output is the aggregation of this data and its transmission to the server.

[0170] Step 3:

[0171] The server converts received documents into text data using OCR technology. OCR processing recognizes characters from paper documents and image files into text format. The input is image data of the document, and the output is the converted text data.

[0172] Step 4:

[0173] The server analyzes this text data using a generative AI model. The analysis aims to identify risk information and key technological elements. The input is the text data obtained in step 3, and the output is the extracted risk information and key elements.

[0174] Step 5:

[0175] The server uses emotion recognition technology to analyze emotion data transmitted from the terminal. This allows for a detailed identification of the user's emotional state. The input is data of the user's facial expressions and voice, and the output is the identified emotional state.

[0176] Step 6:

[0177] The server customizes and presents the analysis results according to the user's emotional state. For example, if the user is confused, the analysis will be made more detailed and additional explanations will be added. The input is the output data from steps 4 and 5, and the output is the adjusted analysis results report.

[0178] Step 7:

[0179] Users can ask further questions about the presented analysis results. They request additional information using the terminal's question-answering interface. The input is the user's question, and the output is the answer provided by the server.

[0180] Step 8:

[0181] The server searches for relevant information based on the user's question and adjusts the tone of its response based on the user's emotional state. The input is data on the user's question and emotional state, and the output is the adjusted response.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0184] Modern document analysis requires not only the identification of risk items and important information in contracts and patent drawings, but also analysis that takes into account the user's emotional state. However, conventional technologies have made it difficult to consider both the content of the document and the user's emotions simultaneously, limiting the means to realize a user-centered interface.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for receiving documents and converting them into text data according to the data format; means for analyzing multiple types of data formats and extracting risk items and important information using generative artificial intelligence technology; and means for recognizing the user's emotional state and adjusting the method of presenting the analysis results according to that emotion. This enables intuitive and effective document analysis and information provision that is tailored to the user's emotions.

[0187] "Documents" are a general term for papers and materials that organize and formalize information, and include specific content such as contracts and patent drawings.

[0188] "Generative artificial intelligence technology" refers to technologies that generate new information and content from data learned by computers, and includes natural language processing and image recognition.

[0189] "User emotional state" refers to changes in the user's psychological state as perceived from their facial expressions, voice, etc., and is information used to adjust the presentation method of the analysis results.

[0190] A "risk item" refers to a potential danger or a part of a contract or patent drawing that requires attention, and is an item that requires particular care.

[0191] "Analysis results" refer to information obtained after document analysis, and include evaluation results of extracted risk items and important information.

[0192] Users upload documents such as contracts and patent drawings to the system via a terminal. The terminal provides a user interface and facilitates document uploads. The terminal is also equipped with a camera and microphone to analyze the user's facial expressions and voice, collecting emotional data. This emotional data is used to identify the user's emotional state.

[0193] The server receives uploaded documents and converts them into text data according to their format. Next, it uses generative artificial intelligence technology to extract risk items and key information from contracts and patent drawings. Furthermore, it can adjust how the analysis results are presented based on the user's emotional state. This ensures that information is presented in a way that is easy for the user to understand, improving the user experience.

[0194] The hardware required includes a general-purpose terminal with camera functionality, and a high-performance information processing unit as the server. The software used will include OpenCV, PyTorch, and natural language processing libraries (SpaCy and Transformers). By combining these tools, emotion recognition and document analysis will be achieved.

[0195] For example, when a company's legal department reviews a new security agreement, if the user is overwhelmed by the complexity of the agreement, the system includes features to facilitate user understanding, such as providing detailed explanations of risk items and relevant supplementary information. To support this operation, the generating AI model can use the following prompt: "List the key risk points of this security agreement in bullet points and provide additional information to aid in decision-making."

[0196] This system provides users with an environment where they can make important decisions with confidence.

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] Users upload documents such as contracts and patent drawings to the system via a terminal. The input consists of document data selected by the user, and the output is a digital file transferred to the server. A file selection window opens through the user interface, allowing the user to select a document and press the submit button.

[0200] Step 2:

[0201] The device uses a camera and microphone to simultaneously record the user's facial expressions and voice, collecting emotional data. Input consists of camera video and audio data, while output is analytical data representing the user's emotional state. OpenCV is used to detect facial feature points in real time, and these are analyzed along with audio data using a PyTorch-trained model.

[0202] Step 3:

[0203] The server converts received documents into a text format suitable for analysis. The input is uploaded documents, and the output is data in a text-analyzable format. It uses OCR technology to extract text information from images, PDFs, and other formats, and then converts it to text format using a natural language processing library.

[0204] Step 4:

[0205] The server uses a generative AI model to extract risk items and key information from documents. The input is text data, and the output is a set of extracted risk items and key information. The generative AI model is prompted with the message, "List the key risk points of this security agreement in bullet points, and provide additional information to aid in decision-making," and then performs the action of researching relevant information.

[0206] Step 5:

[0207] The server adjusts how the analysis results are presented, taking into account the user's emotional state. The input consists of emotional data and analysis results, while the output is an optimized report presented to the user. If the user appears confused, the server highlights important information and provides additional relevant details.

[0208] Step 6:

[0209] Users can interact with the generated report and ask questions to supplement the explanation. The input is user-submitted question data, and the output is the answer provided by the server. A question-answering system using natural language processing analyzes the user's inquiry and adjusts the tone of the answer based on sentiment feedback.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] This invention provides a system for efficiently analyzing contracts and patent drawings. The system's implementation involves three entities: a server, a terminal, and a user.

[0227] First, the user uploads the document to the analysis system. Since the uploaded document may contain a mix of text and image formats, the server first converts these documents into the appropriate data format. For example, OCR technology is used to extract text data from image data. This conversion process prepares the system for processing various data formats in a unified manner.

[0228] Next, the server uses generative AI technology to analyze the document's content. The AI ​​model extracts key information from risk items within the contract and patent drawings, identifying which parts clearly demonstrate business risks. These analysis results serve as foundational data for streamlining subsequent risk management.

[0229] Furthermore, the server generates a visualized report based on the information obtained through analysis, making it easy for users to quickly understand. This report includes all detected risk items and key information, as well as a risk assessment based on them. In addition, the system incorporates an interactive question-answering function for user convenience. When a user asks a question about a specific analysis, the server uses AI to search for the relevant information and provides an immediate answer. This function enables users to make decisions based on the latest information at all times.

[0230] As a concrete example, consider a scenario where a user uploads a supply chain contract and its associated patent drawings to the system. The server analyzes these documents, extracting risk-related information from the contract, such as clauses regarding delivery delays and measures to be taken in case of quality defects. Regarding the patent drawings, it can identify key technical features and point out differences and deficiencies compared to competitors' technologies. All of this information is integrated into a report to support the user's decision-making.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] Users upload contracts and patent drawings to the system via their terminals. The system can handle uploads even if the documents consist of multiple formats (e.g., PDF, image files, etc.).

[0234] Step 2:

[0235] The terminal receives the uploaded document and sends it to the server. At this time, it adds the document's metadata and source information, preparing it for processing on the server.

[0236] Step 3:

[0237] The server manages the received documents. First, it identifies the document format and extracts text data using OCR technology for images and PDF files. Then, it converts the acquired text data into a format that can be analyzed.

[0238] Step 4:

[0239] The server uses generative AI technology to analyze the content of documents. It extracts risk-related clauses from contracts and identifies technical features from patent drawings. These are efficiently detected using AI's natural language processing and image recognition technologies.

[0240] Step 5:

[0241] The server performs a risk assessment based on the analysis results. It evaluates the identified risk items and deficiencies in the drawings, and creates a report based on their importance and frequency. At that time, it visualizes the data in a format requested by the user, preparing to support the user's decision-making.

[0242] Step 6:

[0243] Users review the generated report on their device. Furthermore, they can use the interactive question-and-answer function to ask the server questions about any unclear points or items of interest regarding the analysis results or report.

[0244] Step 7:

[0245] The server analyzes user questions and generates answers based on existing data and analysis results. The answers are immediately returned to the user, allowing them to make more informed decisions based on the additional information they receive.

[0246] (Example 1)

[0247] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0248] In recent years, there has been a growing need to efficiently and accurately analyze the contents of electronic files such as contracts and patent drawings. However, these files are often stored in different formats, making consistent analysis difficult. Furthermore, extracting risk items and important information requires advanced expertise, and doing so manually is time-consuming and laborious. In addition, there is a demand for visualized information that can be quickly understood and used to aid in decision-making. To address these challenges, technology is needed that can handle various data formats and perform advanced analysis automatically.

[0249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0250] In this invention, the server includes means for receiving electronic files and converting them into character data according to the information format; means for analyzing multiple types of information formats and identifying risk items and important information using generation AI technology; and means for performing a risk assessment from the identified results and creating a report with visualized results. This makes it possible to efficiently extract necessary information from electronic files of different formats and to quickly provide visualized analysis results.

[0251] An "electronic file" refers to documents and images that are digital data stored on a computer.

[0252] "Information format" refers to the specific format in which digital data is stored, such as text format or image format.

[0253] "Text data" refers to text information extracted from digital data in a format that humans can read.

[0254] "Generative AI technology" refers to technology that uses artificial intelligence to analyze digital data and generate specific information or patterns.

[0255] A "risk item" refers to an element related to a potential problem or threat identified within the digital data being analyzed.

[0256] "Important information" refers to particularly noteworthy data elements contained within the digital data being analyzed.

[0257] A "visualizable report" refers to a document created to aid understanding by visually representing analysis results in the form of graphs, tables, and other visual formats.

[0258] "Interactive question answering" refers to an interface that provides immediate and relevant information in response to inquiries made by users to the system.

[0259] A "user interface" refers to a mechanism that provides screens and input methods for users to interact with a system.

[0260] A "computer" refers to an electronic device used for data processing, and can particularly function as a server.

[0261] This invention provides a system for efficiently extracting risk items and important information by analyzing electronic files. The system's implementation primarily involves three entities: a server, a terminal, and a user. Specifically, the invention is implemented as follows.

[0262] Users upload electronic files such as contracts and patent drawings to the system via their terminal. They select and send files through the user interface. Users can also ask questions about the analysis results along the way, utilizing an interactive question-and-answer function.

