system

Abstract

We provide the system. [Solution] Information processing means for receiving old and new data, A text extraction means for extracting text data from old and new documents, A difference detection means that compares the text data of old and new documents and detects the differences, A table generation means that classifies the detected differences and generates a revised table, An output means for outputting the generated current revised table, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the 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] When the versions of materials or documents are updated, the conventional method requires manual or visual comparison and confirmation, which consumes a large amount of time and labor. In particular, in an environment where many documents are frequently updated within an enterprise or organization, such a method is inefficient and may lead to a decrease in productivity. In addition, since human errors are likely to occur in manual confirmation work, it is required to accurately grasp the changed parts without omission quickly and accurately.

Means for Solving the Problems

[0005] This invention provides a difference detection means that automatically receives old and new documents, extracts text data from each, compares them, and detects differences. Furthermore, by using a table generation means that automatically classifies the detected differences and generates a current revised table in a two-page spread format, the system provides a system that makes it easy to identify changes. This significantly reduces the time and effort required for checking updated documents and improves accuracy.

[0006] "Older documents" refer to documents and data saved as previous versions, and include information that was available before any updates or changes were made.

[0007] "New materials" refers to the latest version of documents or data after updates or revisions, and includes the most up-to-date information.

[0008] "Information processing means" refers to a device or part of software intended for information processing, such as receiving, analyzing, storing, and transmitting data.

[0009] "Text extraction means" refers to a device or algorithm for analyzing the content of a document file and extracting that text information.

[0010] A "difference detection means" refers to a device or program used to compare two datasets and identify their differences or changes.

[0011] "Table generation means" refers to a device or software function for organizing information and converting it into a table format that provides a visual overview.

[0012] "Output means" refers to devices or methods for providing processing results to the user in a visual or physical form. [Brief explanation of the drawing]

[0013] [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] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It 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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the processor with reference numerals (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the RAM (Random Access Memory) with reference numerals is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the storage with reference numerals is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the communication I / F (Interface) with reference numerals is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system that efficiently and automatically detects changes between old and new documents and presents them to the user in a visually easy-to-understand format. This system primarily operates on a server and processes documents provided by the user's terminal.

[0035] Users upload old and new documents to the server via their devices. The server receives these documents and extracts the content of each document using text extraction methods. This text data is then analyzed using appropriate libraries and parsers selected according to the format of the documents.

[0036] Next, the server uses difference detection methods to compare the text data of the old and new documents. Generative AI models and algorithms are used in this comparison to detect specific changes. At this stage, the locations where changes have been made, the content that has been added, and the content that has been deleted are identified and all categorized.

[0037] The server uses this differential information to create the current revised table using a table generation mechanism. This table is designed so that users can easily see the changes, and the changed parts are highlighted with color coding and annotations.

[0038] Finally, the server sends the generated revised table to the user's terminal via an output device. The user can then visually confirm the revised table using a browser or PDF viewer and quickly understand the changes in the document.

[0039] For example, if a user uploads both an old and new version of their company's product specifications to this system, the server will detect additions, changes, and deletions to the specifications and provide them to the user as a single revised specification sheet. This allows the user to easily see which parts have been changed, significantly streamlining the verification process.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user uploads both old and new documents to the server using their device. The user selects these documents through a file selection menu and sends them to the server by pressing the upload button.

[0043] Step 2:

[0044] The server receives the uploaded materials and saves them to storage. In this process, when a file is sent via an HTTP request, the server retrieves it and stores it in a temporary storage directory.

[0045] Step 3:

[0046] The server uses a text extraction method to extract text data from the stored documents. In this process, if the document is a PDF file, it uses the PDF extraction library; if it's a Word file, it uses the Word extraction library to extract the document content in text format.

[0047] Step 4:

[0048] The server uses difference detection means to compare the extracted text data of the old and new documents. In this process, a generative AI model or difference algorithm is applied to detect the differences between the two documents. The focus is mainly on identifying changes, additions, and deletions.

[0049] Step 5:

[0050] The server creates a current revision table using a table generation mechanism based on the detected difference information. This table clearly displays the detected differences, and changes are highlighted in color, making it easy for the user to review them.

[0051] Step 6:

[0052] The server outputs the completed revised table and sends it to the user's terminal. In this step, the server provides the generated revised table as an HTTP response and presents the user with a download link.

[0053] Step 7:

[0054] The user uses their terminal to download the current revised table provided by the server and check its contents. The user then opens the table using a browser or appropriate document viewer to visually understand the changes.

[0055] (Example 1)

[0056] 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."

[0057] When multiple pieces of information are updated, there is a need for a system that can efficiently detect these changes and present them in a visually easy-to-understand format. However, existing systems require manual verification of information changes, which is time-consuming and labor-intensive. Furthermore, there are insufficient means to present changes in an integrated manner, making it difficult to provide information in a way that users can easily understand.

[0058] 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.

[0059] In this invention, the server includes information processing means for receiving old and new information; data extraction means for extracting character data from the old and new information; difference detection means for comparing the character data of the old and new information and detecting the differences; table generation means for classifying the detected differences and generating a change table; output means for outputting the generated change table; and generation means for generating prompt statements that highlight the differences using a generation AI model and performing correspondence based on them. This allows users to quickly grasp the updated parts of the information. In addition, changes are color-coded and visually highlighted, providing intuitive understanding and significantly improving the efficiency of the verification process.

