system

Abstract

Provide a system. 【Solution means】 Optical character recognition means for extracting character information from a contract document stored as an image file, Generative model means for analyzing contract-related information based on the character information, Index creation means for registering contract information in a contract database using the analyzed information, Search providing means for searching the contract database based on a search query received from a user and providing a link to a relevant contract document, Reference means for immediately referring to a contract document using the link, Receiving means for inputting an image to a portable information terminal, Means for converting the image received by the portable information terminal into character data via the optical character recognition means and automatically extracting contract information using the generative model means, Processing means for efficiently managing and searching the contract information within the portable information terminal, A system including the above.

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 performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] Contract documents saved as image files do not contain character information in the conventional method and are difficult to directly search. For this reason, in order to refer to related contract documents, it is necessary to open and check each file one by one, which has the problem of requiring a large amount of labor and time. Furthermore, there is no regularity in the file names and there is no efficient search means. In such a situation, referring to contract documents is troublesome and has been a factor hindering the efficiency of business operations.

Means for Solving the Problems

[0005] To solve these problems, the present invention provides a system for analyzing and indexing contract documents by extracting character information from image files using optical character recognition means and analyzing contract-related information based on that information using a generation model means. This system registers the analyzed information in a contract database using an index creation means, and the search provision means instantly provides links to relevant contract documents based on search queries from users, thereby significantly improving work efficiency. Furthermore, the reference means allows users to quickly view contract documents by utilizing the links in the search results. This significantly reduces the time and effort required to refer to contract information, thereby improving work efficiency.

[0006] An "image file" refers to digital image data stored on a computer, including formats such as PDF and JPEG.

[0007] A "contract document" is a written document containing legally binding content, and refers to important business documents, including property lease agreements.

[0008] "Optical character recognition means" refers to technology or equipment that handles the process of recognizing characters from image data and converting them into digital text.

[0009] "Character information" refers to the characters themselves contained within a document, or the digital text data derived from that information.

[0010] "Generative model means" refers to an algorithm or system that generates a specific output based on input data, and in particular includes those that utilize artificial intelligence technology.

[0011] "Contract information" refers to specific data included in the contract document, such as contract ID, contracter name, and contract date.

[0012] "Indexing means" refers to the process or system of classifying and organizing data and listing it in a way that allows for efficient searching.

[0013] A "contract database" refers to a digital information management system built to store information related to contracts.

[0014] A "search query" refers to the text or command that a user enters to request specific information from an information system.

[0015] A "search service provider" refers to a system that accepts search queries from users, extracts relevant information from a database, and returns the results.

[0016] A "link" refers to a reference or URL that enables access to a specific digital resource, allowing users to directly access that resource by clicking on it.

[0017] "Means of reference" refers to the functions or processes that users use to directly view documents or information. [Brief explanation of the drawing]

[0018] [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] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0019] 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.

[0020] First, the language used in the following description will be explained.

[0021] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Further, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] In the system of the present invention, it is possible to efficiently manage contract documents stored as image files on a computer and make them easily accessible to users. A specific form for implementing this system is described below.

[0040] First, the user accesses the business system using a terminal and uploads image files of the contract documents to be managed to the server. The uploaded files are received by the server and saved to a specified folder.

[0041] Next, the server performs optical character recognition (OCR) on the saved image files. This extracts the text information contained in the contract documents and converts it into digital text. This text information is retained as text data for use in subsequent processing.

[0042] Subsequently, the server uses a generative model to analyze the extracted text information and identify important information related to the contract. Through this analysis, information such as the contract ID, contract date, contractor name, and contract details are extracted as structured data.

[0043] The server registers the obtained analysis results into a contract database for efficient management of contract information using an indexing mechanism. Links to related image files are also stored at this time, ensuring that the contract information is readily accessible.

[0044] Users can search for contract information via their terminal. Based on the contract ID and keywords entered by the user, the server sends a search query to the contract database and finds relevant documents. The server provides the found information to the user and returns search results, including links to image files.

[0045] Ultimately, users can directly access contract documents using the provided links. For example, if a user receives search results for "Lease Agreement for Property A," they can open the document with a single click. This process significantly reduces the effort required to search for and access contract information, thereby improving operational efficiency.

[0046] Thus, the system of the present invention reduces the workload of users by automating the contract document management and search processes.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The user accesses the business system using a terminal and uploads image files of contract documents to the server. The terminal displays a file selection dialog and sends the file selected by the user to the server.

[0050] Step 2:

[0051] The server saves the received image files to the specified folder. This saving process is performed using a file management system.

[0052] Step 3:

[0053] The server performs optical character recognition (OCR) processing on the stored image files. Using OCR software, it extracts text information from the images and converts it into digital text format.

[0054] Step 4:

[0055] The server passes the character information extracted by OCR to the generative model. The generative model analyzes the contract document and identifies important information such as the contract ID, contract date, and contractor name.

[0056] Step 5:

[0057] The server indexes the analyzed contract information as structured data. This data is then registered in the contract information database.

[0058] Step 6:

[0059] The user enters a contract ID or keywords to search for specific contract information via their device. The device then sends the entered query to the server.

[0060] Step 7:

[0061] The server receives a search query from the user, searches the contract information database, and extracts relevant information. It then generates search results, including links to relevant contract documents.

[0062] Step 8:

[0063] The server sends the generated search results back to the terminal. The terminal displays the results in a list format for the user to review.

[0064] Step 9:

[0065] The user selects a link provided through their device and opens the relevant contract document in their browser. The server provides the requested image file, allowing the user to view it directly.

[0066] (Example 1)

[0067] 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."

[0068] Conventional contract document management systems have faced the problem of requiring considerable time and effort to extract textual information and identify important details when digitizing and managing paper-based contract documents. Furthermore, the limited search functionality makes it difficult for users to quickly find the contract documents they need. There is a need to solve these problems and improve the efficiency of contract document management and searching.

[0069] 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.

[0070] In this invention, the server includes character recognition means for extracting character data from contract documents stored as image files, model means for identifying important information using the character data, and registration means for registering structured data containing the identified information in a management infrastructure. This enables the rapid and easy digitization of paper-based contract documents, as well as efficient searching and management.

[0071] An "image file" refers to a file that stores visual information in digital format, and scanned paper documents are an example of this.

[0072] "Character data" refers to digital data extracted from visual characters, in a format that can be processed and searched by a computer.

[0073] "Character recognition means" refers to technologies and software for extracting character data from image files, and optical character recognition (OCR) technology is included in this.

[0074] "Modeling techniques" refer to analytical techniques and programs used to identify important information from text data extracted using machine learning or artificial intelligence.

[0075] "Structured data" refers to data that has been organized and formalized so that it can be efficiently managed and searched in databases and other systems.

[0076] "Management infrastructure" refers to systems and databases for storing and managing structured data, enabling secure data management and rapid access.

[0077] "Registration means" refers to functions or mechanisms for adding processed information to a management infrastructure and storing it.

[0078] "Information provision means" refers to the technologies and methods used to present appropriate documents and related information in response to search requests from users.

[0079] This invention relates to an image recognition and data processing system for streamlining the management of contract documents. This system mainly consists of a server, terminals, and users.

[0080] First, the user uploads image files of the contract documents to the server using their device. These devices typically include personal computers or tablets, and they access the business system via a browser or dedicated application.

[0081] The server processes the data using the following software and technologies. The server has the capability to receive uploaded image files and store them in a secure directory. Next, the server extracts text data from the image files using Optical Character Recognition (OCR) technology. It is recommended to use an OCR engine such as Tesseract for this process.

[0082] The extracted text data is then input into a generative AI model for analysis. This analysis identifies important information related to the contract. A sophisticated natural language processing technology such as GPT-4® is suitable for this model. The analyzed information is organized as structured data and registered in a database managed by the server. An RDBMS such as MySQL® can be used for this database.

[0083] Users can search for contract information from their terminal using contract identification numbers and related keywords. Based on the search query, the server quickly accesses the database and provides links to relevant documents. At this time, by entering a prompt in the format of "Please tell me the contract information and contract name related to property B," the necessary information can be obtained efficiently.

[0084] This system's configuration and technology automate the management and retrieval of contract documents, allowing users to quickly digitize paper contracts and access them instantly. For example, a real estate management company can centrally manage multiple contracts related to properties, significantly improving operational efficiency.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The user uses a terminal to select an image file of the contract document. They drag and drop the selected image file onto the system's upload interface and press the upload button. The image file is sent to the server as input. The server then receives the file using a data transfer protocol and saves it to the specified directory. This process results in an output indicating that the image file has been saved in storage.

[0088] Step 2:

[0089] The server initiates optical character recognition (OCR) processing on the saved image file. The saved image file is provided as input. Specifically, software such as Tesseract OCR is used to analyze the characters in the image and convert the character data into a digital format. Through this process, the characters in the image are extracted as text data and output as intermediate data for use in subsequent processes.

[0090] Step 3:

[0091] The server inputs the character data extracted by OCR into a generative AI model. The generative AI model (e.g., GPT-4) analyzes the contract details and identifies important information such as the contract ID, contractor name, and contract date. The server organizes the analysis results as structured data in JSON format and outputs the analyzed information. This is treated as preparation for database registration.

[0092] Step 4:

[0093] The server registers the analyzed structured data as contract information in the management infrastructure. The generated structured data is provided to the server as input. A query for data registration is executed against the database system (e.g., MySQL), and the relevant information is stored in the database. This process outputs that the contract information is securely stored in the database and is searchable at a later date.

[0094] Step 5:

[0095] The user accesses an interface via their terminal to search for contract information. A search query (e.g., "Show contract for property B") is entered as a prompt and sent to the server. The server receives the search criteria and queries the contract database to find the relevant document information. This process outputs links to the corresponding contract documents and the relevant information.

[0096] Step 6:

[0097] The user views the search results provided by the server and clicks to open the required document by clicking the link. The link is provided as input, and a browser or PDF reader is launched on the user's device. This allows the user to view the contents of the contract document on the screen. Upon completion of this action, an output indicating that the review of the contract contents is complete is displayed.

[0098] (Application Example 1)

[0099] 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."

[0100] To efficiently manage contract documents, digitization of documents, automated information extraction, and rapid searching are required. However, existing systems have limitations in image file processing and insufficient search flexibility, resulting in time-consuming and labor-intensive management and retrieval of contract information. Furthermore, management on mobile devices was not considered, making it difficult to access contract information quickly while away from the office or in the field.

[0101] 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.

[0102] In this invention, the server includes optical character recognition means for extracting character information from contract documents stored as image files; generation model means for analyzing contract-related information based on the character information; index creation means for registering contract information in a contract database using the analyzed information; receiving means for inputting images to a mobile information terminal; means for converting the images received by the mobile information terminal into character data via the optical character recognition means and automatically extracting contract information using the generation model means; and processing means for efficiently managing and searching the contract information within the mobile information terminal. This enables digitization of contract documents, automatic analysis, efficient information management, and immediate retrieval.

[0103] An "image file" is a file format of electronic data that visually represents information, and is used to digitize contract documents from paper format.

[0104] A "contract document" is a written record of an agreement between parties, detailing the terms and conditions of a transaction or service.

[0105] "Textual information" refers to the text data contained within a document, and is the information necessary to understand the contents of a contract.

[0106] "Optical character recognition means" refers to a device or software that mechanically reads characters contained in an image file and extracts them as text data.

[0107] The "generative model means" is AI-based software for analyzing important information related to a contract based on extracted textual information.

[0108] An "indexing tool" is a device or software that organizes analyzed contract information into a database for efficient searching and management.

[0109] A "search and provision means" is a system that searches a contract database based on user input and provides access links to relevant documents.

[0110] "Receiving means" refers to a device or software that has the function of capturing images on a mobile information terminal and receiving data from that terminal.

[0111] "Processing means" refers to a device or software that efficiently manages the acquired contract information and processes and maintains it so that it can be quickly accessed when needed.

[0112] A "personal information terminal" is an electronic device that a user can carry with them and that is capable of taking pictures and managing and viewing data.

[0113] The system that realizes this invention provides efficient management and rapid access to contract documents. At the core of the system are a personal digital assistant (PDCA) terminal and a server. The PDCA terminal is equipped with a receiving mechanism for users to photograph or upload contract documents. This terminal takes the form of a smartphone or tablet and has a camera function and applications installed.

[0114] Image files received from the terminal are transferred to the server. The server extracts text information from the image files using optical character recognition (OCR) means. For optical character recognition, OCR libraries such as Tesseract are used. The server analyzes the extracted text information using a generative AI model to identify important contract information. The generated information is organized as structured data such as contract ID and contract date, and registered in the contract database using an indexing means. This enables efficient management of contract information.

[0115] Users access the server via their mobile devices to search and refer to contract information. A search engine exists on the server, supporting searches based on contract IDs and keywords. This system displays search results instantly, enabling quick access to relevant documents. For example, if a user wants to check information on a "residential lease agreement," they can access the relevant contract simply by entering the contract holder's name or contract ID.