[0263] The server first converts electronic files received from users into text data according to their format. For image files, OCR technology is used to extract text information. After converting to a unified text data format, the server performs analysis using a generative AI model. This AI model combines natural language processing and image recognition technologies to identify risk items and technically important information within the document. The analysis results are visualized in a way that is easily understandable to the user and generated as a report. Furthermore, the server includes a question-answering function that can immediately provide relevant information in response to user inquiries.

[0264] As a concrete example, consider a case where a user uploads a manufacturing contract and its associated drawings. In this case, the server extracts information related to "supply obligations" and "quality assurance" from the manufacturing contract. Meanwhile, it can analyze the drawings to identify the product's unique technical characteristics. All of these analysis results are integrated into a report and provided to the user.

[0265] An example of a prompt might be, "Please extract the risk items that require particular attention within the contract." The system works by using this prompt to allow a generating AI model to analyze the data and provide the user with the necessary information.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] Users upload the electronic files to be analyzed to the system via a terminal. Using a dedicated interface on the terminal, they select the necessary files and press the send button, which transfers the files to the server. The input consists of electronic files in multiple data formats selected by the user. The output is the storage of these files in the system.

[0269] Step 2:

[0270] The server converts received electronic files into text data according to their format. For image files, OCR technology is used to extract text from the image. Inputs include images and PDF files, and the output is a text file in a standardized character data format. Data processing involves evaluating the file format and performing appropriate text extraction.

[0271] Step 3:

[0272] The server analyzes text data using a generative AI model. By applying the generative AI model and utilizing natural language processing, it identifies risk items and important information from documents. A text file in a unified character data format is used as input, and the output is a list of identified risk items and important information. Here, language analysis is performed as a data calculation.

[0273] Step 4:

[0274] The server performs a risk assessment based on the analysis results and creates a visualized report. It utilizes data visualization tools, including the creation of pie charts and bar graphs, to clearly present the analysis results. The input is a list of analysis results obtained in step 3, and the output is a graphical report based on this list. Data processing involves structuring and visualizing the data based on the analysis results.

[0275] Step 5:

[0276] Users review the generated report and utilize an interactive question-answering system as needed. Through this system, they can obtain additional information about the analysis results and inquire about specific items in more detail. Input consists of natural language questions from the user, and output is an answer containing relevant information to the question. Data processing involves analyzing the question and retrieving related information.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] In modern business operations, quickly and accurately identifying risks hidden within legal and technical documents is crucial for maintaining business safety and efficiency. However, these documents often come in multiple formats and contain a wealth of information, making manual analysis time-consuming and labor-intensive. Furthermore, identifying and evaluating risk areas relies on human judgment, leading to risks of oversight and misinterpretation. To address these challenges, there is a need for technology that automatically analyzes documents, identifies and evaluates risks, and rapidly provides visualized information.

[0280] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes means for receiving information and converting it into character data according to the information format; means for analyzing multiple types of information formats and extracting risk items and important information using machine learning technology; means for performing a risk assessment from the extraction results and generating the results as a report that can be visualized; means for providing relevant information in response to inquiries from users through interactive question and answer; and means including optical character recognition technology for acquiring documents as images using a camera and extracting characters from the images. This makes it possible to efficiently identify and assess risks contained in legal and technical documents and to support users in making quick decisions.

[0282] "Information" is a general term for various types of content such as documents, images, and audio that are recognized as data.

[0283] "Character data" is digital information that has been converted into a form that can be processed as text.

[0284] "Machine learning technology" is a technology for automatically learning features from data and identifying specific patterns or laws.

[0285] "Risk item" is information indicating potential problems or vulnerabilities in business or legal matters.

[0286] "Important information" is highly valuable data that should be given particular priority in decision-making.

[0287] "Visualizable report" is a document that presents data in an intuitive and easy-to-understand form using charts, graphs, etc.

[0288] "Interactive question and answer" is a function that responds to inquiries from users in a real-time dialogue format.

[0289] "Imaging device" is a device for capturing objects or documents as images.

[0290] "Optical character recognition technology" is a technology for analyzing characters in an image and converting them into text data.

[0291] This invention provides a system for efficiently processing information. The server receives various types of information uploaded by users and converts that information into text data. In this conversion operation, for example, optical character recognition technology such as Tesseract is used. As a result, documents uploaded as images are also converted into an analyzable text format.

[0292] The converted text data is analyzed on the server using machine learning techniques, specifically natural language processing (NLP) and image analysis. This process utilizes generative AI models to extract risk items and important information. Possible AI models used include OpenAI's GPT model. Based on these results, the system generates a visualized report and provides it to the user.

[0293] Furthermore, users can access detailed information about each risk item and key information listed in the report through an interactive question-and-answer function. This function operates in real time to support user decision-making.

[0294] As a concrete example, consider a case where a user takes a picture of a commercial contract with their smartphone camera and uploads it to this system. The system automatically extracts risk factors such as "confidentiality clauses" and "terms and conditions" from the contract and provides the user with this information quickly and clearly visualized. In this way, users can make appropriate business decisions based on a rapid and accurate risk assessment.

[0295] An example of a prompt message supplied to a generating AI model is: "Identify business risks from the following text. Pay particular attention to clauses regarding delivery delays, poor quality, and confidentiality."

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The user selects and uploads the document they want to analyze via the terminal's interface. The input provided is either a digital image or scanned data of the document. To ensure secure transmission of the received data to the server, encryption technology is used during data transfer.

[0299] Step 2:

[0300] The server converts the received document into a format that can be analyzed. The input is image data received from the terminal, and the output is text data. The server uses optical character recognition (OCR) technology such as Tesseract to convert the text information in the image into text format. During this process, OCR processing is used to distinguish between text and non-text areas in the image and extract the text.

[0301] Step 3:

[0302] The server analyzes text data to extract important information. The input is the text data obtained in step 2, and the output is the analysis results, including risk items and important information. The server uses a generative AI model to analyze the text and identify risks and key technical features within the contract. The analysis includes a process that leverages natural language processing to understand the context of the document and the meaning of words.

[0303] Step 4:

[0304] The server converts the analysis results into a visualized report. The input is the analysis results from step 3, and the output is the visualized report. In this step, extracted risk items and key information are displayed in a user-friendly format using graphs and charts. A data processing program is used for visualization, and the information is appropriately formatted.

[0305] Step 5:

[0306] Users can review reports and ask additional questions. The server provides interactive responses to these questions. The input is the user's question, and the output is the server's answer. The server uses a generative AI model to understand the intent of the question and quickly provide relevant information. The question-answering process utilizes natural language processing techniques to analyze the context of the question and select appropriate information.

[0307] Throughout this process, the user can efficiently and accurately identify the risk items of the document and obtain information to support decision-making based on them.

[0308] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0309] This invention is a system that combines emotion recognition technology with document analysis, and can more effectively analyze contracts and patent drawings. The system is composed of a server, a terminal, and a user. By using an emotion engine, the user's emotional state is incorporated into the analysis process, providing a more intuitive and user-centered interface.

[0310] First, the user uploads a contract or patent drawing to the system via the terminal. The terminal is equipped with a user interface to facilitate the upload of documents. This interface has an intuitive design to enhance the user's convenience.

[0311] The terminal analyzes the user's expression and voice using a camera and microphone together with the uploaded document, and collects the user's emotion data. This allows monitoring of what emotions the user has during document analysis. The emotion engine analyzes this data in real time to identify the user's emotional state.

[0312] Next, the server receives the document and analyzes the content of the document using OCR technology and generative AI technology. It extracts risk items in the contract and important technical features of the patent drawing, and performs a risk assessment based on them. The analysis result is generated as a report and presented to the user.

[0313] The emotion engine recognizes the user's emotional state, and the server adjusts how the analysis results are presented according to the user's emotions. For example, if the user is confused, the analysis results can be presented in more detail and in an easy-to-understand manner. Furthermore, based on the user's emotions, risk items and important information deemed to require particular attention are prioritized for display.

[0314] Furthermore, in addition to presenting reports, users can use their devices to engage in interactive question-and-answer sessions about the analysis results. The server identifies relevant information in response to the user's questions and adjusts the tone and content of the responses based on feedback from the sentiment engine. This provides users with more appropriate answers and improves the overall user experience.

[0315] For example, if a user uploads an intellectual property agreement and related technical drawings, and the emotion engine detects a cautious attitude from the user's facial expressions, the server will present a detailed assessment of risk items and provide supplementary information anticipating any questions the user might have. This information helps the user make important decisions with confidence.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] Users upload documents, including contracts and patent drawings, to the system interface using their terminals. The interface includes file selection and drag-and-drop functions, making it easy to send documents.

[0319] Step 2:

[0320] As the device uploads a document, it simultaneously collects emotional data from the user's facial expressions and voice using its built-in camera and microphone. The collected data is analyzed by an emotion engine, which identifies the user's emotional state in real time.

[0321] Step 3:

[0322] The server receives the uploaded documents. It automatically determines the document format and converts image data into text data using OCR technology. This process prepares all data for analysis in a unified format.

[0323] Step 4:

[0324] The server uses generative AI technology to analyze contracts and patent drawings. It combines natural language processing and image recognition technologies to extract risk items and key technical information. The analysis results are stored in an internal database and used in subsequent processes.

[0325] Step 5:

[0326] Based on feedback from the emotion engine, the server adjusts how it presents analysis results according to the user's emotional state. If the emotion is determined to be anxiety, it prepares to present a risk report in a detailed and easy-to-understand format.

[0327] Step 6:

[0328] The server generates a visualized report using the analysis results. The report includes prominent risk items and key information. Prioritized information based on sentiment recognition data is also reflected.

[0329] Step 7:

[0330] Users can view the generated reports using their devices. If they have questions about the analysis results, they can use the interactive question-and-answer function to request additional information or detailed explanations from the server.

[0331] Step 8:

[0332] The server receives the user's question and retrieves the necessary information from the relevant database. It constructs a response in a tone that reflects the user's emotions and presents the answer to the user via the terminal. This allows the user to gain a clearer understanding.

[0333] (Example 2)

[0334] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0335] Conventional document analysis systems provide analysis results without considering the user's emotions, resulting in a problem where they cannot present appropriate information according to the user's level of understanding or emotional state. Furthermore, the supplementation of analyzed information and risk assessment may be insufficient, making it difficult for users to obtain the information necessary for decision-making.