[0116] When utilizing the generative AI model, prompts are generated to more precisely analyze the contract content. An example of such a prompt is a specific instruction such as, "Extract and structure important contract information from the following text: ○○ (OCR result text)." This mechanism automatically organizes the vast amount of information contained in the contract, providing users with an environment where they can quickly access the information they need.

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] Users take photos of contract documents or select existing image files using their mobile devices and upload them to the server via the application. The input is an image file, and the output is a file sent to the server. This allows users to register digital versions of contract documents in the system.

[0120] Step 2:

[0121] The server passes the received image file to the optical character recognition (OCR) system and begins processing. The input is an image file, and character information is extracted using OCR technology. The output is text data. Using an OCR library (e.g., Tesseract), the server converts the text in the image into a machine-readable format.

[0122] Step 3:

[0123] The server passes the extracted text information to a generating AI model, which generates prompt sentences and performs analysis. The input is text data obtained by OCR, and the generating AI model is used to identify important information about the contract (contract ID, contractor, contract date, etc.). The output is structured contract information data. This analysis makes the contract content clearer and more organized.

[0124] Step 4:

[0125] The server uses an indexing mechanism to register structured contract information in the contract database. The input is structured contract information data, and registration in the database enables efficient searching. The output is the registered information organized in the database. This makes contract information easier to manage.

[0126] Step 5:

[0127] The user enters a search query using a mobile device. The input is a query based on the user's requested contract information (contract ID and keywords). The device sends this query to the server.

[0128] Step 6:

[0129] The server uses search mechanisms to search the contract database and identify links to relevant contract document information. The input is the user's search query, and the results of the database search are returned to the user as output. This allows the user to quickly access the necessary contract information.

[0130] Step 7:

[0131] Users access contract documents using links provided in the search results. The input is the link within the search results, and the output is the screen displaying the contract document. This allows users to quickly find the necessary contracts.

[0132] 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.

[0133] In the system of the present invention, a more human-friendly operating environment is realized by combining an emotion engine to manage contract documents and provide user interaction that responds to emotions. A specific form for implementing this system is described below.

[0134] First, the user accesses the business system using a terminal and uploads an image file of the contract document to the server. The uploaded file is received by the server and saved to the appropriate folder. The server performs optical character recognition (OCR) processing on the saved image file to extract text information. This text information is analyzed by a generative model, and the contract information is extracted as structured data.

[0135] Next, the analyzed contract information is registered in a contract database using an indexing mechanism. This database is used to quickly respond to user search queries and provide information on relevant contract documents.

[0136] The emotion engine recognizes the user's emotional state by analyzing their voice and text input. Based on this state, it can dynamically adjust the display of search results, notifications related to contract documents, and alerts. For example, if the user is showing negative emotions, the system will automatically adjust the information to be displayed in a calmer tone.

[0137] As a concrete example, when a user searches for contract information using their contract ID, the terminal analyzes the user's input and tone of voice using an emotion engine. If the user is experiencing stress, the server takes measures such as simplifying the navigation of search results or displaying a gentler alert message.

[0138] Thus, by incorporating an emotion engine, the present invention provides an interface that takes user emotions into consideration, making the management and retrieval of contract documents more comfortable. This not only improves operational efficiency but also enhances the user experience.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The user logs into the business system using a terminal and uploads image files of contract documents to the server. The terminal opens a file selection dialog and sends the files selected by the user to the server using the specified protocol.

[0142] Step 2:

[0143] The server saves the received image files to a designated directory. This saving process is automated, and the file name and save location are uniquely determined by the system.

[0144] Step 3:

[0145] The server initiates optical character recognition (OCR) processing on the saved image files. The OCR engine analyzes the image data and extracts the character information contained within it as text data.

[0146] Step 4:

[0147] The server passes the text data extracted by OCR to the generation model. The generation model analyzes the text data and identifies key fields such as contract ID, contract date, and contractor name.

[0148] Step 5:

[0149] The server creates an index based on the analyzed information and registers it in the contract information database. This database registration includes links to related image files.

[0150] Step 6:

[0151] When a user searches for contract information through their device, the emotion engine is activated to analyze the user's input. It uses voice and text data acquired from the device's microphone and keyboard to determine the user's emotional state.

[0152] Step 7:

[0153] The server receives the user's search query along with the emotional state recognized by the emotion engine. The search service then searches the contract database and generates search results that are adjusted according to the user's emotions.

[0154] Step 8:

[0155] The server sends the generated search results to the device. The device displays the results to the user and provides an interface tailored to their emotional state. For example, if the user shows signs of stress, the navigation is simplified.

[0156] Step 9:

[0157] The user clicks a link from their device and directly accesses the relevant contract document. The server immediately provides the corresponding image file, allowing the user to comfortably review the document content.

[0158] (Example 2)

[0159] 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".

[0160] In modern information management, contract documents are complex, and efficient management and retrieval are essential. Furthermore, reducing user stress and providing a more comfortable interface are also crucial challenges. However, conventional systems lack dynamic information presentation that considers user emotions, highlighting the need for innovation to further improve user convenience.

[0161] 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.

[0162] In this invention, the server includes character recognition means for extracting symbolic information from documents stored as image data, generation model means for analyzing contract-related data based on the symbolic information, and emotion analysis and adjustment means for analyzing the user's emotional state and adjusting the method of information presentation. This not only enables rapid and efficient management and retrieval of contract documents, but also allows for information provision that takes the user's emotions into consideration, thereby improving the user experience.

[0163] "Image data" refers to all data stored in digital format that includes visual information.

[0164] "Symbolic information" refers to information composed of letters, numbers, and other visual symbols.

[0165] "Character recognition means" refers to a technology or device that automatically reads symbolic information from image data.

[0166] "Generative modeling means" refers to an algorithm or system for analyzing data and generating specific patterns or information.

[0167] An "information recording device" refers to a system or device for storing data and retaining it so that it can be searched or referenced later.

[0168] "Information registration means" refers to a technology or device that provides the function of systematically registering structured information into an information recording device.

[0169] A "search request" refers to a request made by a user to obtain specific information.

[0170] "Connection information" refers to links or reference information that enable access to related data or documents.

[0171] "Emotional analysis and adjustment means" refers to a technology or system for evaluating a user's emotional state and changing the way information is presented based on that evaluation.

[0172] This invention combines several technical elements to realize a system that manages contract documents and provides user-responsive interaction.

[0173] The user accesses the business system via a terminal and uploads image data of contract documents to the server. The system extracts symbolic information from the image data using appropriate optical character recognition (OCR) means. For this process, an OCR engine such as Tesseract is used as the OCR software. This means that the image data is converted into a machine-readable format.

[0174] The server uses AI technologies, such as Transformers models, as a generative model to analyze the extracted symbolic information. Through this analysis, contract-related data is extracted from the contract document and registered in a contract database, which acts as an information recording device. This allows users to easily search and refer to contract information as needed.

[0175] Furthermore, the device sends user voice and text input to the server, which evaluates the user's emotional state using sentiment analysis and adjustment mechanisms. For example, by using the Google® Cloud Natural Language API, it is possible to analyze emotions from the user's text and voice and adjust the presentation of information according to the user's emotions. This allows for the presentation of information to be made gentler or alert messages to be expressed in a milder tone if negative emotions are detected.

[0176] For example, when a user searches for a document using a contract identification code, the terminal analyzes the expression and tone on the server. If the server detects that the user is experiencing stress, it simplifies and presents the results. Furthermore, user experience is improved by displaying user-friendly navigation and messages.

[0177] Example of a prompt:

[0178] "Search for contract information using contract ID 12345, analyze user sentiment, and adjust the results accordingly."

[0179] Thus, the present invention improves operational efficiency and user experience by providing an emotionally sensitive interface and enabling efficient contract document management.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The user accesses the business system using a terminal, selects image data of contract documents in a file selection window, and uploads it to the server. The input is the image file selected by the user, and the output is the saving of the file to a specific folder on the server. The terminal communicates with the server to process the file appropriately and starts data transfer.

[0183] Step 2:

[0184] The server performs optical character recognition (OCR) processing on the received image data. The input is the uploaded image data, and the output is text data. Specifically, the server applies an OCR engine to extract character information from the image. After processing, the extracted text data is temporarily stored and passed on to the next analysis process.

[0185] Step 3:

[0186] The server analyzes the extracted text data using a generative AI model. The input is the text data obtained in step 2, and the output is structured contract information. The model identifies, for example, dates, party names, and conditions related to the contract, and organizes them as contract information. The server generates this information and prepares it for registration in the database.

[0187] Step 4:

[0188] The server uses an indexing engine to register the obtained contract information in the contract database. The input is structured contract information, and it generates output indicating that registration is complete. The database organizes the information and indexes it so that it can respond quickly to subsequent search requests.

[0189] Step 5:

[0190] User actions and voice are captured by the terminal and sent to the server. The input here is the user's voice and text-based interface operations, and the output is the data sent to the server for analysis. The terminal then formats this data appropriately as sentiment data, enabling the subsequent sentiment analysis process.

[0191] Step 6:

[0192] The server uses an emotion analysis engine to determine the user's emotional state. Input is voice and text data sent by the user, and output is information about the user's emotions. Based on this information, the server adjusts the information presentation method to provide a more appropriate interface.

[0193] Step 7:

[0194] The server provides access to appropriate contract documents based on the user's sentiment and search queries. Inputs are the user's sentiment information and search queries, while output is tailored search results and information presentations. This allows users to obtain the information they need without stress.

[0195] (Application Example 2)

[0196] 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".

[0197] When managing contract documents, there is a lack of interfaces that take into account the user's emotional state, resulting in insufficient improvements in usability and the comfort of the contract process. Therefore, there is a need to improve the overall experience of contract procedures, including electronic payments, by enabling flexible information presentation that responds to the user's emotions.

[0198] 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.

[0199] In this invention, the server includes optical character recognition means for extracting character information from contract documents stored as image files, generative model means for analyzing contract-related information based on the character information, and emotion response means for analyzing the user's emotional state, dynamically adjusting the interface based on the corresponding emotional state, and adaptively providing information. This enables efficient management of contract documents and flexible information provision that takes into account the user's emotions.

[0200] An "image file" is a collection of visual information stored using electronic means, and it is a digital record of the contents of a contract document.

[0201] "Text information" refers to string data extracted from image files, specifically the text content included in contract documents.

[0202] "Optical character recognition means" refers to devices or software that include technology for detecting characters within an image and converting them into electronic text data.

[0203] A "generative modeling method" is a technology that analyzes contract-related information based on input data and generates structured related data.

[0204] "Index creation methods" refer to the procedures and technologies used to register extracted contract information in a database so that it can be efficiently managed and searched.

[0205] A "search provision method" is a technology that provides access links to relevant contract documents and information based on search queries from users.

[0206] "Means of reference" refers to a means of immediately accessing contract documents and verifying their contents.

[0207] "Emotional response methods" are technologies aimed at analyzing a user's emotional state and adjusting the interface or presenting information accordingly.

[0208] This system is designed to provide efficient management of contract documents and an interface that takes user emotions into consideration. First, the user acquires an image of the contract document using an electronic terminal and sends it to the server. The server extracts the text information from the image file using optical character recognition means and analyzes the extracted text information using a generation model means. Through this analysis process, information related to the contract is generated as structured data.

[0209] Next, the server registers the structured contract information in a database using an indexing mechanism. This database enables high-speed searching and provides links to relevant contract documents in response to user search queries.

[0210] The interface analyzes the user's emotional state through emotion-responsive mechanisms and dynamically adjusts how information is presented based on those emotions. The server recognizes emotions from the user's input text or voice data and adjusts the interface accordingly. For example, if the user is stressed, the interface will display information in a calmer tone.

[0211] The specific technologies used include the Google Cloud Vision API for optical character recognition, the Python NLTK library for natural language processing, and the IBM Watson® Tone Analyzer API for sentiment analysis. The hardware can be a smartphone or a personal computer.

[0212] As a concrete example, when a user reviews the terms of service for a new electronic payment service, they can take a picture of the document with their device's camera and instantly view the text. If the user expresses concern, the system uses an AI model to display a message such as, "Please rest assured. We will provide you with support information regarding this agreement immediately." An example of a prompt message in this case would be, "The user is feeling anxious while reviewing the contract. Please sense the user's emotional state and generate a reassuring message."

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The user obtains the contract document as an image using an electronic device. The input is image data of the contract document, and this image is sent to the server as output. The user uses the device's camera function to photograph the entire document and uploads that data to the server.

[0216] Step 2:

[0217] The server performs optical character recognition (OCR) processing on the received image data. The input is image data sent by the user, and the output is extracted character information. As for OCR technology, the Google Cloud Vision API is used to analyze the characters in the image and convert them into text data.

[0218] Step 3:

[0219] The server analyzes the obtained character information using a generative model. The input is text data from OCR processing, and the output is structured contract information. Natural language processing techniques are used to extract contract requirements and conditions and convert them into structured data for registration in the database.

[0220] Step 4:

[0221] The server registers structured contract information in a database. The input is contract information structured by a generative model, and the output is a confirmation of successful registration to the contract database. An indexing mechanism is used to organize the data in a format that allows for rapid searching.

[0222] Step 5:

[0223] This system analyzes the user's emotional state and dynamically adjusts the interface. Input is user voice or text data, and output is adaptive information presentation corresponding to the emotion. The IBM Watson Tone Analyzer API is used for emotion analysis, and the server adjusts the interface tone and information content according to the recognized emotion.

[0224] Step 6:

[0225] The user enters a search query and retrieves information on relevant contract documents. The input is the user's search query, and the output is the relevant contract documents and their links. The search results are displayed on the terminal interface along with emotionally sensitive and gentle messages.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] In the system of the present invention, it is possible to efficiently manage contract documents stored as image files on a computer and make them easily accessible to users. A specific form for implementing this system is described below.

[0243] First, the user accesses the business system using a terminal and uploads image files of the contract documents to be managed to the server. The uploaded files are received by the server and saved to a specified folder.

[0244] Next, the server performs optical character recognition (OCR) on the saved image files. This extracts the text information contained in the contract documents and converts it into digital text. This text information is retained as text data for use in subsequent processing.

[0245] Subsequently, the server uses a generative model to analyze the extracted text information and identify important information related to the contract. Through this analysis, information such as the contract ID, contract date, contractor name, and contract details are extracted as structured data.

[0246] The server registers the obtained analysis results into a contract database for efficient management of contract information using an indexing mechanism. Links to related image files are also stored at this time, ensuring that the contract information is readily accessible.

[0247] Users can search for contract information via their terminal. Based on the contract ID and keywords entered by the user, the server sends a search query to the contract database and finds relevant documents. The server provides the found information to the user and returns search results, including links to image files.

[0248] Ultimately, users can directly access contract documents using the provided links. For example, if a user receives search results for "Lease Agreement for Property A," they can open the document with a single click. This process significantly reduces the effort required to search for and access contract information, thereby improving operational efficiency.

[0249] Thus, the system of the present invention reduces the workload of users by automating the contract document management and search processes.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] The user accesses the business system using a terminal and uploads image files of contract documents to the server. The terminal displays a file selection dialog and sends the file selected by the user to the server.

[0253] Step 2:

[0254] The server saves the received image files to the specified folder. This saving process is performed using a file management system.

[0255] Step 3:

[0256] The server performs optical character recognition (OCR) processing on the stored image files. Using OCR software, it extracts text information from the images and converts it into digital text format.

[0257] Step 4:

[0258] The server passes the character information extracted by OCR to the generative model. The generative model analyzes the contract document and identifies important information such as the contract ID, contract date, and contractor name.

[0259] Step 5:

[0260] The server indexes the analyzed contract information as structured data. This data is then registered in the contract information database.

[0261] Step 6:

[0262] The user enters a contract ID or keywords to search for specific contract information via their device. The device then sends the entered query to the server.

[0263] Step 7:

[0264] The server receives a search query from the user, searches the contract information database, and extracts relevant information. It then generates search results, including links to relevant contract documents.

[0265] Step 8:

[0266] The server sends the generated search results back to the terminal. The terminal displays the results in a list format for the user to review.

[0267] Step 9:

[0268] The user selects a link provided through their device and opens the relevant contract document in their browser. The server provides the requested image file, allowing the user to view it directly.

[0269] (Example 1)

[0270] 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."

[0271] Conventional contract document management systems have faced the problem of requiring considerable time and effort to extract textual information and identify important details when digitizing and managing paper-based contract documents. Furthermore, the limited search functionality makes it difficult for users to quickly find the contract documents they need. There is a need to solve these problems and improve the efficiency of contract document management and searching.

[0272] 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.

[0273] In this invention, the server includes character recognition means for extracting character data from contract documents stored as image files, model means for identifying important information using the character data, and registration means for registering structured data containing the identified information in a management infrastructure. This enables the rapid and easy digitization of paper-based contract documents, as well as efficient searching and management.

[0274] An "image file" refers to a file that stores visual information in digital format, and scanned paper documents are an example of this.

[0275] "Character data" refers to digital data extracted from visual characters, in a format that can be processed and searched by a computer.

[0276] "Character recognition means" refers to technologies and software for extracting character data from image files, and optical character recognition (OCR) technology is included in this.

[0277] "Modeling techniques" refer to analytical techniques and programs used to identify important information from text data extracted using machine learning or artificial intelligence.

[0278] "Structured data" refers to data that has been organized and formalized so that it can be efficiently managed and searched in databases and other systems.

[0279] "Management infrastructure" refers to systems and databases for storing and managing structured data, enabling secure data management and rapid access.

[0280] "Registration means" refers to functions or mechanisms for adding processed information to a management infrastructure and storing it.

[0281] "Information provision means" refers to the technologies and methods used to present appropriate documents and related information in response to search requests from users.

[0282] This invention relates to an image recognition and data processing system for streamlining the management of contract documents. This system mainly consists of a server, terminals, and users.

[0283] First, the user uploads image files of the contract documents to the server using their device. These devices typically include personal computers or tablets, and they access the business system via a browser or dedicated application.

[0284] The server performs processing using the following multiple software and technologies. The server has the function of receiving the uploaded image file and saving it in a secure directory. Next, the server uses optical character recognition (OCR) technology to extract character data from the image file. It is recommended to use an OCR engine such as Tesseract in this process.

[0285] The extracted character data is then input into the generative AI model for analysis. Through this analysis, important information regarding the contract is identified. Advanced natural language processing technologies such as GPT-4 are suitable as the model. The analyzed information is organized as structured data and registered in the database that serves as the management foundation of the server. An RDBMS such as MySQL can be used for this database.

[0286] Users can search for contract information from the terminal using the contract identification number and related keywords. Based on the search query, the server quickly references the database and provides links to relevant documents. At this time, by inputting in the form of a prompt sentence such as "Tell me the contract information related to Property B and the names of the contractors", the necessary information can be efficiently obtained.

[0287] With the configuration and technology of this system, the management and search of contract documents are automated, enabling users to quickly digitize paper-based contracts and access them immediately. As a specific example, a real estate management company can centrally manage multiple contracts related to properties, greatly improving business efficiency.

[0288] The flow of the specific processing in Example 1 will be described using FIG. 11.

[0289] Step 1:

[0290] The user uses a terminal to select an image file of the contract document. They drag and drop the selected image file onto the system's upload interface and press the upload button. The image file is sent to the server as input. The server then receives the file using a data transfer protocol and saves it to the specified directory. This process results in an output indicating that the image file has been saved in storage.

[0291] Step 2:

[0292] The server initiates optical character recognition (OCR) processing on the saved image file. The saved image file is provided as input. Specifically, software such as Tesseract OCR is used to analyze the characters in the image and convert the character data into a digital format. Through this process, the characters in the image are extracted as text data and output as intermediate data for use in subsequent processes.

[0293] Step 3:

[0294] The server inputs the character data extracted by OCR into a generative AI model. The generative AI model (e.g., GPT-4) analyzes the contract details and identifies important information such as the contract ID, contractor name, and contract date. The server organizes the analysis results as structured data in JSON format and outputs the analyzed information. This is treated as preparation for database registration.

[0295] Step 4:

[0296] The server registers the analyzed structured data as contract information in the management infrastructure. The generated structured data is provided to the server as input. A query for data registration is executed against the database system (e.g., MySQL), and the relevant information is stored in the database. This process outputs that the contract information is securely stored in the database and is searchable at a later date.

[0297] Step 5:

[0298] The user accesses an interface via their terminal to search for contract information. A search query (e.g., "Show contract for property B") is entered as a prompt and sent to the server. The server receives the search criteria and queries the contract database to find the relevant document information. This process outputs links to the corresponding contract documents and the relevant information.

[0299] Step 6:

[0300] The user views the search results provided by the server and clicks to open the required document by clicking the link. The link is provided as input, and a browser or PDF reader is launched on the user's device. This allows the user to view the contents of the contract document on the screen. Upon completion of this action, an output indicating that the review of the contract contents is complete is displayed.

[0301] (Application Example 1)

[0302] 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."

[0303] To efficiently manage contract documents, digitization of documents, automated information extraction, and rapid searching are required. However, existing systems have limitations in image file processing and insufficient search flexibility, resulting in time-consuming and labor-intensive management and retrieval of contract information. Furthermore, management on mobile devices was not considered, making it difficult to access contract information quickly while away from the office or in the field.

[0304] 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.

[0305] In this invention, the server includes an optical character recognition means for extracting character information from a contract document stored as an image file, a generation model means for analyzing contract-related information based on the character information, an index creation means for registering contract information in a contract database using the analyzed information, a receiving means for inputting an image to a portable information terminal, a means for converting the image received by the portable information terminal into character data via the optical character recognition means and automatically extracting contract information using the generation model means, and a processing means for efficiently managing and searching the contract information within the portable information terminal. As a result, digitization, automatic analysis, efficient information management, and instant search of contract documents become possible.

[0306] An "image file" is a file format of electronic data that visually represents information and is used to digitize a contract document from a paper medium.

[0307] A "contract document" is a document that documents the agreement reached between parties and details the terms of a transaction or service.

[0308] "Character information" refers to text data contained in a document and is information necessary for understanding the contract content.

[0309] "Optical character recognition means" is a device or software that mechanically reads characters contained in an image file and extracts them as text data.

[0310] "Generation model means" is AI-based software for analyzing important contract-related information based on the extracted character information.

[0311] "Index creation means" is a device or software that organizes and arranges analyzed contract information in a database for efficient search and management.

[0312] "Search providing means" is a system that searches a contract database based on an input from a user and provides an access link to a relevant document.

[0313] "Receiving means" refers to a device or software that has the function of capturing images on a mobile information terminal and receiving data from that terminal.

[0314] "Processing means" refers to a device or software that efficiently manages the acquired contract information and processes and maintains it so that it can be quickly accessed when needed.

[0315] A "personal information terminal" is an electronic device that a user can carry with them and that is capable of taking pictures and managing and viewing data.

[0316] The system that realizes this invention provides efficient management and rapid access to contract documents. At the core of the system are a personal digital assistant (PDCA) terminal and a server. The PDCA terminal is equipped with a receiving mechanism for users to photograph or upload contract documents. This terminal takes the form of a smartphone or tablet and has a camera function and applications installed.

[0317] Image files received from the terminal are transferred to the server. The server extracts text information from the image files using optical character recognition (OCR) means. For optical character recognition, OCR libraries such as Tesseract are used. The server analyzes the extracted text information using a generative AI model to identify important contract information. The generated information is organized as structured data such as contract ID and contract date, and registered in the contract database using an indexing means. This enables efficient management of contract information.

[0318] Users access the server via their mobile devices to search and refer to contract information. A search engine exists on the server, supporting searches based on contract IDs and keywords. This system displays search results instantly, enabling quick access to relevant documents. For example, if a user wants to check information on a "residential lease agreement," they can access the relevant contract simply by entering the contract holder's name or contract ID.

[0319] When utilizing the generative AI model, prompts are generated to more precisely analyze the contract content. An example of such a prompt is a specific instruction such as, "Extract and structure important contract information from the following text: ○○ (OCR result text)." This mechanism automatically organizes the vast amount of information contained in the contract, providing users with an environment where they can quickly access the information they need.

[0320] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0321] Step 1:

[0322] Users take photos of contract documents or select existing image files using their mobile devices and upload them to the server via the application. The input is an image file, and the output is a file sent to the server. This allows users to register digital versions of contract documents in the system.

[0323] Step 2:

[0324] The server passes the received image file to the optical character recognition (OCR) system and begins processing. The input is an image file, and character information is extracted using OCR technology. The output is text data. Using an OCR library (e.g., Tesseract), the server converts the text in the image into a machine-readable format.

[0325] Step 3:

[0326] The server passes the extracted text information to a generating AI model, which generates prompt sentences and performs analysis. The input is text data obtained by OCR, and the generating AI model is used to identify important information about the contract (contract ID, contractor, contract date, etc.). The output is structured contract information data. This analysis makes the contract content clearer and more organized.

[0327] Step 4:

[0328] The server uses an indexing mechanism to register structured contract information in the contract database. The input is structured contract information data, and registration in the database enables efficient searching. The output is the registered information organized in the database. This makes contract information easier to manage.

[0329] Step 5:

[0330] The user enters a search query using a mobile device. The input is a query based on the user's requested contract information (contract ID and keywords). The device sends this query to the server.

[0331] Step 6:

[0332] The server uses search mechanisms to search the contract database and identify links to relevant contract document information. The input is the user's search query, and the results of the database search are returned to the user as output. This allows the user to quickly access the necessary contract information.

[0333] Step 7:

[0334] Users access contract documents using links provided in the search results. The input is the link within the search results, and the output is the screen displaying the contract document. This allows users to quickly find the necessary contracts.

[0335] 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.

[0336] In the system of the present invention, a more human-friendly operating environment is realized by combining an emotion engine to manage contract documents and provide user interaction that responds to emotions. A specific form for implementing this system is described below.

[0337] First, the user accesses the business system using a terminal and uploads an image file of the contract document to the server. The uploaded file is received by the server and saved to the appropriate folder. The server performs optical character recognition (OCR) processing on the saved image file to extract text information. This text information is analyzed by a generative model, and the contract information is extracted as structured data.