[0336] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0337] In this invention, the server includes means for receiving documents and converting them into information according to the data format; means for analyzing multiple types of data formats and extracting risk information and important elements using generative AI technology; and means for identifying the user's emotional state using emotion recognition technology and adjusting the presentation method of the analysis results based on the user's emotions. This makes it possible to provide appropriate information according to the user's emotional state, enabling the user to efficiently acquire the information necessary for decision-making.

[0338] A "document" is a written document containing information in text or drawing format, including contracts and technical drawings.

[0339] "Data format" refers to the form or structure in which information is represented or stored, and includes text, images, videos, and so on.

[0340] An "information processing device" is a device that analyzes received data, extracts specific information, and outputs the results.

[0341] "Generative AI technology" is a technology that uses artificial intelligence to generate new information and insights from text data and image data.

[0342] "Risk information" refers to information that contains potential risks related to contracts and drawings, and this is a point of caution for users.

[0343] "Key elements" refer to particularly noteworthy information or technical details included within a document.

[0344] "Emotion recognition technology" is a technology that detects and identifies a user's emotional state from their facial expressions and voice.

[0345] "Information presentation" refers to the act of providing the user with the analyzed results visually or audibly.

[0346] "Interactive question answering" refers to a two-way communication system where the user asks a question to the system and the system provides an appropriate answer.

[0347] This system is a document analysis system that combines emotion recognition technology to more effectively analyze contracts and patent drawings. The system consists of three components: a server, terminals, and users.

[0348] Users upload contracts and patent drawings via their devices. These devices feature an intuitive user interface with file selection buttons and drag-and-drop functionality, making document uploading easy for users.

[0349] The device collects the user's facial expressions and voice using its built-in camera and microphone, along with uploaded documents. This allows the device to acquire data in real time for analyzing the user's emotional state. By applying emotion recognition technology, it identifies what emotions the user is experiencing.

[0350] The server analyzes received documents using OCR technology and a generative AI model. The OCR technology converts the document into text data, and the generative AI model analyzes that text data to identify risk information and key elements. Based on these analysis results, the server performs a risk assessment and generates a report with visualized results.

[0351] Once the emotion engine identifies the user's emotional state, the server adjusts how the analysis results are presented based on the user's emotions. For example, if the user is confused, the server can provide more detailed and easily understandable information by adding explanations to the analysis results.

[0352] Users can use their devices to ask questions about the analysis results. Interactive question-and-answer sessions allow users to obtain detailed information on areas of interest. The server identifies relevant information in response to the user's questions and adjusts its responses based on sentiment feedback to provide accurate answers.

[0353] For example, if a user uploads an intellectual property contract and their facial expression indicates a cautious attitude, the server will present a detailed assessment of the risk items and provide supplementary information that anticipates the user's questions. As a result, the user can make decisions with confidence.

[0354] An example of a prompt message is, "Please provide a detailed explanation of the specific risk items in the contract and offer examples to alleviate user confusion."

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] Users upload contracts and patent drawings using a terminal. The terminal's user interface is intuitive, allowing users to select files using buttons and drag-and-drop functionality. Input is the document file selected by the user, and output is its transmission to the server.

[0358] Step 2:

[0359] The device records the user's facial expressions and voice using its built-in camera and microphone, simultaneously with the uploaded documents. This data is collected to determine the user's emotional state. The input is the user's real-time facial expressions and voice, and the output is the aggregation of this data and its transmission to the server.

[0360] Step 3:

[0361] The server converts received documents into text data using OCR technology. OCR processing recognizes characters from paper documents and image files into text format. The input is image data of the document, and the output is the converted text data.

[0362] Step 4:

[0363] The server analyzes this text data using a generative AI model. The analysis aims to identify risk information and key technological elements. The input is the text data obtained in step 3, and the output is the extracted risk information and key elements.

[0364] Step 5:

[0365] The server uses emotion recognition technology to analyze emotion data transmitted from the terminal. This allows for a detailed identification of the user's emotional state. The input is data of the user's facial expressions and voice, and the output is the identified emotional state.

[0366] Step 6:

[0367] The server customizes and presents the analysis results according to the user's emotional state. For example, if the user is confused, the analysis will be made more detailed and additional explanations will be added. The input is the output data from steps 4 and 5, and the output is the adjusted analysis results report.

[0368] Step 7:

[0369] Users can ask further questions about the presented analysis results. They request additional information using the terminal's question-answering interface. The input is the user's question, and the output is the answer provided by the server.

[0370] Step 8:

[0371] The server searches for relevant information based on the user's question and adjusts the tone of its response based on the user's emotional state. The input is data on the user's question and emotional state, and the output is the adjusted response.

[0372] (Application Example 2)

[0373] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0374] Modern document analysis requires not only the identification of risk items and important information in contracts and patent drawings, but also analysis that takes into account the user's emotional state. However, conventional technologies have made it difficult to consider both the content of the document and the user's emotions simultaneously, limiting the means to realize a user-centered interface.

[0375] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0376] In this invention, the server includes means for receiving documents and converting them into text data according to the data format; means for analyzing multiple types of data formats and extracting risk items and important information using generative artificial intelligence technology; and means for recognizing the user's emotional state and adjusting the method of presenting the analysis results according to that emotion. This enables intuitive and effective document analysis and information provision that is tailored to the user's emotions.

[0377] "Documents" are a general term for papers and materials that organize and formalize information, and include specific content such as contracts and patent drawings.

[0378] "Generative artificial intelligence technology" refers to technologies that generate new information and content from data learned by computers, and includes natural language processing and image recognition.

[0379] "User emotional state" refers to changes in the user's psychological state as perceived from their facial expressions, voice, etc., and is information used to adjust the presentation method of the analysis results.

[0380] A "risk item" refers to a potential danger or a part of a contract or patent drawing that requires attention, and is an item that requires particular care.

[0381] "Analysis results" refer to information obtained after document analysis, and include evaluation results of extracted risk items and important information.

[0382] Users upload documents such as contracts and patent drawings to the system via a terminal. The terminal provides a user interface and facilitates document uploads. The terminal is also equipped with a camera and microphone to analyze the user's facial expressions and voice, collecting emotional data. This emotional data is used to identify the user's emotional state.

[0383] The server receives uploaded documents and converts them into text data according to their format. Next, it uses generative artificial intelligence technology to extract risk items and key information from contracts and patent drawings. Furthermore, it can adjust how the analysis results are presented based on the user's emotional state. This ensures that information is presented in a way that is easy for the user to understand, improving the user experience.

[0384] The hardware required includes a general-purpose terminal with camera functionality, and a high-performance information processing unit as the server. The software used will include OpenCV, PyTorch, and natural language processing libraries (SpaCy and Transformers). By combining these tools, emotion recognition and document analysis will be achieved.

[0385] For example, when a company's legal department reviews a new security agreement, if the user is overwhelmed by the complexity of the agreement, the system includes features to facilitate user understanding, such as providing detailed explanations of risk items and relevant supplementary information. To support this operation, the generating AI model can use the following prompt: "List the key risk points of this security agreement in bullet points and provide additional information to aid in decision-making."

[0386] This system provides users with an environment where they can make important decisions with confidence.

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] Users upload documents such as contracts and patent drawings to the system via a terminal. The input consists of document data selected by the user, and the output is a digital file transferred to the server. A file selection window opens through the user interface, allowing the user to select a document and press the submit button.

[0390] Step 2:

[0391] The device uses a camera and microphone to simultaneously record the user's facial expressions and voice, collecting emotional data. Input consists of camera video and audio data, while output is analytical data representing the user's emotional state. OpenCV is used to detect facial feature points in real time, and these are analyzed along with audio data using a PyTorch-trained model.

[0392] Step 3:

[0393] The server converts received documents into a text format suitable for analysis. The input is uploaded documents, and the output is data in a text-analyzable format. It uses OCR technology to extract text information from images, PDFs, and other formats, and then converts it to text format using a natural language processing library.

[0394] Step 4:

[0395] The server uses a generative AI model to extract risk items and key information from documents. The input is text data, and the output is a set of extracted risk items and key information. The generative AI model is prompted with the message, "List the key risk points of this security agreement in bullet points, and provide additional information to aid in decision-making," and then performs the action of researching relevant information.

[0396] Step 5:

[0397] The server adjusts how the analysis results are presented, taking into account the user's emotional state. The input consists of emotional data and analysis results, while the output is an optimized report presented to the user. If the user appears confused, the server highlights important information and provides additional relevant details.

[0398] Step 6:

[0399] Users can interact with the generated report and ask questions to supplement the explanation. The input is user-submitted question data, and the output is the answer provided by the server. A question-answering system using natural language processing analyzes the user's inquiry and adjusts the tone of the answer based on sentiment feedback.

[0400] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0401] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0402] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0406] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0407] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0408] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0409] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0410] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0411] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0412] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0413] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0414] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0415] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0416] This invention provides a system for efficiently analyzing contracts and patent drawings. The system's implementation involves three entities: a server, a terminal, and a user.

[0417] First, the user uploads the document to the analysis system. Since the uploaded document may contain a mix of text and image formats, the server first converts these documents into the appropriate data format. For example, OCR technology is used to extract text data from image data. This conversion process prepares the system for processing various data formats in a unified manner.

[0418] Next, the server uses generative AI technology to analyze the document's content. The AI ​​model extracts key information from risk items within the contract and patent drawings, identifying which parts clearly demonstrate business risks. These analysis results serve as foundational data for streamlining subsequent risk management.

[0419] Furthermore, the server generates a visualized report based on the information obtained through analysis, making it easy for users to quickly understand. This report includes all detected risk items and key information, as well as a risk assessment based on them. In addition, the system incorporates an interactive question-answering function for user convenience. When a user asks a question about a specific analysis, the server uses AI to search for the relevant information and provides an immediate answer. This function enables users to make decisions based on the latest information at all times.

[0420] As a concrete example, consider a scenario where a user uploads a supply chain contract and its associated patent drawings to the system. The server analyzes these documents, extracting risk-related information from the contract, such as clauses regarding delivery delays and measures to be taken in case of quality defects. Regarding the patent drawings, it can identify key technical features and point out differences and deficiencies compared to competitors' technologies. All of this information is integrated into a report to support the user's decision-making.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] Users upload contracts and patent drawings to the system via their terminals. The system can handle uploads even if the documents consist of multiple formats (e.g., PDF, image files, etc.).

[0424] Step 2:

[0425] The terminal receives the uploaded document and sends it to the server. At this time, it adds the document's metadata and source information, preparing it for processing on the server.