[0338] Next, the analyzed contract information is registered in a contract database using an indexing mechanism. This database is used to quickly respond to user search queries and provide information on relevant contract documents.

[0339] The emotion engine recognizes the user's emotional state by analyzing their voice and text input. Based on this state, it can dynamically adjust the display of search results, notifications related to contract documents, and alerts. For example, if the user is showing negative emotions, the system will automatically adjust the information to be displayed in a calmer tone.

[0340] As a concrete example, when a user searches for contract information using their contract ID, the terminal analyzes the user's input and tone of voice using an emotion engine. If the user is experiencing stress, the server takes measures such as simplifying the navigation of search results or displaying a gentler alert message.

[0341] Thus, by incorporating an emotion engine, the present invention provides an interface that takes user emotions into consideration, making the management and retrieval of contract documents more comfortable. This not only improves operational efficiency but also enhances the user experience.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The user logs into the business system using a terminal and uploads image files of contract documents to the server. The terminal opens a file selection dialog and sends the files selected by the user to the server using the specified protocol.

[0345] Step 2:

[0346] The server saves the received image files to a designated directory. This saving process is automated, and the file name and save location are uniquely determined by the system.

[0347] Step 3:

[0348] The server initiates optical character recognition (OCR) processing on the saved image files. The OCR engine analyzes the image data and extracts the character information contained within it as text data.

[0349] Step 4:

[0350] The server passes the text data extracted by OCR to the generation model. The generation model analyzes the text data and identifies key fields such as contract ID, contract date, and contractor name.

[0351] Step 5:

[0352] The server creates an index based on the analyzed information and registers it in the contract information database. This database registration includes links to related image files.

[0353] Step 6:

[0354] When a user searches for contract information through their device, the emotion engine is activated to analyze the user's input. It uses voice and text data acquired from the device's microphone and keyboard to determine the user's emotional state.

[0355] Step 7:

[0356] The server receives the user's search query along with the emotional state recognized by the emotion engine. The search service then searches the contract database and generates search results that are adjusted according to the user's emotions.

[0357] Step 8:

[0358] The server sends the generated search results to the device. The device displays the results to the user and provides an interface tailored to their emotional state. For example, if the user shows signs of stress, the navigation is simplified.

[0359] Step 9:

[0360] The user clicks a link from their device and directly accesses the relevant contract document. The server immediately provides the corresponding image file, allowing the user to comfortably review the document content.

[0361] (Example 2)

[0362] 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".

[0363] In modern information management, contract documents are complex, and efficient management and retrieval are essential. Furthermore, reducing user stress and providing a more comfortable interface are also crucial challenges. However, conventional systems lack dynamic information presentation that considers user emotions, highlighting the need for innovation to further improve user convenience.

[0364] 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.

[0365] In this invention, the server includes character recognition means for extracting symbolic information from documents stored as image data, generation model means for analyzing contract-related data based on the symbolic information, and emotion analysis and adjustment means for analyzing the user's emotional state and adjusting the method of information presentation. This not only enables rapid and efficient management and retrieval of contract documents, but also allows for information provision that takes the user's emotions into consideration, thereby improving the user experience.

[0366] "Image data" refers to all data stored in digital format that includes visual information.

[0367] "Symbolic information" refers to information composed of letters, numbers, and other visual symbols.

[0368] "Character recognition means" refers to a technology or device that automatically reads symbolic information from image data.

[0369] "Generative modeling means" refers to an algorithm or system for analyzing data and generating specific patterns or information.

[0370] An "information recording device" refers to a system or device for storing data and retaining it so that it can be searched or referenced later.

[0371] "Information registration means" refers to a technology or device that provides the function of systematically registering structured information into an information recording device.

[0372] A "search request" refers to a request made by a user to obtain specific information.

[0373] "Connection information" refers to links or reference information that enable access to related data or documents.

[0374] "Emotional analysis and adjustment means" refers to a technology or system for evaluating a user's emotional state and changing the way information is presented based on that evaluation.

[0375] This invention combines several technical elements to realize a system that manages contract documents and provides user-responsive interaction.

[0376] The user accesses the business system via a terminal and uploads image data of contract documents to the server. The system extracts symbolic information from the image data using appropriate optical character recognition (OCR) means. For this process, an OCR engine such as Tesseract is used as the OCR software. This means that the image data is converted into a machine-readable format.

[0377] The server uses AI technologies, such as Transformers models, as a generative model to analyze the extracted symbolic information. Through this analysis, contract-related data is extracted from the contract document and registered in a contract database, which acts as an information recording device. This allows users to easily search and refer to contract information as needed.

[0378] Furthermore, the device sends user voice and text input to the server, which evaluates the user's emotional state using sentiment analysis and adjustment mechanisms. For example, by using the Google Cloud Natural Language API, it is possible to analyze emotions from the user's text and voice and adjust the presentation of information according to the user's emotions. This allows for the presentation of information to be made gentler or alert messages to be expressed in a milder tone if negative emotions are detected.

[0379] For example, when a user searches for a document using a contract identification code, the terminal analyzes the expression and tone on the server. If the server detects that the user is experiencing stress, it simplifies and presents the results. Furthermore, user experience is improved by displaying user-friendly navigation and messages.

[0380] Example of a prompt:

[0381] "Search for contract information using contract ID 12345, analyze user sentiment, and adjust the results accordingly."

[0382] Thus, the present invention improves operational efficiency and user experience by providing an emotionally sensitive interface and enabling efficient contract document management.

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The user accesses the business system using a terminal, selects image data of contract documents in a file selection window, and uploads it to the server. The input is the image file selected by the user, and the output is the saving of the file to a specific folder on the server. The terminal communicates with the server to process the file appropriately and starts data transfer.

[0386] Step 2:

[0387] The server performs optical character recognition (OCR) processing on the received image data. The input is the uploaded image data, and the output is text data. Specifically, the server applies an OCR engine to extract character information from the image. After processing, the extracted text data is temporarily stored and passed on to the next analysis process.

[0388] Step 3:

[0389] The server analyzes the extracted text data using a generative AI model. The input is the text data obtained in step 2, and the output is structured contract information. The model identifies, for example, dates, party names, and conditions related to the contract, and organizes them as contract information. The server generates this information and prepares it for registration in the database.

[0390] Step 4:

[0391] The server uses an indexing engine to register the obtained contract information in the contract database. The input is structured contract information, and it generates output indicating that registration is complete. The database organizes the information and indexes it so that it can respond quickly to subsequent search requests.

[0392] Step 5:

[0393] User actions and voice are captured by the terminal and sent to the server. The input here is the user's voice and text-based interface operations, and the output is the data sent to the server for analysis. The terminal then formats this data appropriately as sentiment data, enabling the subsequent sentiment analysis process.

[0394] Step 6:

[0395] The server uses an emotion analysis engine to determine the user's emotional state. Input is voice and text data sent by the user, and output is information about the user's emotions. Based on this information, the server adjusts the information presentation method to provide a more appropriate interface.

[0396] Step 7:

[0397] The server provides access to appropriate contract documents based on the user's sentiment and search queries. Inputs are the user's sentiment information and search queries, while output is tailored search results and information presentations. This allows users to obtain the information they need without stress.

[0398] (Application Example 2)

[0399] 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 will be referred to as the "terminal."

[0400] When managing contract documents, there is a lack of interfaces that take into account the user's emotional state, resulting in insufficient improvements in usability and the comfort of the contract process. Therefore, there is a need to improve the overall experience of contract procedures, including electronic payments, by enabling flexible information presentation that responds to the user's emotions.

[0401] 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.

[0402] In this invention, the server includes optical character recognition means for extracting character information from contract documents stored as image files, generative model means for analyzing contract-related information based on the character information, and emotion response means for analyzing the user's emotional state, dynamically adjusting the interface based on the corresponding emotional state, and adaptively providing information. This enables efficient management of contract documents and flexible information provision that takes into account the user's emotions.

[0403] An "image file" is a collection of visual information stored using electronic means, and it is a digital record of the contents of a contract document.

[0404] "Text information" refers to string data extracted from image files, specifically the text content included in contract documents.

[0405] "Optical character recognition means" refers to devices or software that include technology for detecting characters within an image and converting them into electronic text data.

[0406] A "generative modeling method" is a technology that analyzes contract-related information based on input data and generates structured related data.

[0407] "Index creation methods" refer to the procedures and technologies used to register extracted contract information in a database so that it can be efficiently managed and searched.

[0408] A "search provision method" is a technology that provides access links to relevant contract documents and information based on search queries from users.

[0409] "Means of reference" refers to a means of immediately accessing contract documents and verifying their contents.

[0410] "Emotional response methods" are technologies aimed at analyzing a user's emotional state and adjusting the interface or presenting information accordingly.

[0411] This system is designed to provide efficient management of contract documents and an interface that takes user emotions into consideration. First, the user acquires an image of the contract document using an electronic terminal and sends it to the server. The server extracts the text information from the image file using optical character recognition means and analyzes the extracted text information using a generation model means. Through this analysis process, information related to the contract is generated as structured data.

[0412] Next, the server registers the structured contract information in a database using an indexing mechanism. This database enables high-speed searching and provides links to relevant contract documents in response to user search queries.

[0413] The interface analyzes the user's emotional state through emotion-responsive mechanisms and dynamically adjusts how information is presented based on those emotions. The server recognizes emotions from the user's input text or voice data and adjusts the interface accordingly. For example, if the user is stressed, the interface will display information in a calmer tone.

[0414] The specific technologies used include the Google Cloud Vision API for optical character recognition, the Python NLTK library for natural language processing, and the IBM Watson Tone Analyzer API for sentiment analysis. The hardware can be a smartphone or a personal computer.

[0415] As a concrete example, when a user reviews the terms of service for a new electronic payment service, they can take a picture of the document with their device's camera and instantly view the text. If the user expresses concern, the system uses an AI model to display a message such as, "Please rest assured. We will provide you with support information regarding this agreement immediately." An example of a prompt message in this case would be, "The user is feeling anxious while reviewing the contract. Please sense the user's emotional state and generate a reassuring message."

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The user obtains the contract document as an image using an electronic device. The input is image data of the contract document, and this image is sent to the server as output. The user uses the device's camera function to photograph the entire document and uploads that data to the server.

[0419] Step 2:

[0420] The server performs optical character recognition (OCR) processing on the received image data. The input is image data sent by the user, and the output is extracted character information. As for OCR technology, the Google Cloud Vision API is used to analyze the characters in the image and convert them into text data.

[0421] Step 3:

[0422] The server analyzes the obtained character information using a generative model. The input is text data from OCR processing, and the output is structured contract information. Natural language processing techniques are used to extract contract requirements and conditions and convert them into structured data for registration in the database.

[0423] Step 4:

[0424] The server registers structured contract information in a database. The input is contract information structured by a generative model, and the output is a confirmation of successful registration to the contract database. An indexing mechanism is used to organize the data in a format that allows for rapid searching.

[0425] Step 5:

[0426] This system analyzes the user's emotional state and dynamically adjusts the interface. Input is user voice or text data, and output is adaptive information presentation corresponding to the emotion. The IBM Watson Tone Analyzer API is used for emotion analysis, and the server adjusts the interface tone and information content according to the recognized emotion.

[0427] Step 6:

[0428] The user enters a search query and retrieves information on relevant contract documents. The input is the user's search query, and the output is the relevant contract documents and their links. The search results are displayed on the terminal interface along with emotionally sensitive and gentle messages.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] [Third Embodiment]

[0433] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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".

[0445] In the system of the present invention, it is possible to efficiently manage contract documents stored as image files on a computer and make them easily accessible to users. A specific form for implementing this system is described below.

[0446] First, the user accesses the business system using a terminal and uploads image files of the contract documents to be managed to the server. The uploaded files are received by the server and saved to a specified folder.

[0447] Next, the server performs optical character recognition (OCR) on the saved image files. This extracts the text information contained in the contract documents and converts it into digital text. This text information is retained as text data for use in subsequent processing.

[0448] Subsequently, the server uses a generative model to analyze the extracted text information and identify important information related to the contract. Through this analysis, information such as the contract ID, contract date, contractor name, and contract details are extracted as structured data.

[0449] The server registers the obtained analysis results into a contract database for efficient management of contract information using an indexing mechanism. Links to related image files are also stored at this time, ensuring that the contract information is readily accessible.

[0450] Users can search for contract information via their terminal. Based on the contract ID and keywords entered by the user, the server sends a search query to the contract database and finds relevant documents. The server provides the found information to the user and returns search results, including links to image files.

[0451] Ultimately, users can directly access contract documents using the provided links. For example, if a user receives search results for "Lease Agreement for Property A," they can open the document with a single click. This process significantly reduces the effort required to search for and access contract information, thereby improving operational efficiency.

[0452] Thus, the system of the present invention reduces the workload of users by automating the contract document management and search processes.

[0453] The following describes the processing flow.

[0454] Step 1:

[0455] The user accesses the business system using a terminal and uploads image files of contract documents to the server. The terminal displays a file selection dialog and sends the file selected by the user to the server.