[0426] Step 3:

[0427] The server manages the received documents. First, it identifies the document format and extracts text data using OCR technology for images and PDF files. Then, it converts the acquired text data into a format that can be analyzed.

[0428] Step 4:

[0429] The server uses generative AI technology to analyze the content of documents. It extracts risk-related clauses from contracts and identifies technical features from patent drawings. These are efficiently detected using AI's natural language processing and image recognition technologies.

[0430] Step 5:

[0431] The server performs a risk assessment based on the analysis results. It evaluates the identified risk items and deficiencies in the drawings, and creates a report based on their importance and frequency. At that time, it visualizes the data in a format requested by the user, preparing to support the user's decision-making.

[0432] Step 6:

[0433] Users review the generated report on their device. Furthermore, they can use the interactive question-and-answer function to ask the server questions about any unclear points or items of interest regarding the analysis results or report.

[0434] Step 7:

[0435] The server analyzes user questions and generates answers based on existing data and analysis results. The answers are immediately returned to the user, allowing them to make more informed decisions based on the additional information they receive.

[0436] (Example 1)

[0437] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0438] In recent years, there has been a growing need to efficiently and accurately analyze the contents of electronic files such as contracts and patent drawings. However, these files are often stored in different formats, making consistent analysis difficult. Furthermore, extracting risk items and important information requires advanced expertise, and doing so manually is time-consuming and laborious. In addition, there is a demand for visualized information that can be quickly understood and used to aid in decision-making. To address these challenges, technology is needed that can handle various data formats and perform advanced analysis automatically.

[0439] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0440] In this invention, the server includes means for receiving electronic files and converting them into character data according to the information format; means for analyzing multiple types of information formats and identifying risk items and important information using generation AI technology; and means for performing a risk assessment from the identified results and creating a report with visualized results. This makes it possible to efficiently extract necessary information from electronic files of different formats and to quickly provide visualized analysis results.

[0441] An "electronic file" refers to documents and images that are digital data stored on a computer.

[0442] "Information format" refers to the specific format in which digital data is stored, such as text format or image format.

[0443] "Text data" refers to text information extracted from digital data in a format that humans can read.

[0444] "Generative AI technology" refers to technology that uses artificial intelligence to analyze digital data and generate specific information or patterns.

[0445] A "risk item" refers to an element related to a potential problem or threat identified within the digital data being analyzed.

[0446] "Important information" refers to particularly noteworthy data elements contained within the digital data being analyzed.

[0447] A "visualizable report" refers to a document created to aid understanding by visually representing analysis results in the form of graphs, tables, and other visual formats.

[0448] "Interactive question answering" refers to an interface that provides immediate and relevant information in response to inquiries made by users to the system.

[0449] A "user interface" refers to a mechanism that provides screens and input methods for users to interact with a system.

[0450] A "computer" refers to an electronic device used for data processing, and can particularly function as a server.

[0451] This invention provides a system for efficiently extracting risk items and important information by analyzing electronic files. The system's implementation primarily involves three entities: a server, a terminal, and a user. Specifically, the invention is implemented as follows.

[0452] Users upload electronic files such as contracts and patent drawings to the system via their terminal. They select and send files through the user interface. Users can also ask questions about the analysis results along the way, utilizing an interactive question-and-answer function.

[0453] The server first converts electronic files received from users into text data according to their format. For image files, OCR technology is used to extract text information. After converting to a unified text data format, the server performs analysis using a generative AI model. This AI model combines natural language processing and image recognition technologies to identify risk items and technically important information within the document. The analysis results are visualized in a way that is easily understandable to the user and generated as a report. Furthermore, the server includes a question-answering function that can immediately provide relevant information in response to user inquiries.

[0454] As a concrete example, consider a case where a user uploads a manufacturing contract and its associated drawings. In this case, the server extracts information related to "supply obligations" and "quality assurance" from the manufacturing contract. Meanwhile, it can analyze the drawings to identify the product's unique technical characteristics. All of these analysis results are integrated into a report and provided to the user.

[0455] An example of a prompt might be, "Please extract the risk items that require particular attention within the contract." The system works by using this prompt to allow a generating AI model to analyze the data and provide the user with the necessary information.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] Users upload the electronic files to be analyzed to the system via a terminal. Using a dedicated interface on the terminal, they select the necessary files and press the send button, which transfers the files to the server. The input consists of electronic files in multiple data formats selected by the user. The output is the storage of these files in the system.

[0459] Step 2:

[0460] The server converts received electronic files into text data according to their format. For image files, OCR technology is used to extract text from the image. Inputs include images and PDF files, and the output is a text file in a standardized character data format. Data processing involves evaluating the file format and performing appropriate text extraction.

[0461] Step 3:

[0462] The server analyzes text data using a generative AI model. By applying the generative AI model and utilizing natural language processing, it identifies risk items and important information from documents. A text file in a unified character data format is used as input, and the output is a list of identified risk items and important information. Here, language analysis is performed as a data calculation.

[0463] Step 4:

[0464] The server performs a risk assessment based on the analysis results and creates a visualized report. It utilizes data visualization tools, including the creation of pie charts and bar graphs, to clearly present the analysis results. The input is a list of analysis results obtained in step 3, and the output is a graphical report based on this list. Data processing involves structuring and visualizing the data based on the analysis results.

[0465] Step 5:

[0466] Users review the generated report and utilize an interactive question-answering system as needed. Through this system, they can obtain additional information about the analysis results and inquire about specific items in more detail. Input consists of natural language questions from the user, and output is an answer containing relevant information to the question. Data processing involves analyzing the question and retrieving related information.

[0467] (Application Example 1)

[0468] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0469] In modern business operations, quickly and accurately identifying risks hidden within legal and technical documents is crucial for maintaining business safety and efficiency. However, these documents often come in multiple formats and contain a wealth of information, making manual analysis time-consuming and labor-intensive. Furthermore, identifying and evaluating risk areas relies on human judgment, leading to risks of oversight and misinterpretation. To address these challenges, there is a need for technology that automatically analyzes documents, identifies and evaluates risks, and rapidly provides visualized information.

[0470] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0471] In this invention, the server includes means for receiving information and converting it into character data according to the information format; means for analyzing multiple types of information formats and extracting risk items and important information using machine learning technology; means for performing a risk assessment from the extraction results and generating the results as a report that can be visualized; means for providing relevant information in response to inquiries from users through interactive question and answer; and means including optical character recognition technology for acquiring documents as images using a camera and extracting characters from the images. This makes it possible to efficiently identify and assess risks contained in legal and technical documents and to support users in making quick decisions.

[0472] "Information" is a general term for various types of content, such as documents, images, and audio, that are recognized as data.

[0473] "Character data" refers to digital information that has been converted into a format that can be processed as text.

[0474] "Machine learning technology" is a technique for automatically learning features from data and identifying specific patterns or rules.

[0475] A "risk item" is information that indicates potential business or legal problems or vulnerabilities.

[0476] "Important information" refers to high-value data that should be given particular priority in decision-making.

[0477] A "visualizable report" is a document that presents data in an intuitively understandable format using charts, graphs, and other visual aids.

[0478] "Interactive question answering" is a function that answers user inquiries in a real-time, conversational format.

[0479] A "photography device" is a device used to capture objects or documents as images.

[0480] "Optical character recognition technology" is a technology that analyzes characters in an image and converts them into text data.

[0481] This invention provides a system for efficiently processing information. The server receives information in various formats uploaded by users and converts it into text data. This conversion process utilizes optical character recognition (OCR), such as Tesseract. This allows documents uploaded as images to also be converted into a parseable text format.

[0482] The converted text data is analyzed on the server using machine learning techniques, specifically natural language processing (NLP) and image analysis. This process utilizes generative AI models to extract risk items and important information. Possible AI models used include OpenAI's GPT model. Based on these results, the system generates a visualized report and provides it to the user.

[0483] Furthermore, users can access detailed information about each risk item and key information listed in the report through an interactive question-and-answer function. This function operates in real time to support user decision-making.

[0484] As a concrete example, consider a case where a user takes a picture of a commercial contract with their smartphone camera and uploads it to this system. The system automatically extracts risk factors such as "confidentiality clauses" and "terms and conditions" from the contract and provides the user with this information quickly and clearly visualized. In this way, users can make appropriate business decisions based on a rapid and accurate risk assessment.

[0485] An example of a prompt message supplied to a generating AI model is: "Identify business risks from the following text. Pay particular attention to clauses regarding delivery delays, poor quality, and confidentiality."

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The user selects and uploads the document they want to analyze via the terminal's interface. The input provided is either a digital image or scanned data of the document. To ensure secure transmission of the received data to the server, encryption technology is used during data transfer.

[0489] Step 2:

[0490] The server converts the received document into a format that can be analyzed. The input is image data received from the terminal, and the output is text data. The server uses optical character recognition (OCR) technology such as Tesseract to convert the text information in the image into text format. During this process, OCR processing is used to distinguish between text and non-text areas in the image and extract the text.

[0491] Step 3:

[0492] The server analyzes text data to extract important information. The input is the text data obtained in step 2, and the output is the analysis results, including risk items and important information. The server uses a generative AI model to analyze the text and identify risks and key technical features within the contract. The analysis includes a process that leverages natural language processing to understand the context of the document and the meaning of words.

[0493] Step 4:

[0494] The server converts the analysis results into a visualized report. The input is the analysis results from step 3, and the output is the visualized report. In this step, extracted risk items and key information are displayed in a user-friendly format using graphs and charts. A data processing program is used for visualization, and the information is appropriately formatted.

[0495] Step 5:

[0496] Users can review reports and ask additional questions. The server provides interactive responses to these questions. The input is the user's question, and the output is the server's answer. The server uses a generative AI model to understand the intent of the question and quickly provide relevant information. The question-answering process utilizes natural language processing techniques to analyze the context of the question and select appropriate information.

[0497] Throughout this entire process, users can efficiently and accurately identify risk items in documents and obtain information to support decision-making based on those items.

[0498] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0499] This invention is a system that combines document analysis with emotion recognition technology, enabling more effective analysis of contracts and patent drawings. The system consists of a server, a terminal, and a user, and by using an emotion engine, it incorporates the user's emotional state into the analysis process, providing a more intuitive and user-centered interface.

[0500] First, users upload contracts and patent drawings to the system via a terminal. The terminal is equipped with a user interface to facilitate document uploads. This interface is designed to be intuitive to enhance user convenience.