[0456] Step 2:

[0457] The server saves the received image files to the specified folder. This saving process is performed using a file management system.

[0458] Step 3:

[0459] The server performs optical character recognition (OCR) processing on the stored image files. Using OCR software, it extracts text information from the images and converts it into digital text format.

[0460] Step 4:

[0461] The server passes the character information extracted by OCR to the generative model. The generative model analyzes the contract document and identifies important information such as the contract ID, contract date, and contractor name.

[0462] Step 5:

[0463] The server indexes the analyzed contract information as structured data. This data is then registered in the contract information database.

[0464] Step 6:

[0465] The user enters a contract ID or keywords to search for specific contract information via their device. The device then sends the entered query to the server.

[0466] Step 7:

[0467] The server receives a search query from the user, searches the contract information database, and extracts relevant information. It then generates search results, including links to relevant contract documents.

[0468] Step 8:

[0469] The server sends the generated search results back to the terminal. The terminal displays the results in a list format for the user to review.

[0470] Step 9:

[0471] The user selects a link provided through their device and opens the relevant contract document in their browser. The server provides the requested image file, allowing the user to view it directly.

[0472] (Example 1)

[0473] 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."

[0474] Conventional contract document management systems have faced the problem of requiring considerable time and effort to extract textual information and identify important details when digitizing and managing paper-based contract documents. Furthermore, the limited search functionality makes it difficult for users to quickly find the contract documents they need. There is a need to solve these problems and improve the efficiency of contract document management and searching.

[0475] 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.

[0476] In this invention, the server includes character recognition means for extracting character data from contract documents stored as image files, model means for identifying important information using the character data, and registration means for registering structured data containing the identified information in a management infrastructure. This enables the rapid and easy digitization of paper-based contract documents, as well as efficient searching and management.

[0477] An "image file" refers to a file that stores visual information in digital format, and scanned paper documents are an example of this.

[0478] "Character data" refers to digital data extracted from visual characters, in a format that can be processed and searched by a computer.

[0479] "Character recognition means" refers to technologies and software for extracting character data from image files, and optical character recognition (OCR) technology is included in this.

[0480] "Modeling techniques" refer to analytical techniques and programs used to identify important information from text data extracted using machine learning or artificial intelligence.

[0481] "Structured data" refers to data that has been organized and formalized so that it can be efficiently managed and searched in databases and other systems.

[0482] "Management infrastructure" refers to systems and databases for storing and managing structured data, enabling secure data management and rapid access.

[0483] "Registration means" refers to functions or mechanisms for adding processed information to a management infrastructure and storing it.

[0484] "Information provision means" refers to the technologies and methods used to present appropriate documents and related information in response to search requests from users.

[0485] This invention relates to an image recognition and data processing system for streamlining the management of contract documents. This system mainly consists of a server, terminals, and users.

[0486] First, the user uploads image files of the contract documents to the server using their device. These devices typically include personal computers or tablets, and they access the business system via a browser or dedicated application.

[0487] The server processes the data using the following software and technologies. The server has the capability to receive uploaded image files and store them in a secure directory. Next, the server extracts text data from the image files using Optical Character Recognition (OCR) technology. It is recommended to use an OCR engine such as Tesseract for this process.

[0488] The extracted text data is then input into a generative AI model for analysis. This analysis identifies important information related to the contract. A sophisticated natural language processing technique such as GPT-4 is suitable for this model. The analyzed information is organized as structured data and registered in a database managed by the server. An RDBMS such as MySQL can be used for this database.

[0489] Users can search for contract information from their terminal using contract identification numbers and related keywords. Based on the search query, the server quickly accesses the database and provides links to relevant documents. At this time, by entering a prompt in the format of "Please tell me the contract information and contract name related to property B," the necessary information can be obtained efficiently.

[0490] This system's configuration and technology automate the management and retrieval of contract documents, allowing users to quickly digitize paper contracts and access them instantly. For example, a real estate management company can centrally manage multiple contracts related to properties, significantly improving operational efficiency.

[0491] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0492] Step 1:

[0493] The user uses a terminal to select an image file of the contract document. They drag and drop the selected image file onto the system's upload interface and press the upload button. The image file is sent to the server as input. The server then receives the file using a data transfer protocol and saves it to the specified directory. This process results in an output indicating that the image file has been saved in storage.

[0494] Step 2:

[0495] The server initiates optical character recognition (OCR) processing on the saved image file. The saved image file is provided as input. Specifically, software such as Tesseract OCR is used to analyze the characters in the image and convert the character data into a digital format. Through this process, the characters in the image are extracted as text data and output as intermediate data for use in subsequent processes.

[0496] Step 3:

[0497] The server inputs the character data extracted by OCR into a generative AI model. The generative AI model (e.g., GPT-4) analyzes the contract details and identifies important information such as the contract ID, contractor name, and contract date. The server organizes the analysis results as structured data in JSON format and outputs the analyzed information. This is treated as preparation for database registration.

[0498] Step 4:

[0499] The server registers the analyzed structured data as contract information in the management infrastructure. The generated structured data is provided to the server as input. A query for data registration is executed against the database system (e.g., MySQL), and the relevant information is stored in the database. This process outputs that the contract information is securely stored in the database and is searchable at a later date.

[0500] Step 5:

[0501] The user accesses an interface via their terminal to search for contract information. A search query (e.g., "Show contract for property B") is entered as a prompt and sent to the server. The server receives the search criteria and queries the contract database to find the relevant document information. This process outputs links to the corresponding contract documents and the relevant information.

[0502] Step 6:

[0503] The user views the search results provided by the server and clicks to open the required document by clicking the link. The link is provided as input, and a browser or PDF reader is launched on the user's device. This allows the user to view the contents of the contract document on the screen. Upon completion of this action, an output indicating that the review of the contract contents is complete is displayed.

[0504] (Application Example 1)

[0505] 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."

[0506] To efficiently manage contract documents, digitization of documents, automated information extraction, and rapid searching are required. However, existing systems have limitations in image file processing and insufficient search flexibility, resulting in time-consuming and labor-intensive management and retrieval of contract information. Furthermore, management on mobile devices was not considered, making it difficult to access contract information quickly while away from the office or in the field.

[0507] 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.

[0508] In this invention, the server includes optical character recognition means for extracting character information from contract documents stored as image files; generation model means for analyzing contract-related information based on the character information; index creation means for registering contract information in a contract database using the analyzed information; receiving means for inputting images to a mobile information terminal; means for converting the images received by the mobile information terminal into character data via the optical character recognition means and automatically extracting contract information using the generation model means; and processing means for efficiently managing and searching the contract information within the mobile information terminal. This enables digitization of contract documents, automatic analysis, efficient information management, and immediate retrieval.

[0509] An "image file" is a file format of electronic data that visually represents information, and is used to digitize contract documents from paper format.

[0510] A "contract document" is a written record of an agreement between parties, detailing the terms and conditions of a transaction or service.

[0511] "Textual information" refers to the text data contained within a document, and is the information necessary to understand the contents of a contract.

[0512] "Optical character recognition means" refers to a device or software that mechanically reads characters contained in an image file and extracts them as text data.

[0513] The "generative model means" is AI-based software for analyzing important information related to a contract based on extracted textual information.

[0514] An "indexing tool" is a device or software that organizes analyzed contract information into a database for efficient searching and management.

[0515] A "search and provision means" is a system that searches a contract database based on user input and provides access links to relevant documents.

[0516] "Receiving means" refers to a device or software that has the function of capturing images on a mobile information terminal and receiving data from that terminal.

[0517] "Processing means" refers to a device or software that efficiently manages the acquired contract information and processes and maintains it so that it can be quickly accessed when needed.

[0518] A "personal information terminal" is an electronic device that a user can carry with them and that is capable of taking pictures and managing and viewing data.

[0519] The system that realizes this invention provides efficient management and rapid access to contract documents. At the core of the system are a personal digital assistant (PDCA) terminal and a server. The PDCA terminal is equipped with a receiving mechanism for users to photograph or upload contract documents. This terminal takes the form of a smartphone or tablet and has a camera function and applications installed.

[0520] Image files received from the terminal are transferred to the server. The server extracts text information from the image files using optical character recognition (OCR) means. For optical character recognition, OCR libraries such as Tesseract are used. The server analyzes the extracted text information using a generative AI model to identify important contract information. The generated information is organized as structured data such as contract ID and contract date, and registered in the contract database using an indexing means. This enables efficient management of contract information.

[0521] Users access the server via their mobile devices to search and refer to contract information. A search engine exists on the server, supporting searches based on contract IDs and keywords. This system displays search results instantly, enabling quick access to relevant documents. For example, if a user wants to check information on a "residential lease agreement," they can access the relevant contract simply by entering the contract holder's name or contract ID.

[0522] When utilizing the generative AI model, prompts are generated to more precisely analyze the contract content. An example of such a prompt is a specific instruction such as, "Extract and structure important contract information from the following text: ○○ (OCR result text)." This mechanism automatically organizes the vast amount of information contained in the contract, providing users with an environment where they can quickly access the information they need.

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] Users take photos of contract documents or select existing image files using their mobile devices and upload them to the server via the application. The input is an image file, and the output is a file sent to the server. This allows users to register digital versions of contract documents in the system.

[0526] Step 2:

[0527] The server passes the received image file to the optical character recognition (OCR) system and begins processing. The input is an image file, and character information is extracted using OCR technology. The output is text data. Using an OCR library (e.g., Tesseract), the server converts the text in the image into a machine-readable format.

[0528] Step 3:

[0529] The server passes the extracted text information to a generating AI model, which generates prompt sentences and performs analysis. The input is text data obtained by OCR, and the generating AI model is used to identify important information about the contract (contract ID, contractor, contract date, etc.). The output is structured contract information data. This analysis makes the contract content clearer and more organized.

[0530] Step 4:

[0531] The server uses an indexing mechanism to register structured contract information in the contract database. The input is structured contract information data, and registration in the database enables efficient searching. The output is the registered information organized in the database. This makes contract information easier to manage.

[0532] Step 5:

[0533] The user enters a search query using a mobile device. The input is a query based on the user's requested contract information (contract ID and keywords). The device sends this query to the server.

[0534] Step 6:

[0535] The server uses search mechanisms to search the contract database and identify links to relevant contract document information. The input is the user's search query, and the results of the database search are returned to the user as output. This allows the user to quickly access the necessary contract information.

[0536] Step 7:

[0537] Users access contract documents using links provided in the search results. The input is the link within the search results, and the output is the screen displaying the contract document. This allows users to quickly find the necessary contracts.

[0538] 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.

[0539] In the system of the present invention, a more human-friendly operating environment is realized by combining an emotion engine to manage contract documents and provide user interaction that responds to emotions. A specific form for implementing this system is described below.

[0540] First, the user accesses the business system using a terminal and uploads an image file of the contract document to the server. The uploaded file is received by the server and saved to the appropriate folder. The server performs optical character recognition (OCR) processing on the saved image file to extract text information. This text information is analyzed by a generative model, and the contract information is extracted as structured data.

[0541] Next, the analyzed contract information is registered in a contract database using an indexing mechanism. This database is used to quickly respond to user search queries and provide information on relevant contract documents.

[0542] The emotion engine recognizes the user's emotional state by analyzing their voice and text input. Based on this state, it can dynamically adjust the display of search results, notifications related to contract documents, and alerts. For example, if the user is showing negative emotions, the system will automatically adjust the information to be displayed in a calmer tone.

[0543] As a concrete example, when a user searches for contract information using their contract ID, the terminal analyzes the user's input and tone of voice using an emotion engine. If the user is experiencing stress, the server takes measures such as simplifying the navigation of search results or displaying a gentler alert message.

[0544] Thus, by incorporating an emotion engine, the present invention provides an interface that takes user emotions into consideration, making the management and retrieval of contract documents more comfortable. This not only improves operational efficiency but also enhances the user experience.

[0545] The following describes the processing flow.

[0546] Step 1:

[0547] The user logs into the business system using a terminal and uploads image files of contract documents to the server. The terminal opens a file selection dialog and sends the files selected by the user to the server using the specified protocol.

[0548] Step 2:

[0549] The server saves the received image files to a designated directory. This saving process is automated, and the file name and save location are uniquely determined by the system.

[0550] Step 3:

[0551] The server initiates optical character recognition (OCR) processing on the saved image files. The OCR engine analyzes the image data and extracts the character information contained within it as text data.

[0552] Step 4:

[0553] The server passes the text data extracted by OCR to the generation model. The generation model analyzes the text data and identifies key fields such as contract ID, contract date, and contractor name.

[0554] Step 5:

[0555] The server creates an index based on the analyzed information and registers it in the contract information database. This database registration includes links to related image files.

[0556] Step 6:

[0557] When a user searches for contract information through their device, the emotion engine is activated to analyze the user's input. It uses voice and text data acquired from the device's microphone and keyboard to determine the user's emotional state.

[0558] Step 7:

[0559] The server receives the user's search query along with the emotional state recognized by the emotion engine. The search service then searches the contract database and generates search results that are adjusted according to the user's emotions.