[0501] The device uses its camera and microphone to analyze the user's facial expressions and voice along with the uploaded documents, collecting user emotion data. This allows monitoring of the user's emotions while they are analyzing the documents. The emotion engine analyzes this data in real time to identify the user's emotional state.

[0502] Next, the server receives the document and analyzes its contents using OCR and generative AI technologies. It extracts key technical features from risk items within the contract and patent drawings, and performs a risk assessment based on these. The analysis results are generated as a report and presented to the user.

[0503] The emotion engine recognizes the user's emotional state, and the server adjusts how the analysis results are presented according to the user's emotions. For example, if the user is confused, the analysis results can be presented in more detail and in an easy-to-understand manner. Furthermore, based on the user's emotions, risk items and important information deemed to require particular attention are prioritized for display.

[0504] Furthermore, in addition to presenting reports, users can use their devices to engage in interactive question-and-answer sessions about the analysis results. The server identifies relevant information in response to the user's questions and adjusts the tone and content of the responses based on feedback from the sentiment engine. This provides users with more appropriate answers and improves the overall user experience.

[0505] For example, if a user uploads an intellectual property agreement and related technical drawings, and the emotion engine detects a cautious attitude from the user's facial expressions, the server will present a detailed assessment of risk items and provide supplementary information anticipating any questions the user might have. This information helps the user make important decisions with confidence.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] Users upload documents, including contracts and patent drawings, to the system interface using their terminals. The interface includes file selection and drag-and-drop functions, making it easy to send documents.

[0509] Step 2:

[0510] As the device uploads a document, it simultaneously collects emotional data from the user's facial expressions and voice using its built-in camera and microphone. The collected data is analyzed by an emotion engine, which identifies the user's emotional state in real time.

[0511] Step 3:

[0512] The server receives the uploaded documents. It automatically determines the document format and converts image data into text data using OCR technology. This process prepares all data for analysis in a unified format.

[0513] Step 4:

[0514] The server uses generative AI technology to analyze contracts and patent drawings. It combines natural language processing and image recognition technologies to extract risk items and key technical information. The analysis results are stored in an internal database and used in subsequent processes.

[0515] Step 5:

[0516] Based on feedback from the emotion engine, the server adjusts how it presents analysis results according to the user's emotional state. If the emotion is determined to be anxiety, it prepares to present a risk report in a detailed and easy-to-understand format.

[0517] Step 6:

[0518] The server generates a visualized report using the analysis results. The report includes prominent risk items and key information. Prioritized information based on sentiment recognition data is also reflected.

[0519] Step 7:

[0520] Users can view the generated reports using their devices. If they have questions about the analysis results, they can use the interactive question-and-answer function to request additional information or detailed explanations from the server.

[0521] Step 8:

[0522] The server receives the user's question and retrieves the necessary information from the relevant database. It constructs a response in a tone that reflects the user's emotions and presents the answer to the user via the terminal. This allows the user to gain a clearer understanding.

[0523] (Example 2)

[0524] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0525] Conventional document analysis systems provide analysis results without considering the user's emotions, resulting in a problem where they cannot present appropriate information according to the user's level of understanding or emotional state. Furthermore, the supplementation of analyzed information and risk assessment may be insufficient, making it difficult for users to obtain the information necessary for decision-making.

[0526] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0527] In this invention, the server includes means for receiving documents and converting them into information according to the data format; means for analyzing multiple types of data formats and extracting risk information and important elements using generative AI technology; and means for identifying the user's emotional state using emotion recognition technology and adjusting the presentation method of the analysis results based on the user's emotions. This makes it possible to provide appropriate information according to the user's emotional state, enabling the user to efficiently acquire the information necessary for decision-making.

[0528] A "document" is a written document containing information in text or drawing format, including contracts and technical drawings.

[0529] "Data format" refers to the form or structure in which information is represented or stored, and includes text, images, videos, and so on.

[0530] An "information processing device" is a device that analyzes received data, extracts specific information, and outputs the results.

[0531] "Generative AI technology" is a technology that uses artificial intelligence to generate new information and insights from text data and image data.

[0532] "Risk information" refers to information that contains potential risks related to contracts and drawings, and this is a point of caution for users.

[0533] "Key elements" refer to particularly noteworthy information or technical details included within a document.

[0534] "Emotion recognition technology" is a technology that detects and identifies a user's emotional state from their facial expressions and voice.

[0535] "Information presentation" refers to the act of providing the user with the analyzed results visually or audibly.

[0536] "Interactive question answering" refers to a two-way communication system where the user asks a question to the system and the system provides an appropriate answer.

[0537] This system is a document analysis system that combines emotion recognition technology to more effectively analyze contracts and patent drawings. The system consists of three components: a server, terminals, and users.

[0538] Users upload contracts and patent drawings via their devices. These devices feature an intuitive user interface with file selection buttons and drag-and-drop functionality, making document uploading easy for users.

[0539] The device collects the user's facial expressions and voice using its built-in camera and microphone, along with uploaded documents. This allows the device to acquire data in real time for analyzing the user's emotional state. By applying emotion recognition technology, it identifies what emotions the user is experiencing.

[0540] The server analyzes received documents using OCR technology and a generative AI model. The OCR technology converts the document into text data, and the generative AI model analyzes that text data to identify risk information and key elements. Based on these analysis results, the server performs a risk assessment and generates a report with visualized results.

[0541] Once the emotion engine identifies the user's emotional state, the server adjusts how the analysis results are presented based on the user's emotions. For example, if the user is confused, the server can provide more detailed and easily understandable information by adding explanations to the analysis results.

[0542] Users can use their devices to ask questions about the analysis results. Interactive question-and-answer sessions allow users to obtain detailed information on areas of interest. The server identifies relevant information in response to the user's questions and adjusts its responses based on sentiment feedback to provide accurate answers.

[0543] For example, if a user uploads an intellectual property contract and their facial expression indicates a cautious attitude, the server will present a detailed assessment of the risk items and provide supplementary information that anticipates the user's questions. As a result, the user can make decisions with confidence.

[0544] An example of a prompt message is, "Please provide a detailed explanation of the specific risk items in the contract and offer examples to alleviate user confusion."

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] Users upload contracts and patent drawings using a terminal. The terminal's user interface is intuitive, allowing users to select files using buttons and drag-and-drop functionality. Input is the document file selected by the user, and output is its transmission to the server.

[0548] Step 2:

[0549] The device records the user's facial expressions and voice using its built-in camera and microphone, simultaneously with the uploaded documents. This data is collected to determine the user's emotional state. The input is the user's real-time facial expressions and voice, and the output is the aggregation of this data and its transmission to the server.

[0550] Step 3:

[0551] The server converts received documents into text data using OCR technology. OCR processing recognizes characters from paper documents and image files into text format. The input is image data of the document, and the output is the converted text data.

[0552] Step 4:

[0553] The server analyzes this text data using a generative AI model. The analysis aims to identify risk information and key technological elements. The input is the text data obtained in step 3, and the output is the extracted risk information and key elements.

[0554] Step 5:

[0555] The server uses emotion recognition technology to analyze emotion data transmitted from the terminal. This allows for a detailed identification of the user's emotional state. The input is data of the user's facial expressions and voice, and the output is the identified emotional state.

[0556] Step 6:

[0557] The server customizes and presents the analysis results according to the user's emotional state. For example, if the user is confused, the analysis will be made more detailed and additional explanations will be added. The input is the output data from steps 4 and 5, and the output is the adjusted analysis results report.

[0558] Step 7:

[0559] Users can ask further questions about the presented analysis results. They request additional information using the terminal's question-answering interface. The input is the user's question, and the output is the answer provided by the server.

[0560] Step 8:

[0561] The server searches for relevant information based on the user's question and adjusts the tone of its response based on the user's emotional state. The input is data on the user's question and emotional state, and the output is the adjusted response.

[0562] (Application Example 2)

[0563] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0564] Modern document analysis requires not only the identification of risk items and important information in contracts and patent drawings, but also analysis that takes into account the user's emotional state. However, conventional technologies have made it difficult to consider both the content of the document and the user's emotions simultaneously, limiting the means to realize a user-centered interface.

[0565] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0566] In this invention, the server includes means for receiving documents and converting them into text data according to the data format; means for analyzing multiple types of data formats and extracting risk items and important information using generative artificial intelligence technology; and means for recognizing the user's emotional state and adjusting the method of presenting the analysis results according to that emotion. This enables intuitive and effective document analysis and information provision that is tailored to the user's emotions.

[0567] "Documents" are a general term for papers and materials that organize and formalize information, and include specific content such as contracts and patent drawings.

[0568] "Generative artificial intelligence technology" refers to technologies that generate new information and content from data learned by computers, and includes natural language processing and image recognition.

[0569] "User emotional state" refers to changes in the user's psychological state as perceived from their facial expressions, voice, etc., and is information used to adjust the presentation method of the analysis results.

[0570] A "risk item" refers to a potential danger or a part of a contract or patent drawing that requires attention, and is an item that requires particular care.

[0571] "Analysis results" refer to information obtained after document analysis, and include evaluation results of extracted risk items and important information.

[0572] Users upload documents such as contracts and patent drawings to the system via a terminal. The terminal provides a user interface and facilitates document uploads. The terminal is also equipped with a camera and microphone to analyze the user's facial expressions and voice, collecting emotional data. This emotional data is used to identify the user's emotional state.

[0573] The server receives uploaded documents and converts them into text data according to their format. Next, it uses generative artificial intelligence technology to extract risk items and key information from contracts and patent drawings. Furthermore, it can adjust how the analysis results are presented based on the user's emotional state. This ensures that information is presented in a way that is easy for the user to understand, improving the user experience.

[0574] The hardware required includes a general-purpose terminal with camera functionality, and a high-performance information processing unit as the server. The software used will include OpenCV, PyTorch, and natural language processing libraries (SpaCy and Transformers). By combining these tools, emotion recognition and document analysis will be achieved.

[0575] For example, when a company's legal department reviews a new security agreement, if the user is overwhelmed by the complexity of the agreement, the system includes features to facilitate user understanding, such as providing detailed explanations of risk items and relevant supplementary information. To support this operation, the generating AI model can use the following prompt: "List the key risk points of this security agreement in bullet points and provide additional information to aid in decision-making."

[0576] This system provides users with an environment where they can make important decisions with confidence.

[0577] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0578] Step 1:

[0579] Users upload documents such as contracts and patent drawings to the system via a terminal. The input consists of document data selected by the user, and the output is a digital file transferred to the server. A file selection window opens through the user interface, allowing the user to select a document and press the submit button.

[0580] Step 2:

[0581] The device uses a camera and microphone to simultaneously record the user's facial expressions and voice, collecting emotional data. Input consists of camera video and audio data, while output is analytical data representing the user's emotional state. OpenCV is used to detect facial feature points in real time, and these are analyzed along with audio data using a PyTorch-trained model.

[0582] Step 3:

[0583] The server converts received documents into a text format suitable for analysis. The input is uploaded documents, and the output is data in a text-analyzable format. It uses OCR technology to extract text information from images, PDFs, and other formats, and then converts it to text format using a natural language processing library.

[0584] Step 4:

[0585] The server uses a generative AI model to extract risk items and key information from documents. The input is text data, and the output is a set of extracted risk items and key information. The generative AI model is prompted with the message, "List the key risk points of this security agreement in bullet points, and provide additional information to aid in decision-making," and then performs the action of researching relevant information.

[0586] Step 5:

[0587] The server adjusts how the analysis results are presented, taking into account the user's emotional state. The input consists of emotional data and analysis results, while the output is an optimized report presented to the user. If the user appears confused, the server highlights important information and provides additional relevant details.

[0588] Step 6:

[0589] Users can interact with the generated report and ask questions to supplement the explanation. The input is user-submitted question data, and the output is the answer provided by the server. A question-answering system using natural language processing analyzes the user's inquiry and adjusts the tone of the answer based on sentiment feedback.

[0590] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0591] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0592] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0593] [Fourth Embodiment]

[0594] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0595] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0596] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0597] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0598] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0599] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0600] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0601] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0602] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0603] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0604] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0605] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0606] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0607] This invention provides a system for efficiently analyzing contracts and patent drawings. The system's implementation involves three entities: a server, a terminal, and a user.

[0608] First, the user uploads the document to the analysis system. Since the uploaded document may contain a mix of text and image formats, the server first converts these documents into the appropriate data format. For example, OCR technology is used to extract text data from image data. This conversion process prepares the system for processing various data formats in a unified manner.

[0609] Next, the server uses generative AI technology to analyze the document's content. The AI ​​model extracts key information from risk items within the contract and patent drawings, identifying which parts clearly demonstrate business risks. These analysis results serve as foundational data for streamlining subsequent risk management.

[0610] Furthermore, the server generates a visualized report based on the information obtained through analysis, making it easy for users to quickly understand. This report includes all detected risk items and key information, as well as a risk assessment based on them. In addition, the system incorporates an interactive question-answering function for user convenience. When a user asks a question about a specific analysis, the server uses AI to search for the relevant information and provides an immediate answer. This function enables users to make decisions based on the latest information at all times.

[0611] As a concrete example, consider a scenario where a user uploads a supply chain contract and its associated patent drawings to the system. The server analyzes these documents, extracting risk-related information from the contract, such as clauses regarding delivery delays and measures to be taken in case of quality defects. Regarding the patent drawings, it can identify key technical features and point out differences and deficiencies compared to competitors' technologies. All of this information is integrated into a report to support the user's decision-making.

[0612] The following describes the processing flow.

[0613] Step 1:

[0614] Users upload contracts and patent drawings to the system via their terminals. The system can handle uploads even if the documents consist of multiple formats (e.g., PDF, image files, etc.).

[0615] Step 2:

[0616] The terminal receives the uploaded document and sends it to the server. At this time, it adds the document's metadata and source information, preparing it for processing on the server.

[0617] Step 3:

[0618] The server manages the received documents. First, it identifies the document format and extracts text data using OCR technology for images and PDF files. Then, it converts the acquired text data into a format that can be analyzed.

[0619] Step 4:

[0620] The server uses generative AI technology to analyze the content of documents. It extracts risk-related clauses from contracts and identifies technical features from patent drawings. These are efficiently detected using AI's natural language processing and image recognition technologies.

[0621] Step 5:

[0622] The server performs a risk assessment based on the analysis results. It evaluates the identified risk items and deficiencies in the drawings, and creates a report based on their importance and frequency. At that time, it visualizes the data in a format requested by the user, preparing to support the user's decision-making.

[0623] Step 6:

[0624] Users review the generated report on their device. Furthermore, they can use the interactive question-and-answer function to ask the server questions about any unclear points or items of interest regarding the analysis results or report.

[0625] Step 7:

[0626] The server analyzes user questions and generates answers based on existing data and analysis results. The answers are immediately returned to the user, allowing them to make more informed decisions based on the additional information they receive.

[0627] (Example 1)

[0628] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0629] In recent years, there has been a growing need to efficiently and accurately analyze the contents of electronic files such as contracts and patent drawings. However, these files are often stored in different formats, making consistent analysis difficult. Furthermore, extracting risk items and important information requires advanced expertise, and doing so manually is time-consuming and laborious. In addition, there is a demand for visualized information that can be quickly understood and used to aid in decision-making. To address these challenges, technology is needed that can handle various data formats and perform advanced analysis automatically.

[0630] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0631] In this invention, the server includes means for receiving electronic files and converting them into character data according to the information format; means for analyzing multiple types of information formats and identifying risk items and important information using generation AI technology; and means for performing a risk assessment from the identified results and creating a report with visualized results. This makes it possible to efficiently extract necessary information from electronic files of different formats and to quickly provide visualized analysis results.

[0632] An "electronic file" refers to documents and images that are digital data stored on a computer.

[0633] "Information format" refers to the specific format in which digital data is stored, such as text format or image format.

[0634] "Text data" refers to text information extracted from digital data in a format that humans can read.

[0635] "Generative AI technology" refers to technology that uses artificial intelligence to analyze digital data and generate specific information or patterns.

[0636] A "risk item" refers to an element related to a potential problem or threat identified within the digital data being analyzed.

[0637] "Important information" refers to particularly noteworthy data elements contained within the digital data being analyzed.

[0638] A "visualizable report" refers to a document created to aid understanding by visually representing analysis results in the form of graphs, tables, and other visual formats.

[0639] "Interactive question answering" refers to an interface that provides immediate and relevant information in response to inquiries made by users to the system.

[0640] A "user interface" refers to a mechanism that provides screens and input methods for users to interact with a system.

[0641] A "computer" refers to an electronic device used for data processing, and can particularly function as a server.

[0642] This invention provides a system for efficiently extracting risk items and important information by analyzing electronic files. The system's implementation primarily involves three entities: a server, a terminal, and a user. Specifically, the invention is implemented as follows.

[0643] Users upload electronic files such as contracts and patent drawings to the system via their terminal. They select and send files through the user interface. Users can also ask questions about the analysis results along the way, utilizing an interactive question-and-answer function.

[0644] The server first converts electronic files received from users into text data according to their format. For image files, OCR technology is used to extract text information. After converting to a unified text data format, the server performs analysis using a generative AI model. This AI model combines natural language processing and image recognition technologies to identify risk items and technically important information within the document. The analysis results are visualized in a way that is easily understandable to the user and generated as a report. Furthermore, the server includes a question-answering function that can immediately provide relevant information in response to user inquiries.

[0645] As a concrete example, consider a case where a user uploads a manufacturing contract and its associated drawings. In this case, the server extracts information related to "supply obligations" and "quality assurance" from the manufacturing contract. Meanwhile, it can analyze the drawings to identify the product's unique technical characteristics. All of these analysis results are integrated into a report and provided to the user.

[0646] An example of a prompt might be, "Please extract the risk items that require particular attention within the contract." The system works by using this prompt to allow a generating AI model to analyze the data and provide the user with the necessary information.

[0647] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0648] Step 1:

[0649] Users upload the electronic files to be analyzed to the system via a terminal. Using a dedicated interface on the terminal, they select the necessary files and press the send button, which transfers the files to the server. The input consists of electronic files in multiple data formats selected by the user. The output is the storage of these files in the system.

[0650] Step 2:

[0651] The server converts received electronic files into text data according to their format. For image files, OCR technology is used to extract text from the image. Inputs include images and PDF files, and the output is a text file in a standardized character data format. Data processing involves evaluating the file format and performing appropriate text extraction.

[0652] Step 3:

[0653] The server analyzes text data using a generative AI model. By applying the generative AI model and utilizing natural language processing, it identifies risk items and important information from documents. A text file in a unified character data format is used as input, and the output is a list of identified risk items and important information. Here, language analysis is performed as a data calculation.

[0654] Step 4:

[0655] The server performs a risk assessment based on the analysis results and creates a visualized report. It utilizes data visualization tools, including the creation of pie charts and bar graphs, to clearly present the analysis results. The input is a list of analysis results obtained in step 3, and the output is a graphical report based on this list. Data processing involves structuring and visualizing the data based on the analysis results.

[0656] Step 5:

[0657] Users review the generated report and utilize an interactive question-answering system as needed. Through this system, they can obtain additional information about the analysis results and inquire about specific items in more detail. Input consists of natural language questions from the user, and output is an answer containing relevant information to the question. Data processing involves analyzing the question and retrieving related information.

[0658] (Application Example 1)

[0659] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] In modern business operations, quickly and accurately identifying risks hidden within legal and technical documents is crucial for maintaining business safety and efficiency. However, these documents often come in multiple formats and contain a wealth of information, making manual analysis time-consuming and labor-intensive. Furthermore, identifying and evaluating risk areas relies on human judgment, leading to risks of oversight and misinterpretation. To address these challenges, there is a need for technology that automatically analyzes documents, identifies and evaluates risks, and rapidly provides visualized information.

[0661] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0662] In this invention, the server includes means for receiving information and converting it into character data according to the information format; means for analyzing multiple types of information formats and extracting risk items and important information using machine learning technology; means for performing a risk assessment from the extraction results and generating the results as a report that can be visualized; means for providing relevant information in response to inquiries from users through interactive question and answer; and means including optical character recognition technology for acquiring documents as images using a camera and extracting characters from the images. This makes it possible to efficiently identify and assess risks contained in legal and technical documents and to support users in making quick decisions.

[0663] "Information" is a general term for various types of content, such as documents, images, and audio, that are recognized as data.

[0664] "Character data" refers to digital information that has been converted into a format that can be processed as text.