[0560] Step 8:

[0561] The server sends the generated search results to the device. The device displays the results to the user and provides an interface tailored to their emotional state. For example, if the user shows signs of stress, the navigation is simplified.

[0562] Step 9:

[0563] The user clicks a link from their device and directly accesses the relevant contract document. The server immediately provides the corresponding image file, allowing the user to comfortably review the document content.

[0564] (Example 2)

[0565] 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."

[0566] In modern information management, contract documents are complex, and efficient management and retrieval are essential. Furthermore, reducing user stress and providing a more comfortable interface are also crucial challenges. However, conventional systems lack dynamic information presentation that considers user emotions, highlighting the need for innovation to further improve user convenience.

[0567] 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.

[0568] In this invention, the server includes character recognition means for extracting symbolic information from documents stored as image data, generation model means for analyzing contract-related data based on the symbolic information, and emotion analysis and adjustment means for analyzing the user's emotional state and adjusting the method of information presentation. This not only enables rapid and efficient management and retrieval of contract documents, but also allows for information provision that takes the user's emotions into consideration, thereby improving the user experience.

[0569] "Image data" refers to all data stored in digital format that includes visual information.

[0570] "Symbolic information" refers to information composed of letters, numbers, and other visual symbols.

[0571] "Character recognition means" refers to a technology or device that automatically reads symbolic information from image data.

[0572] "Generative modeling means" refers to an algorithm or system for analyzing data and generating specific patterns or information.

[0573] An "information recording device" refers to a system or device for storing data and retaining it so that it can be searched or referenced later.

[0574] "Information registration means" refers to a technology or device that provides the function of systematically registering structured information into an information recording device.

[0575] A "search request" refers to a request made by a user to obtain specific information.

[0576] "Connection information" refers to links or reference information that enable access to related data or documents.

[0577] "Emotional analysis and adjustment means" refers to a technology or system for evaluating a user's emotional state and changing the way information is presented based on that evaluation.

[0578] This invention combines several technical elements to realize a system that manages contract documents and provides user-responsive interaction.

[0579] The user accesses the business system via a terminal and uploads image data of contract documents to the server. The system extracts symbolic information from the image data using appropriate optical character recognition (OCR) means. For this process, an OCR engine such as Tesseract is used as the OCR software. This means that the image data is converted into a machine-readable format.

[0580] The server uses AI technologies, such as Transformers models, as a generative model to analyze the extracted symbolic information. Through this analysis, contract-related data is extracted from the contract document and registered in a contract database, which acts as an information recording device. This allows users to easily search and refer to contract information as needed.

[0581] Furthermore, the device sends user voice and text input to the server, which evaluates the user's emotional state using sentiment analysis and adjustment mechanisms. For example, by using the Google Cloud Natural Language API, it is possible to analyze emotions from the user's text and voice and adjust the presentation of information according to the user's emotions. This allows for the presentation of information to be made gentler or alert messages to be expressed in a milder tone if negative emotions are detected.

[0582] For example, when a user searches for a document using a contract identification code, the terminal analyzes the expression and tone on the server. If the server detects that the user is experiencing stress, it simplifies and presents the results. Furthermore, user experience is improved by displaying user-friendly navigation and messages.

[0583] Example of a prompt:

[0584] "Search for contract information using contract ID 12345, analyze user sentiment, and adjust the results accordingly."

[0585] Thus, the present invention improves operational efficiency and user experience by providing an emotionally sensitive interface and enabling efficient contract document management.

[0586] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0587] Step 1:

[0588] The user accesses the business system using a terminal, selects image data of contract documents in a file selection window, and uploads it to the server. The input is the image file selected by the user, and the output is the saving of the file to a specific folder on the server. The terminal communicates with the server to process the file appropriately and starts data transfer.

[0589] Step 2:

[0590] The server performs optical character recognition (OCR) processing on the received image data. The input is the uploaded image data, and the output is text data. Specifically, the server applies an OCR engine to extract character information from the image. After processing, the extracted text data is temporarily stored and passed on to the next analysis process.

[0591] Step 3:

[0592] The server analyzes the extracted text data using a generative AI model. The input is the text data obtained in step 2, and the output is structured contract information. The model identifies, for example, dates, party names, and conditions related to the contract, and organizes them as contract information. The server generates this information and prepares it for registration in the database.

[0593] Step 4:

[0594] The server uses an indexing engine to register the obtained contract information in the contract database. The input is structured contract information, and it generates output indicating that registration is complete. The database organizes the information and indexes it so that it can respond quickly to subsequent search requests.

[0595] Step 5:

[0596] User actions and voice are captured by the terminal and sent to the server. The input here is the user's voice and text-based interface operations, and the output is the data sent to the server for analysis. The terminal then formats this data appropriately as sentiment data, enabling the subsequent sentiment analysis process.

[0597] Step 6:

[0598] The server uses an emotion analysis engine to determine the user's emotional state. Input is voice and text data sent by the user, and output is information about the user's emotions. Based on this information, the server adjusts the information presentation method to provide a more appropriate interface.

[0599] Step 7:

[0600] The server provides access to appropriate contract documents based on the user's sentiment and search queries. Inputs are the user's sentiment information and search queries, while output is tailored search results and information presentations. This allows users to obtain the information they need without stress.

[0601] (Application Example 2)

[0602] 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."

[0603] When managing contract documents, there is a lack of interfaces that take into account the user's emotional state, resulting in insufficient improvements in usability and the comfort of the contract process. Therefore, there is a need to improve the overall experience of contract procedures, including electronic payments, by enabling flexible information presentation that responds to the user's emotions.

[0604] 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.

[0605] In this invention, the server includes optical character recognition means for extracting character information from contract documents stored as image files, generative model means for analyzing contract-related information based on the character information, and emotion response means for analyzing the user's emotional state, dynamically adjusting the interface based on the corresponding emotional state, and adaptively providing information. This enables efficient management of contract documents and flexible information provision that takes into account the user's emotions.

[0606] An "image file" is a collection of visual information stored using electronic means, and it is a digital record of the contents of a contract document.

[0607] "Text information" refers to string data extracted from image files, specifically the text content included in contract documents.

[0608] "Optical character recognition means" refers to devices or software that include technology for detecting characters within an image and converting them into electronic text data.

[0609] A "generative modeling method" is a technology that analyzes contract-related information based on input data and generates structured related data.

[0610] "Index creation methods" refer to the procedures and technologies used to register extracted contract information in a database so that it can be efficiently managed and searched.

[0611] A "search provision method" is a technology that provides access links to relevant contract documents and information based on search queries from users.

[0612] "Means of reference" refers to a means of immediately accessing contract documents and verifying their contents.

[0613] "Emotional response methods" are technologies aimed at analyzing a user's emotional state and adjusting the interface or presenting information accordingly.

[0614] This system is designed to provide efficient management of contract documents and an interface that takes user emotions into consideration. First, the user acquires an image of the contract document using an electronic terminal and sends it to the server. The server extracts the text information from the image file using optical character recognition means and analyzes the extracted text information using a generation model means. Through this analysis process, information related to the contract is generated as structured data.

[0615] Next, the server registers the structured contract information in a database using an indexing mechanism. This database enables high-speed searching and provides links to relevant contract documents in response to user search queries.

[0616] The interface analyzes the user's emotional state through emotion-responsive mechanisms and dynamically adjusts how information is presented based on those emotions. The server recognizes emotions from the user's input text or voice data and adjusts the interface accordingly. For example, if the user is stressed, the interface will display information in a calmer tone.

[0617] The specific technologies used include the Google Cloud Vision API for optical character recognition, the Python NLTK library for natural language processing, and the IBM Watson Tone Analyzer API for sentiment analysis. The hardware can be a smartphone or a personal computer.

[0618] As a concrete example, when a user reviews the terms of service for a new electronic payment service, they can take a picture of the document with their device's camera and instantly view the text. If the user expresses concern, the system uses an AI model to display a message such as, "Please rest assured. We will provide you with support information regarding this agreement immediately." An example of a prompt message in this case would be, "The user is feeling anxious while reviewing the contract. Please sense the user's emotional state and generate a reassuring message."

[0619] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0620] Step 1:

[0621] The user obtains the contract document as an image using an electronic device. The input is image data of the contract document, and this image is sent to the server as output. The user uses the device's camera function to photograph the entire document and uploads that data to the server.

[0622] Step 2:

[0623] The server performs optical character recognition (OCR) processing on the received image data. The input is image data sent by the user, and the output is extracted character information. As for OCR technology, the Google Cloud Vision API is used to analyze the characters in the image and convert them into text data.

[0624] Step 3:

[0625] The server analyzes the obtained character information using a generative model. The input is text data from OCR processing, and the output is structured contract information. Natural language processing techniques are used to extract contract requirements and conditions and convert them into structured data for registration in the database.

[0626] Step 4:

[0627] The server registers structured contract information in a database. The input is contract information structured by a generative model, and the output is a confirmation of successful registration to the contract database. An indexing mechanism is used to organize the data in a format that allows for rapid searching.

[0628] Step 5:

[0629] This system analyzes the user's emotional state and dynamically adjusts the interface. Input is user voice or text data, and output is adaptive information presentation corresponding to the emotion. The IBM Watson Tone Analyzer API is used for emotion analysis, and the server adjusts the interface tone and information content according to the recognized emotion.

[0630] Step 6:

[0631] The user enters a search query and retrieves information on relevant contract documents. The input is the user's search query, and the output is the relevant contract documents and their links. The search results are displayed on the terminal interface along with emotionally sensitive and gentle messages.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] [Fourth Embodiment]

[0636] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0637] 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.

[0638] 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).

[0639] 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.

[0640] 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.

[0641] 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).

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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".

[0649] In the system of the present invention, it is possible to efficiently manage contract documents stored as image files on a computer and make them easily accessible to users. A specific form for implementing this system is described below.

[0650] First, the user accesses the business system using a terminal and uploads image files of the contract documents to be managed to the server. The uploaded files are received by the server and saved to a specified folder.

[0651] Next, the server performs optical character recognition (OCR) on the saved image files. This extracts the text information contained in the contract documents and converts it into digital text. This text information is retained as text data for use in subsequent processing.

[0652] Subsequently, the server uses a generative model to analyze the extracted text information and identify important information related to the contract. Through this analysis, information such as the contract ID, contract date, contractor name, and contract details are extracted as structured data.

[0653] The server registers the obtained analysis results into a contract database for efficient management of contract information using an indexing mechanism. Links to related image files are also stored at this time, ensuring that the contract information is readily accessible.

[0654] Users can search for contract information via their terminal. Based on the contract ID and keywords entered by the user, the server sends a search query to the contract database and finds relevant documents. The server provides the found information to the user and returns search results, including links to image files.

[0655] Ultimately, users can directly access contract documents using the provided links. For example, if a user receives search results for "Lease Agreement for Property A," they can open the document with a single click. This process significantly reduces the effort required to search for and access contract information, thereby improving operational efficiency.

[0656] Thus, the system of the present invention reduces the workload of users by automating the contract document management and search processes.

[0657] The following describes the processing flow.

[0658] Step 1:

[0659] The user accesses the business system using a terminal and uploads image files of contract documents to the server. The terminal displays a file selection dialog and sends the file selected by the user to the server.

[0660] Step 2:

[0661] The server saves the received image files to the specified folder. This saving process is performed using a file management system.

[0662] Step 3:

[0663] The server performs optical character recognition (OCR) processing on the stored image files. Using OCR software, it extracts text information from the images and converts it into digital text format.

[0664] Step 4:

[0665] The server passes the character information extracted by OCR to the generative model. The generative model analyzes the contract document and identifies important information such as the contract ID, contract date, and contractor name.

[0666] Step 5:

[0667] The server indexes the analyzed contract information as structured data. This data is then registered in the contract information database.

[0668] Step 6:

[0669] The user enters a contract ID or keywords to search for specific contract information via their device. The device then sends the entered query to the server.

[0670] Step 7:

[0671] The server receives a search query from the user, searches the contract information database, and extracts relevant information. It then generates search results, including links to relevant contract documents.

[0672] Step 8:

[0673] The server sends the generated search results back to the terminal. The terminal displays the results in a list format for the user to review.

[0674] Step 9:

[0675] The user selects a link provided through their device and opens the relevant contract document in their browser. The server provides the requested image file, allowing the user to view it directly.

[0676] (Example 1)

[0677] 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".

[0678] Conventional contract document management systems have faced the problem of requiring considerable time and effort to extract textual information and identify important details when digitizing and managing paper-based contract documents. Furthermore, the limited search functionality makes it difficult for users to quickly find the contract documents they need. There is a need to solve these problems and improve the efficiency of contract document management and searching.

[0679] 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.

[0680] In this invention, the server includes character recognition means for extracting character data from contract documents stored as image files, model means for identifying important information using the character data, and registration means for registering structured data containing the identified information in a management infrastructure. This enables the rapid and easy digitization of paper-based contract documents, as well as efficient searching and management.

[0681] An "image file" refers to a file that stores visual information in digital format, and scanned paper documents are an example of this.

[0682] "Character data" refers to digital data extracted from visual characters, in a format that can be processed and searched by a computer.