[0665] "Machine learning technology" is a technique for automatically learning features from data and identifying specific patterns or rules.

[0666] A "risk item" is information that indicates potential business or legal problems or vulnerabilities.

[0667] "Important information" refers to high-value data that should be given particular priority in decision-making.

[0668] A "visualizable report" is a document that presents data in an intuitively understandable format using charts, graphs, and other visual aids.

[0669] "Interactive question answering" is a function that answers user inquiries in a real-time, conversational format.

[0670] A "photography device" is a device used to capture objects or documents as images.

[0671] "Optical character recognition technology" is a technology that analyzes characters in an image and converts them into text data.

[0672] This invention provides a system for efficiently processing information. The server receives information in various formats uploaded by users and converts it into text data. This conversion process utilizes optical character recognition (OCR), such as Tesseract. This allows documents uploaded as images to also be converted into a parseable text format.

[0673] The converted text data is analyzed on the server using machine learning techniques, specifically natural language processing (NLP) and image analysis. This process utilizes generative AI models to extract risk items and important information. Possible AI models used include OpenAI's GPT model. Based on these results, the system generates a visualized report and provides it to the user.

[0674] Furthermore, users can access detailed information about each risk item and key information listed in the report through an interactive question-and-answer function. This function operates in real time to support user decision-making.

[0675] As a concrete example, consider a case where a user takes a picture of a commercial contract with their smartphone camera and uploads it to this system. The system automatically extracts risk factors such as "confidentiality clauses" and "terms and conditions" from the contract and provides the user with this information quickly and clearly visualized. In this way, users can make appropriate business decisions based on a rapid and accurate risk assessment.

[0676] An example of a prompt message supplied to a generating AI model is: "Identify business risks from the following text. Pay particular attention to clauses regarding delivery delays, poor quality, and confidentiality."

[0677] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0678] Step 1:

[0679] The user selects and uploads the document they want to analyze via the terminal's interface. The input provided is either a digital image or scanned data of the document. To ensure secure transmission of the received data to the server, encryption technology is used during data transfer.

[0680] Step 2:

[0681] The server converts the received document into a format that can be analyzed. The input is image data received from the terminal, and the output is text data. The server uses optical character recognition (OCR) technology such as Tesseract to convert the text information in the image into text format. During this process, OCR processing is used to distinguish between text and non-text areas in the image and extract the text.

[0682] Step 3:

[0683] The server analyzes text data to extract important information. The input is the text data obtained in step 2, and the output is the analysis results, including risk items and important information. The server uses a generative AI model to analyze the text and identify risks and key technical features within the contract. The analysis includes a process that leverages natural language processing to understand the context of the document and the meaning of words.

[0684] Step 4:

[0685] The server converts the analysis results into a visualized report. The input is the analysis results from step 3, and the output is the visualized report. In this step, extracted risk items and key information are displayed in a user-friendly format using graphs and charts. A data processing program is used for visualization, and the information is appropriately formatted.

[0686] Step 5:

[0687] Users can review reports and ask additional questions. The server provides interactive responses to these questions. The input is the user's question, and the output is the server's answer. The server uses a generative AI model to understand the intent of the question and quickly provide relevant information. The question-answering process utilizes natural language processing techniques to analyze the context of the question and select appropriate information.

[0688] Throughout this entire process, users can efficiently and accurately identify risk items in documents and obtain information to support decision-making based on those items.

[0689] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0690] This invention is a system that combines document analysis with emotion recognition technology, enabling more effective analysis of contracts and patent drawings. The system consists of a server, a terminal, and a user, and by using an emotion engine, it incorporates the user's emotional state into the analysis process, providing a more intuitive and user-centered interface.

[0691] First, users upload contracts and patent drawings to the system via a terminal. The terminal is equipped with a user interface to facilitate document uploads. This interface is designed to be intuitive to enhance user convenience.

[0692] The device uses its camera and microphone to analyze the user's facial expressions and voice along with the uploaded documents, collecting user emotion data. This allows monitoring of the user's emotions while they are analyzing the documents. The emotion engine analyzes this data in real time to identify the user's emotional state.

[0693] Next, the server receives the document and analyzes its contents using OCR and generative AI technologies. It extracts key technical features from risk items within the contract and patent drawings, and performs a risk assessment based on these. The analysis results are generated as a report and presented to the user.

[0694] The emotion engine recognizes the user's emotional state, and the server adjusts how the analysis results are presented according to the user's emotions. For example, if the user is confused, the analysis results can be presented in more detail and in an easy-to-understand manner. Furthermore, based on the user's emotions, risk items and important information deemed to require particular attention are prioritized for display.

[0695] Furthermore, in addition to presenting reports, users can use their devices to engage in interactive question-and-answer sessions about the analysis results. The server identifies relevant information in response to the user's questions and adjusts the tone and content of the responses based on feedback from the sentiment engine. This provides users with more appropriate answers and improves the overall user experience.

[0696] For example, if a user uploads an intellectual property agreement and related technical drawings, and the emotion engine detects a cautious attitude from the user's facial expressions, the server will present a detailed assessment of risk items and provide supplementary information anticipating any questions the user might have. This information helps the user make important decisions with confidence.

[0697] The following describes the processing flow.

[0698] Step 1:

[0699] Users upload documents, including contracts and patent drawings, to the system interface using their terminals. The interface includes file selection and drag-and-drop functions, making it easy to send documents.

[0700] Step 2:

[0701] As the device uploads a document, it simultaneously collects emotional data from the user's facial expressions and voice using its built-in camera and microphone. The collected data is analyzed by an emotion engine, which identifies the user's emotional state in real time.

[0702] Step 3:

[0703] The server receives the uploaded documents. It automatically determines the document format and converts image data into text data using OCR technology. This process prepares all data for analysis in a unified format.

[0704] Step 4:

[0705] The server uses generative AI technology to analyze contracts and patent drawings. It combines natural language processing and image recognition technologies to extract risk items and key technical information. The analysis results are stored in an internal database and used in subsequent processes.

[0706] Step 5:

[0707] Based on feedback from the emotion engine, the server adjusts how it presents analysis results according to the user's emotional state. If the emotion is determined to be anxiety, it prepares to present a risk report in a detailed and easy-to-understand format.

[0708] Step 6:

[0709] The server generates a visualized report using the analysis results. The report includes prominent risk items and key information. Prioritized information based on sentiment recognition data is also reflected.

[0710] Step 7:

[0711] Users can view the generated reports using their devices. If they have questions about the analysis results, they can use the interactive question-and-answer function to request additional information or detailed explanations from the server.

[0712] Step 8:

[0713] The server receives the user's question and retrieves the necessary information from the relevant database. It constructs a response in a tone that reflects the user's emotions and presents the answer to the user via the terminal. This allows the user to gain a clearer understanding.

[0714] (Example 2)

[0715] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0716] Conventional document analysis systems provide analysis results without considering the user's emotions, resulting in a problem where they cannot present appropriate information according to the user's level of understanding or emotional state. Furthermore, the supplementation of analyzed information and risk assessment may be insufficient, making it difficult for users to obtain the information necessary for decision-making.

[0717] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0718] In this invention, the server includes means for receiving documents and converting them into information according to the data format; means for analyzing multiple types of data formats and extracting risk information and important elements using generative AI technology; and means for identifying the user's emotional state using emotion recognition technology and adjusting the presentation method of the analysis results based on the user's emotions. This makes it possible to provide appropriate information according to the user's emotional state, enabling the user to efficiently acquire the information necessary for decision-making.

[0719] A "document" is a written document containing information in text or drawing format, including contracts and technical drawings.

[0720] "Data format" refers to the form or structure in which information is represented or stored, and includes text, images, videos, and so on.

[0721] An "information processing device" is a device that analyzes received data, extracts specific information, and outputs the results.

[0722] "Generative AI technology" is a technology that uses artificial intelligence to generate new information and insights from text data and image data.

[0723] "Risk information" refers to information that contains potential risks related to contracts and drawings, and this is a point of caution for users.

[0724] "Key elements" refer to particularly noteworthy information or technical details included within a document.

[0725] "Emotion recognition technology" is a technology that detects and identifies a user's emotional state from their facial expressions and voice.

[0726] "Information presentation" refers to the act of providing the user with the analyzed results visually or audibly.

[0727] "Interactive question answering" refers to a two-way communication system where the user asks a question to the system and the system provides an appropriate answer.

[0728] This system is a document analysis system that combines emotion recognition technology to more effectively analyze contracts and patent drawings. The system consists of three components: a server, terminals, and users.

[0729] Users upload contracts and patent drawings via their devices. These devices feature an intuitive user interface with file selection buttons and drag-and-drop functionality, making document uploading easy for users.

[0730] The device collects the user's facial expressions and voice using its built-in camera and microphone, along with uploaded documents. This allows the device to acquire data in real time for analyzing the user's emotional state. By applying emotion recognition technology, it identifies what emotions the user is experiencing.

[0731] The server analyzes received documents using OCR technology and a generative AI model. The OCR technology converts the document into text data, and the generative AI model analyzes that text data to identify risk information and key elements. Based on these analysis results, the server performs a risk assessment and generates a report with visualized results.

[0732] Once the emotion engine identifies the user's emotional state, the server adjusts how the analysis results are presented based on the user's emotions. For example, if the user is confused, the server can provide more detailed and easily understandable information by adding explanations to the analysis results.

[0733] Users can use their devices to ask questions about the analysis results. Interactive question-and-answer sessions allow users to obtain detailed information on areas of interest. The server identifies relevant information in response to the user's questions and adjusts its responses based on sentiment feedback to provide accurate answers.

[0734] For example, if a user uploads an intellectual property contract and their facial expression indicates a cautious attitude, the server will present a detailed assessment of the risk items and provide supplementary information that anticipates the user's questions. As a result, the user can make decisions with confidence.

[0735] An example of a prompt message is, "Please provide a detailed explanation of the specific risk items in the contract and offer examples to alleviate user confusion."

[0736] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0737] Step 1:

[0738] Users upload contracts and patent drawings using a terminal. The terminal's user interface is intuitive, allowing users to select files using buttons and drag-and-drop functionality. Input is the document file selected by the user, and output is its transmission to the server.

[0739] Step 2:

[0740] The device records the user's facial expressions and voice using its built-in camera and microphone, simultaneously with the uploaded documents. This data is collected to determine the user's emotional state. The input is the user's real-time facial expressions and voice, and the output is the aggregation of this data and its transmission to the server.

[0741] Step 3:

[0742] The server converts received documents into text data using OCR technology. OCR processing recognizes characters from paper documents and image files into text format. The input is image data of the document, and the output is the converted text data.

[0743] Step 4:

[0744] The server analyzes this text data using a generative AI model. The analysis aims to identify risk information and key technological elements. The input is the text data obtained in step 3, and the output is the extracted risk information and key elements.

[0745] Step 5:

[0746] The server uses emotion recognition technology to analyze emotion data transmitted from the terminal. This allows for a detailed identification of the user's emotional state. The input is data of the user's facial expressions and voice, and the output is the identified emotional state.

[0747] Step 6:

[0748] The server customizes and presents the analysis results according to the user's emotional state. For example, if the user is confused, the analysis will be made more detailed and additional explanations will be added. The input is the output data from steps 4 and 5, and the output is the adjusted analysis results report.

[0749] Step 7:

[0750] Users can ask further questions about the presented analysis results. They request additional information using the terminal's question-answering interface. The input is the user's question, and the output is the answer provided by the server.

[0751] Step 8:

[0752] The server searches for relevant information based on the user's question and adjusts the tone of its response based on the user's emotional state. The input is data on the user's question and emotional state, and the output is the adjusted response.

[0753] (Application Example 2)

[0754] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0755] Modern document analysis requires not only the identification of risk items and important information in contracts and patent drawings, but also analysis that takes into account the user's emotional state. However, conventional technologies have made it difficult to consider both the content of the document and the user's emotions simultaneously, limiting the means to realize a user-centered interface.

[0756] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0757] In this invention, the server includes means for receiving documents and converting them into text data according to the data format; means for analyzing multiple types of data formats and extracting risk items and important information using generative artificial intelligence technology; and means for recognizing the user's emotional state and adjusting the method of presenting the analysis results according to that emotion. This enables intuitive and effective document analysis and information provision that is tailored to the user's emotions.

[0758] "Documents" are a general term for papers and materials that organize and formalize information, and include specific content such as contracts and patent drawings.

[0759] "Generative artificial intelligence technology" refers to technologies that generate new information and content from data learned by computers, and includes natural language processing and image recognition.

[0760] "User emotional state" refers to changes in the user's psychological state as perceived from their facial expressions, voice, etc., and is information used to adjust the presentation method of the analysis results.

[0761] A "risk item" refers to a potential danger or a part of a contract or patent drawing that requires attention, and is an item that requires particular care.

[0762] "Analysis results" refer to information obtained after document analysis, and include evaluation results of extracted risk items and important information.

[0763] Users upload documents such as contracts and patent drawings to the system via a terminal. The terminal provides a user interface and facilitates document uploads. The terminal is also equipped with a camera and microphone to analyze the user's facial expressions and voice, collecting emotional data. This emotional data is used to identify the user's emotional state.

[0764] The server receives uploaded documents and converts them into text data according to their format. Next, it uses generative artificial intelligence technology to extract risk items and key information from contracts and patent drawings. Furthermore, it can adjust how the analysis results are presented based on the user's emotional state. This ensures that information is presented in a way that is easy for the user to understand, improving the user experience.

[0765] The hardware required includes a general-purpose terminal with camera functionality, and a high-performance information processing unit as the server. The software used will include OpenCV, PyTorch, and natural language processing libraries (SpaCy and Transformers). By combining these tools, emotion recognition and document analysis will be achieved.

[0766] For example, when a company's legal department reviews a new security agreement, if the user is overwhelmed by the complexity of the agreement, the system includes features to facilitate user understanding, such as providing detailed explanations of risk items and relevant supplementary information. To support this operation, the generating AI model can use the following prompt: "List the key risk points of this security agreement in bullet points and provide additional information to aid in decision-making."

[0767] This system provides users with an environment where they can make important decisions with confidence.

[0768] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0769] Step 1:

[0770] Users upload documents such as contracts and patent drawings to the system via a terminal. The input consists of document data selected by the user, and the output is a digital file transferred to the server. A file selection window opens through the user interface, allowing the user to select a document and press the submit button.

[0771] Step 2:

[0772] The device uses a camera and microphone to simultaneously record the user's facial expressions and voice, collecting emotional data. Input consists of camera video and audio data, while output is analytical data representing the user's emotional state. OpenCV is used to detect facial feature points in real time, and these are analyzed along with audio data using a PyTorch-trained model.

[0773] Step 3:

[0774] The server converts received documents into a text format suitable for analysis. The input is uploaded documents, and the output is data in a text-analyzable format. It uses OCR technology to extract text information from images, PDFs, and other formats, and then converts it to text format using a natural language processing library.

[0775] Step 4:

[0776] The server uses a generative AI model to extract risk items and key information from documents. The input is text data, and the output is a set of extracted risk items and key information. The generative AI model is prompted with the message, "List the key risk points of this security agreement in bullet points, and provide additional information to aid in decision-making," and then performs the action of researching relevant information.

[0777] Step 5:

[0778] The server adjusts how the analysis results are presented, taking into account the user's emotional state. The input consists of emotional data and analysis results, while the output is an optimized report presented to the user. If the user appears confused, the server highlights important information and provides additional relevant details.

[0779] Step 6:

[0780] Users can interact with the generated report and ask questions to supplement the explanation. The input is user-submitted question data, and the output is the answer provided by the server. A question-answering system using natural language processing analyzes the user's inquiry and adjusts the tone of the answer based on sentiment feedback.

[0781] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0782] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0783] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0784] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0785] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0786] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0787] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0788] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0789] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0790] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0791] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0792] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0793] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0794] 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.

[0795] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0796] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0797] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0798] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0799] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0800] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0801] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0802] The following is further disclosed regarding the embodiments described above.

[0803] (Claim 1)

[0804] A means of receiving a document and converting it into text data according to its data format,

[0805] A method for analyzing multiple data formats and extracting risk items and important information using generation AI technology,

[0806] A means of performing a risk assessment from the extracted results and generating a report in which the results can be visualized,

[0807] A means of providing relevant information in response to user inquiries through interactive question-and-answer sessions,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, further comprising a means for providing a user interface for uploading documents and for sending documents received from a user via the interface to a server.

[0811] (Claim 3)

[0812] The system according to claim 1, characterized in that the generation AI technology includes natural language processing and image recognition technology and is capable of simultaneously analyzing risk clauses in contracts and important technical features in patent drawings.

[0813] "Example 1"

[0814] (Claim 1)

[0815] A means for receiving electronic files and converting them into text data according to the information format,

[0816] A means of analyzing multiple types of information formats and using generation AI technology to identify risk items and important information,

[0817] A means of conducting a risk assessment based on the identified results and creating a report that visualizes the results,

[0818] A means of providing relevant information in response to user inquiries through interactive question-and-answer sessions,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, further comprising means for providing a user interface for sending electronic files and for sending electronic files received from a user via the interface to a computer.

[0822] (Claim 3)

[0823] The system according to claim 1, characterized in that the generation AI technology includes natural language processing and image recognition technology, and is capable of simultaneously analyzing risk items and important technical features in the illustrated content within a document.

[0824] "Application Example 1"

[0825] (Claim 1)

[0826] A means for receiving information and converting it into character data according to the information format,

[0827] A method for analyzing multiple types of information formats and extracting risk items and important information using machine learning techniques,

[0828] A means of performing a risk assessment from the extracted results and generating the results as a report that can be visualized,

[0829] A means of providing relevant information in response to user inquiries through interactive question-and-answer sessions,

[0830] A means including optical character recognition technology for acquiring a document as an image using a photographic device and extracting characters from the image,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, further comprising means for providing a user operation screen for uploading information and for transmitting information received from the user via the operation screen to a server.

[0834] (Claim 3)

[0835] The system according to claim 1, characterized in that the machine learning technology includes natural language processing and image analysis technologies, and is capable of simultaneously analyzing risk items in a contract and important technical features in drawings.

[0836] "Example 2 of combining an emotion engine"

[0837] (Claim 1)

[0838] A means of receiving a document and converting it into information according to the data format,

[0839] A method for analyzing multiple data formats and extracting risk information and key elements using generation AI technology,

[0840] A means of performing an evaluation from the extracted results and generating a report in which the results can be visualized,

[0841] A means for identifying the user's emotional state using emotion recognition technology and adjusting the presentation method of the analysis results based on the user's emotions,

[0842] A means of providing relevant information to user inquiries through interactive question-and-answer sessions and adjusting responses according to the user's emotional state,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising means for providing a user interface for uploading documents and for transmitting documents received from a user via the interface to an information processing device.

[0846] (Claim 3)

[0847] The system according to claim 1, characterized in that the generation AI technology includes natural language processing and data recognition technology, and is capable of simultaneously analyzing risk items in a contract and important technical elements in technical drawings.

[0848] "Application example 2 when combining with an emotional engine"

[0849] (Claim 1)

[0850] A means of receiving a document and converting it into text data according to its data format,

[0851] A means of analyzing multiple types of data formats and extracting risk items and important information using generative artificial intelligence technology,

[0852] A means for recognizing the user's emotional state and adjusting the method of presenting the analysis results according to that emotion,

[0853] A means of performing a risk assessment from the extracted results and generating a report in which the results can be visualized,

[0854] A means of providing relevant information in response to user inquiries through interactive question-and-answer sessions,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, further comprising means for providing a user interface for uploading documents and for transmitting documents received from a user via the interface to an information processing device.

[0858] (Claim 3)

[0859] The system according to claim 1, characterized in that the generative artificial intelligence technology includes natural language processing and image recognition technology, is capable of simultaneously analyzing risk clauses in contracts and important technical features in drawings, and can further adjust the tone and content of responses based on the user's emotions. [Explanation of symbols]

[0860] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving a document and converting it into text data according to its data format, A method for analyzing multiple data formats and extracting risk items and important information using generation AI technology, A means of performing a risk assessment from the extracted results and generating a report in which the results can be visualized, A means of providing relevant information in response to user inquiries through interactive question-and-answer sessions, A system that includes this.

2. The system according to claim 1, further comprising a means for providing a user interface for uploading documents and for transmitting documents received from a user via the interface to a server.

3. The system according to claim 1, characterized in that the generation AI technology includes natural language processing and image recognition technology and is capable of simultaneously analyzing risk clauses in contracts and important technical features in patent drawings.

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