[0683] "Character recognition means" refers to technologies and software for extracting character data from image files, and optical character recognition (OCR) technology is included in this.

[0684] "Modeling techniques" refer to analytical techniques and programs used to identify important information from text data extracted using machine learning or artificial intelligence.

[0685] "Structured data" refers to data that has been organized and formalized so that it can be efficiently managed and searched in databases and other systems.

[0686] "Management infrastructure" refers to systems and databases for storing and managing structured data, enabling secure data management and rapid access.

[0687] "Registration means" refers to functions or mechanisms for adding processed information to a management infrastructure and storing it.

[0688] "Information provision means" refers to the technologies and methods used to present appropriate documents and related information in response to search requests from users.

[0689] This invention relates to an image recognition and data processing system for streamlining the management of contract documents. This system mainly consists of a server, terminals, and users.

[0690] First, the user uploads image files of the contract documents to the server using their device. These devices typically include personal computers or tablets, and they access the business system via a browser or dedicated application.

[0691] The server processes the data using the following software and technologies. The server has the capability to receive uploaded image files and store them in a secure directory. Next, the server extracts text data from the image files using Optical Character Recognition (OCR) technology. It is recommended to use an OCR engine such as Tesseract for this process.

[0692] The extracted text data is then input into a generative AI model for analysis. This analysis identifies important information related to the contract. A sophisticated natural language processing technique such as GPT-4 is suitable for this model. The analyzed information is organized as structured data and registered in a database managed by the server. An RDBMS such as MySQL can be used for this database.

[0693] Users can search for contract information from their terminal using contract identification numbers and related keywords. Based on the search query, the server quickly accesses the database and provides links to relevant documents. At this time, by entering a prompt in the format of "Please tell me the contract information and contract name related to property B," the necessary information can be obtained efficiently.

[0694] This system's configuration and technology automate the management and retrieval of contract documents, allowing users to quickly digitize paper contracts and access them instantly. For example, a real estate management company can centrally manage multiple contracts related to properties, significantly improving operational efficiency.

[0695] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0696] Step 1:

[0697] The user uses a terminal to select an image file of the contract document. They drag and drop the selected image file onto the system's upload interface and press the upload button. The image file is sent to the server as input. The server then receives the file using a data transfer protocol and saves it to the specified directory. This process results in an output indicating that the image file has been saved in storage.

[0698] Step 2:

[0699] The server initiates optical character recognition (OCR) processing on the saved image file. The saved image file is provided as input. Specifically, software such as Tesseract OCR is used to analyze the characters in the image and convert the character data into a digital format. Through this process, the characters in the image are extracted as text data and output as intermediate data for use in subsequent processes.

[0700] Step 3:

[0701] The server inputs the character data extracted by OCR into a generative AI model. The generative AI model (e.g., GPT-4) analyzes the contract details and identifies important information such as the contract ID, contractor name, and contract date. The server organizes the analysis results as structured data in JSON format and outputs the analyzed information. This is treated as preparation for database registration.

[0702] Step 4:

[0703] The server registers the analyzed structured data as contract information in the management infrastructure. The generated structured data is provided to the server as input. A query for data registration is executed against the database system (e.g., MySQL), and the relevant information is stored in the database. This process outputs that the contract information is securely stored in the database and is searchable at a later date.

[0704] Step 5:

[0705] The user accesses an interface via their terminal to search for contract information. A search query (e.g., "Show contract for property B") is entered as a prompt and sent to the server. The server receives the search criteria and queries the contract database to find the relevant document information. This process outputs links to the corresponding contract documents and the relevant information.

[0706] Step 6:

[0707] The user views the search results provided by the server and clicks to open the required document by clicking the link. The link is provided as input, and a browser or PDF reader is launched on the user's device. This allows the user to view the contents of the contract document on the screen. Upon completion of this action, an output indicating that the review of the contract contents is complete is displayed.

[0708] (Application Example 1)

[0709] 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".

[0710] To efficiently manage contract documents, digitization of documents, automated information extraction, and rapid searching are required. However, existing systems have limitations in image file processing and insufficient search flexibility, resulting in time-consuming and labor-intensive management and retrieval of contract information. Furthermore, management on mobile devices was not considered, making it difficult to access contract information quickly while away from the office or in the field.

[0711] 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.

[0712] In this invention, the server includes optical character recognition means for extracting character information from contract documents stored as image files; generation model means for analyzing contract-related information based on the character information; index creation means for registering contract information in a contract database using the analyzed information; receiving means for inputting images to a mobile information terminal; means for converting the images received by the mobile information terminal into character data via the optical character recognition means and automatically extracting contract information using the generation model means; and processing means for efficiently managing and searching the contract information within the mobile information terminal. This enables digitization of contract documents, automatic analysis, efficient information management, and immediate retrieval.

[0713] An "image file" is a file format of electronic data that visually represents information, and is used to digitize contract documents from paper format.

[0714] A "contract document" is a written record of an agreement between parties, detailing the terms and conditions of a transaction or service.

[0715] "Textual information" refers to the text data contained within a document, and is the information necessary to understand the contents of a contract.

[0716] "Optical character recognition means" refers to a device or software that mechanically reads characters contained in an image file and extracts them as text data.

[0717] The "generative model means" is AI-based software for analyzing important information related to a contract based on extracted textual information.

[0718] An "indexing tool" is a device or software that organizes analyzed contract information into a database for efficient searching and management.

[0719] A "search and provision means" is a system that searches a contract database based on user input and provides access links to relevant documents.

[0720] "Receiving means" refers to a device or software that has the function of capturing images on a mobile information terminal and receiving data from that terminal.

[0721] "Processing means" refers to a device or software that efficiently manages the acquired contract information and processes and maintains it so that it can be quickly accessed when needed.

[0722] A "personal information terminal" is an electronic device that a user can carry with them and that is capable of taking pictures and managing and viewing data.

[0723] The system that realizes this invention provides efficient management and rapid access to contract documents. At the core of the system are a personal digital assistant (PDCA) terminal and a server. The PDCA terminal is equipped with a receiving mechanism for users to photograph or upload contract documents. This terminal takes the form of a smartphone or tablet and has a camera function and applications installed.

[0724] Image files received from the terminal are transferred to the server. The server extracts text information from the image files using optical character recognition (OCR) means. For optical character recognition, OCR libraries such as Tesseract are used. The server analyzes the extracted text information using a generative AI model to identify important contract information. The generated information is organized as structured data such as contract ID and contract date, and registered in the contract database using an indexing means. This enables efficient management of contract information.

[0725] Users access the server via their mobile devices to search and refer to contract information. A search engine exists on the server, supporting searches based on contract IDs and keywords. This system displays search results instantly, enabling quick access to relevant documents. For example, if a user wants to check information on a "residential lease agreement," they can access the relevant contract simply by entering the contract holder's name or contract ID.

[0726] When utilizing the generative AI model, prompts are generated to more precisely analyze the contract content. An example of such a prompt is a specific instruction such as, "Extract and structure important contract information from the following text: ○○ (OCR result text)." This mechanism automatically organizes the vast amount of information contained in the contract, providing users with an environment where they can quickly access the information they need.

[0727] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0728] Step 1:

[0729] Users take photos of contract documents or select existing image files using their mobile devices and upload them to the server via the application. The input is an image file, and the output is a file sent to the server. This allows users to register digital versions of contract documents in the system.

[0730] Step 2:

[0731] The server passes the received image file to the optical character recognition (OCR) system and begins processing. The input is an image file, and character information is extracted using OCR technology. The output is text data. Using an OCR library (e.g., Tesseract), the server converts the text in the image into a machine-readable format.

[0732] Step 3:

[0733] The server passes the extracted text information to a generating AI model, which generates prompt sentences and performs analysis. The input is text data obtained by OCR, and the generating AI model is used to identify important information about the contract (contract ID, contractor, contract date, etc.). The output is structured contract information data. This analysis makes the contract content clearer and more organized.

[0734] Step 4:

[0735] The server uses an indexing mechanism to register structured contract information in the contract database. The input is structured contract information data, and registration in the database enables efficient searching. The output is the registered information organized in the database. This makes contract information easier to manage.

[0736] Step 5:

[0737] The user enters a search query using a mobile device. The input is a query based on the user's requested contract information (contract ID and keywords). The device sends this query to the server.

[0738] Step 6:

[0739] The server uses search mechanisms to search the contract database and identify links to relevant contract document information. The input is the user's search query, and the results of the database search are returned to the user as output. This allows the user to quickly access the necessary contract information.

[0740] Step 7:

[0741] Users access contract documents using links provided in the search results. The input is the link within the search results, and the output is the screen displaying the contract document. This allows users to quickly find the necessary contracts.

[0742] 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.

[0743] In the system of the present invention, a more human-friendly operating environment is realized by combining an emotion engine to manage contract documents and provide user interaction that responds to emotions. A specific form for implementing this system is described below.

[0744] First, the user accesses the business system using a terminal and uploads an image file of the contract document to the server. The uploaded file is received by the server and saved to the appropriate folder. The server performs optical character recognition (OCR) processing on the saved image file to extract text information. This text information is analyzed by a generative model, and the contract information is extracted as structured data.

[0745] Next, the analyzed contract information is registered in a contract database using an indexing mechanism. This database is used to quickly respond to user search queries and provide information on relevant contract documents.

[0746] The emotion engine recognizes the user's emotional state by analyzing their voice and text input. Based on this state, it can dynamically adjust the display of search results, notifications related to contract documents, and alerts. For example, if the user is showing negative emotions, the system will automatically adjust the information to be displayed in a calmer tone.

[0747] As a concrete example, when a user searches for contract information using their contract ID, the terminal analyzes the user's input and tone of voice using an emotion engine. If the user is experiencing stress, the server takes measures such as simplifying the navigation of search results or displaying a gentler alert message.

[0748] Thus, by incorporating an emotion engine, the present invention provides an interface that takes user emotions into consideration, making the management and retrieval of contract documents more comfortable. This not only improves operational efficiency but also enhances the user experience.

[0749] The following describes the processing flow.

[0750] Step 1:

[0751] The user logs into the business system using a terminal and uploads image files of contract documents to the server. The terminal opens a file selection dialog and sends the files selected by the user to the server using the specified protocol.

[0752] Step 2:

[0753] The server saves the received image files to a designated directory. This saving process is automated, and the file name and save location are uniquely determined by the system.

[0754] Step 3:

[0755] The server initiates optical character recognition (OCR) processing on the saved image files. The OCR engine analyzes the image data and extracts the character information contained within it as text data.

[0756] Step 4:

[0757] The server passes the text data extracted by OCR to the generation model. The generation model analyzes the text data and identifies key fields such as contract ID, contract date, and contractor name.

[0758] Step 5:

[0759] The server creates an index based on the analyzed information and registers it in the contract information database. This database registration includes links to related image files.

[0760] Step 6:

[0761] When a user searches for contract information through their device, the emotion engine is activated to analyze the user's input. It uses voice and text data acquired from the device's microphone and keyboard to determine the user's emotional state.

[0762] Step 7:

[0763] The server receives the user's search query along with the emotional state recognized by the emotion engine. The search service then searches the contract database and generates search results that are adjusted according to the user's emotions.

[0764] Step 8:

[0765] The server sends the generated search results to the device. The device displays the results to the user and provides an interface tailored to their emotional state. For example, if the user shows signs of stress, the navigation is simplified.

[0766] Step 9:

[0767] The user clicks a link from their device and directly accesses the relevant contract document. The server immediately provides the corresponding image file, allowing the user to comfortably review the document content.

[0768] (Example 2)

[0769] 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".

[0770] In modern information management, contract documents are complex, and efficient management and retrieval are essential. Furthermore, reducing user stress and providing a more comfortable interface are also crucial challenges. However, conventional systems lack dynamic information presentation that considers user emotions, highlighting the need for innovation to further improve user convenience.

[0771] 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.

[0772] In this invention, the server includes character recognition means for extracting symbolic information from documents stored as image data, generation model means for analyzing contract-related data based on the symbolic information, and emotion analysis and adjustment means for analyzing the user's emotional state and adjusting the method of information presentation. This not only enables rapid and efficient management and retrieval of contract documents, but also allows for information provision that takes the user's emotions into consideration, thereby improving the user experience.

[0773] "Image data" refers to all data stored in digital format that includes visual information.

[0774] "Symbolic information" refers to information composed of letters, numbers, and other visual symbols.

[0775] "Character recognition means" refers to a technology or device that automatically reads symbolic information from image data.

[0776] "Generative modeling means" refers to an algorithm or system for analyzing data and generating specific patterns or information.

[0777] An "information recording device" refers to a system or device for storing data and retaining it so that it can be searched or referenced later.

[0778] "Information registration means" refers to a technology or device that provides the function of systematically registering structured information into an information recording device.

[0779] A "search request" refers to a request made by a user to obtain specific information.

[0780] "Connection information" refers to links or reference information that enable access to related data or documents.

[0781] "Emotional analysis and adjustment means" refers to a technology or system for evaluating a user's emotional state and changing the way information is presented based on that evaluation.

[0782] This invention combines several technical elements to realize a system that manages contract documents and provides user-responsive interaction.

[0783] The user accesses the business system via a terminal and uploads image data of contract documents to the server. The system extracts symbolic information from the image data using appropriate optical character recognition (OCR) means. For this process, an OCR engine such as Tesseract is used as the OCR software. This means that the image data is converted into a machine-readable format.

[0784] The server uses AI technologies, such as Transformers models, as a generative model to analyze the extracted symbolic information. Through this analysis, contract-related data is extracted from the contract document and registered in a contract database, which acts as an information recording device. This allows users to easily search and refer to contract information as needed.

[0785] Furthermore, the device sends user voice and text input to the server, which evaluates the user's emotional state using sentiment analysis and adjustment mechanisms. For example, by using the Google Cloud Natural Language API, it is possible to analyze emotions from the user's text and voice and adjust the presentation of information according to the user's emotions. This allows for the presentation of information to be made gentler or alert messages to be expressed in a milder tone if negative emotions are detected.

[0786] For example, when a user searches for a document using a contract identification code, the terminal analyzes the expression and tone on the server. If the server detects that the user is experiencing stress, it simplifies and presents the results. Furthermore, user experience is improved by displaying user-friendly navigation and messages.

[0787] Example of a prompt:

[0788] "Search for contract information using contract ID 12345, analyze user sentiment, and adjust the results accordingly."

[0789] Thus, the present invention improves operational efficiency and user experience by providing an emotionally sensitive interface and enabling efficient contract document management.

[0790] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0791] Step 1:

[0792] The user accesses the business system using a terminal, selects image data of contract documents in a file selection window, and uploads it to the server. The input is the image file selected by the user, and the output is the saving of the file to a specific folder on the server. The terminal communicates with the server to process the file appropriately and starts data transfer.

[0793] Step 2:

[0794] The server performs optical character recognition (OCR) processing on the received image data. The input is the uploaded image data, and the output is text data. Specifically, the server applies an OCR engine to extract character information from the image. After processing, the extracted text data is temporarily stored and passed on to the next analysis process.

[0795] Step 3:

[0796] The server analyzes the extracted text data using a generative AI model. The input is the text data obtained in step 2, and the output is structured contract information. The model identifies, for example, dates, party names, and conditions related to the contract, and organizes them as contract information. The server generates this information and prepares it for registration in the database.

[0797] Step 4:

[0798] The server uses an indexing engine to register the obtained contract information in the contract database. The input is structured contract information, and it generates output indicating that registration is complete. The database organizes the information and indexes it so that it can respond quickly to subsequent search requests.

[0799] Step 5:

[0800] User actions and voice are captured by the terminal and sent to the server. The input here is the user's voice and text-based interface operations, and the output is the data sent to the server for analysis. The terminal then formats this data appropriately as sentiment data, enabling the subsequent sentiment analysis process.

[0801] Step 6:

[0802] The server uses an emotion analysis engine to determine the user's emotional state. Input is voice and text data sent by the user, and output is information about the user's emotions. Based on this information, the server adjusts the information presentation method to provide a more appropriate interface.

[0803] Step 7:

[0804] The server provides access to appropriate contract documents based on the user's sentiment and search queries. Inputs are the user's sentiment information and search queries, while output is tailored search results and information presentations. This allows users to obtain the information they need without stress.

[0805] (Application Example 2)

[0806] 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".

[0807] When managing contract documents, there is a lack of interfaces that take into account the user's emotional state, resulting in insufficient improvements in usability and the comfort of the contract process. Therefore, there is a need to improve the overall experience of contract procedures, including electronic payments, by enabling flexible information presentation that responds to the user's emotions.

[0808] 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.

[0809] In this invention, the server includes optical character recognition means for extracting character information from contract documents stored as image files, generative model means for analyzing contract-related information based on the character information, and emotion response means for analyzing the user's emotional state, dynamically adjusting the interface based on the corresponding emotional state, and adaptively providing information. This enables efficient management of contract documents and flexible information provision that takes into account the user's emotions.

[0810] An "image file" is a collection of visual information stored using electronic means, and it is a digital record of the contents of a contract document.

[0811] "Text information" refers to string data extracted from image files, specifically the text content included in contract documents.

[0812] "Optical character recognition means" refers to devices or software that include technology for detecting characters within an image and converting them into electronic text data.

[0813] A "generative modeling method" is a technology that analyzes contract-related information based on input data and generates structured related data.

[0814] "Index creation methods" refer to the procedures and technologies used to register extracted contract information in a database so that it can be efficiently managed and searched.

[0815] A "search provision method" is a technology that provides access links to relevant contract documents and information based on search queries from users.

[0816] "Means of reference" refers to a means of immediately accessing contract documents and verifying their contents.

[0817] "Emotional response methods" are technologies aimed at analyzing a user's emotional state and adjusting the interface or presenting information accordingly.

[0818] This system is designed to provide efficient management of contract documents and an interface that takes user emotions into consideration. First, the user acquires an image of the contract document using an electronic terminal and sends it to the server. The server extracts the text information from the image file using optical character recognition means and analyzes the extracted text information using a generation model means. Through this analysis process, information related to the contract is generated as structured data.

[0819] Next, the server registers the structured contract information in a database using an indexing mechanism. This database enables high-speed searching and provides links to relevant contract documents in response to user search queries.

[0820] The interface analyzes the user's emotional state through emotion-responsive mechanisms and dynamically adjusts how information is presented based on those emotions. The server recognizes emotions from the user's input text or voice data and adjusts the interface accordingly. For example, if the user is stressed, the interface will display information in a calmer tone.

[0821] The specific technologies used include the Google Cloud Vision API for optical character recognition, the Python NLTK library for natural language processing, and the IBM Watson Tone Analyzer API for sentiment analysis. The hardware can be a smartphone or a personal computer.

[0822] As a concrete example, when a user reviews the terms of service for a new electronic payment service, they can take a picture of the document with their device's camera and instantly view the text. If the user expresses concern, the system uses an AI model to display a message such as, "Please rest assured. We will provide you with support information regarding this agreement immediately." An example of a prompt message in this case would be, "The user is feeling anxious while reviewing the contract. Please sense the user's emotional state and generate a reassuring message."

[0823] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0824] Step 1:

[0825] The user obtains the contract document as an image using an electronic device. The input is image data of the contract document, and this image is sent to the server as output. The user uses the device's camera function to photograph the entire document and uploads that data to the server.

[0826] Step 2:

[0827] The server performs optical character recognition (OCR) processing on the received image data. The input is image data sent by the user, and the output is extracted character information. As for OCR technology, the Google Cloud Vision API is used to analyze the characters in the image and convert them into text data.

[0828] Step 3:

[0829] The server analyzes the obtained character information using a generative model. The input is text data from OCR processing, and the output is structured contract information. Natural language processing techniques are used to extract contract requirements and conditions and convert them into structured data for registration in the database.

[0830] Step 4:

[0831] The server registers structured contract information in a database. The input is contract information structured by a generative model, and the output is a confirmation of successful registration to the contract database. An indexing mechanism is used to organize the data in a format that allows for rapid searching.

[0832] Step 5:

[0833] This system analyzes the user's emotional state and dynamically adjusts the interface. Input is user voice or text data, and output is adaptive information presentation corresponding to the emotion. The IBM Watson Tone Analyzer API is used for emotion analysis, and the server adjusts the interface tone and information content according to the recognized emotion.

[0834] Step 6:

[0835] The user enters a search query and retrieves information on relevant contract documents. The input is the user's search query, and the output is the relevant contract documents and their links. The search results are displayed on the terminal interface along with emotionally sensitive and gentle messages.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] The inside of the Emotion Map 400 represents what's in your mind, while the outside represents what you're doing. Therefore, the further you go out the 400-coordinate scale, the more visible your emotions become (the more they manifest in your actions).

[0843] 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.

[0844] 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."

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] The following is further disclosed regarding the embodiments described above.

[0858] (Claim 1)

[0859] An optical character recognition means for extracting text information from contract documents saved as image files,

[0860] A generative model means for analyzing contract-related information based on the aforementioned textual information,

[0861] An index creation means for registering contract information in a contract database using the analyzed information,

[0862] A search provision means that searches the contract database based on a search query received from a user and provides links to relevant contract documents,

[0863] A means of reference that allows immediate access to contract documents using the aforementioned link,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The optical character recognition means has the function of processing individual pages from an image file having multiple pages and efficiently extracting character information, according to claim 1.

[0867] (Claim 3)

[0868] The search provision means supports both a search based on a contract identification number and a full-text search based on any keyword, according to claim 1.

[0869] "Example 1"

[0870] (Claim 1)

[0871] A character recognition method for extracting character data from contract documents saved as image files,

[0872] A model means for identifying important information using the aforementioned character data,

[0873] A registration means for registering structured data containing the identified information in a management platform,

[0874] An information provision means that provides reference information to relevant documents based on search criteria received from the user,

[0875] A referencing means that allows referencing the document content using the aforementioned reference information,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, wherein the character recognition means has the function of sequentially processing each page of an image file having multiple pages and efficiently extracting character data.

[0879] (Claim 3)

[0880] The information provision means is the system according to claim 1, which enables both a search based on contract identification information and a full-text search based on a variety of keywords.

[0881] "Application Example 1"

[0882] (Claim 1)

[0883] An optical character recognition means for extracting text information from contract documents saved as image files,

[0884] A generative model means for analyzing contract-related information based on the aforementioned textual information,

[0885] An index creation means for registering contract information in a contract database using the analyzed information,

[0886] A search provision means that searches the contract database based on a search query received from a user and provides links to relevant contract documents,

[0887] A means of reference that allows immediate access to contract documents using the aforementioned link,

[0888] A means of receiving images into a mobile information terminal,

[0889] A means for converting an image received by the mobile information terminal into character data via the optical character recognition means, and for automatically extracting contract information using the generation model means,

[0890] A processing means for efficiently managing and retrieving the aforementioned contract information within a mobile information terminal,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The optical character recognition means has the function of processing individual pages from an image file having multiple pages and efficiently extracting character information, and similarly processes images taken using the camera function of a mobile information terminal, as described in claim 1.

[0894] (Claim 3)

[0895] The search provision means supports both a search based on a contract identification number and a full-text search based on any keyword, and the system according to claim 1 displays the search results in a user-friendly interface on a mobile information terminal.

[0896] "Example 2 of combining an emotion engine"

[0897] (Claim 1)

[0898] A character recognition means for extracting symbolic information from a document stored as image data,

[0899] A generative model means for analyzing contract-related data based on the aforementioned symbolic information,

[0900] Information registration means for registering contract data in an information recording device using the analyzed data,

[0901] A search provision means that searches the information recording device based on a search request received from a user and provides connection information to related documents,

[0902] A referencing means that allows immediate access to a document using the aforementioned connection information,

[0903] A means for analyzing the user's emotional state and adjusting the way information is presented,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, wherein the character recognition means has the function of processing individual pages from image data having multiple pages and efficiently extracting symbolic information.

[0907] (Claim 3)

[0908] The search provision means supports both a search based on a contract identification code and a full-text search based on an arbitrary code, according to claim 1.

[0909] "Application example 2 when combining with an emotional engine"

[0910] (Claim 1)

[0911] An optical character recognition means for extracting text information from contract documents saved as image files,

[0912] A generative model means for analyzing contract-related information based on the aforementioned textual information,

[0913] An index creation means for registering contract information in a contract database using the analyzed information,

[0914] A search provision means that searches the contract database based on a search query received from a user and provides links to relevant contract documents,

[0915] A means of reference that allows immediate access to contract documents using the aforementioned link,

[0916] An emotional response means that analyzes the user's emotional state, dynamically adjusts the interface based on the corresponding emotional state, and adaptively provides information;

[0917] A system that includes this.

[0918] (Claim 2)

[0919] The optical character recognition means has the function of processing individual pages from an image file having multiple pages and efficiently extracting character information, according to claim 1.

[0920] (Claim 3)

[0921] The search provision means supports both a search based on a contract identification number and a full-text search based on any keyword, according to claim 1. [Explanation of symbols]

[0922] 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. An optical character recognition means for extracting text information from contract documents saved as image files, A generative model means for analyzing contract-related information based on the aforementioned textual information, An index creation means for registering contract information in a contract database using the analyzed information, A search provision means that searches the contract database based on a search query received from a user and provides links to relevant contract documents, A means of reference that allows immediate access to contract documents using the aforementioned link, A means of receiving images into a mobile information terminal, A means for converting an image received by the mobile information terminal into character data via the optical character recognition means, and for automatically extracting contract information using the generation model means, A processing means for efficiently managing and retrieving the aforementioned contract information within a mobile information terminal, A system that includes this.

2. The optical character recognition means has the function of processing individual pages from an image file having multiple pages and efficiently extracting character information, and similarly processes images taken using the camera function of a mobile information terminal, as described in claim 1.

3. The system according to claim 1, wherein the search provision means supports both a search based on a contract identification number and a full-text search based on any keyword, and displays the search results on a mobile information terminal with a user-friendly interface.

Images