[0060] "Information processing means" refers to means that have the function of receiving old and new information and processing that information appropriately.

[0061] A "data extraction means" is a means that has the function of extracting character data from received information and converting it into the required format.

[0062] A "difference detection means" is a means that has the function of comparing extracted character data and identifying the parts that are different.

[0063] The "table generation means" is a means that has the function of classifying the detected different parts and generating a change table that can be visually confirmed.

[0064] "Output means" refers to means that have the function of sending the generated change sheet to the user's terminal and outputting it so that the user can confirm it.

[0065] A "generative AI model" is an artificial intelligence model used to explicitly indicate different parts based on a given prompt statement.

[0066] A "prompt statement" is a statement used to give specific instructions to a generative AI model, and in this invention, it is used to clarify different parts.

[0067] This invention provides a system that efficiently detects updated information and presents it to the user in a visually easy-to-understand format. The following describes specific embodiments for implementing this system.

[0068] The user selects the old and new information they want to compare from their device and uploads it to the server. The server receives this information and extracts the text data using appropriate data extraction methods depending on the format, such as PDF or Word document. Specifically, a PDF parser or document reading library can be used.

[0069] The server utilizes a generative AI model as a means of detecting differences, using prompts to identify the differences. An example of such a prompt is an instruction such as, "Identify the differences between the old and new information and clearly indicate them." Based on the generated prompts, the AI ​​model compares the old and new information and clearly indicates the differences.

[0070] Next, the server classifies the detected differences and uses a table generation mechanism to create a visually easy-to-understand change table. In this change table, text changes are color-coded, ensuring that users can intuitively understand the changes.

[0071] Finally, the server sends the generated change sheet to the user's terminal via an output device. The user can then review the change sheet on their terminal using a PDF viewer or similar software, allowing them to quickly and accurately understand the updated information.

[0072] For example, if a user uploads an old version of a business manual along with a new version, the server analyzes each section of the manual and generates a change list indicating which parts have been updated. Sections that have been changed are marked with a yellow marker, allowing users to see at a glance where the changes are.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user uses a terminal to select old and new information and upload it to the server. The input is information files in PDF or Word format. The server receives these files and saves them to the specified folder.

[0076] Step 2:

[0077] The server uses a data extraction method to extract character data from the received file. The input is the information file received in step 1. For data extraction, a PDF parser or document reading library is used to convert the character data into text format and output it.

[0078] Step 3:

[0079] The server uses a generative AI model to compare the text data of the old and new information. The input is the text data converted in step 2. The generative AI model is given the instruction, "Identify the differences between the old and new information and clearly indicate them," as a prompt. The AI ​​model detects the differences and identifies the parts that are different. Based on this, it outputs data on the changes.

[0080] Step 4:

[0081] The server classifies the difference data and creates a change table using a table generation mechanism. The input is the difference data generated in step 3. Based on the difference data, the server generates and outputs a visually easy-to-understand table with changes color-coded.

[0082] Step 5:

[0083] The server sends the generated change sheet to the user's terminal via an output method. The input is the change sheet generated in step 4. The output is a change sheet in PDF format that is easily accessible to the user. The user opens this with a PDF viewer on their terminal and checks the updated information.

[0084] (Application Example 1)

[0085] 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."

[0086] Traditional information management in physical stores often involved manual changes to product information and campaign details, leading to errors and wasted time. Furthermore, it was difficult for staff to accurately grasp the changes and provide appropriate guidance to customers.

[0087] 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.

[0088] In this invention, the server includes an information processing device for receiving old and new documents, a character extraction device for extracting character data from the old and new documents, and a difference detection device for comparing the character data of the old and new documents and detecting differences. This enables the rapid and accurate notification of changes to staff using mobile terminals when changes occur in documents at a physical store, and facilitates the smooth provision of information to customers.

[0089] An "information processing device for receiving old and new materials" is a device that receives materials uploaded by users and converts them into a format that can be used in subsequent processing steps.

[0090] A "character extraction device" is a device that extracts character data related to the content from received materials and organizes it as data that can be analyzed using text.

[0091] A "difference detection device" is a device that compares text data between old and new documents and has the function of detecting the differences between the two, i.e., the points of difference.

[0092] A "table generation device" is a device that organizes and classifies the detected differences and automatically generates an updated comparison table in a visually easy-to-understand format.

[0093] An "output device" is a device that transmits and displays the generated update comparison table and other information in an appropriate format to the user's terminal or display.

[0094] A "highlighting display device" is a device that visually highlights important changes and differences within a generated update comparison table, allowing users to quickly identify them.

[0095] A "portable terminal device" is a device that provides users with generated information in a portable format for use in actual work environments.

[0096] The system implementing this invention can improve the efficiency of information updates in store operations by comparing old and new documents, automatically detecting and displaying the changes contained therein.

[0097] The server first receives the old and new documents uploaded by the user via an information processing device. Text data is extracted from these documents using a character extraction device. This process utilizes Python's text analysis libraries for advanced text extraction and analysis. Next, a difference detection device compares this text data and detects the differences. Libraries such as difflib are used for difference detection.

[0098] Once all differences are detected, the table generator automatically uses this information to create an updated comparison table. This table is designed so that users can intuitively understand the changes, as the changes are color-coded using a highlighting device. The table is finally sent from the output device to the user's mobile device. On the mobile device, a React Native-based application displays this information, allowing store staff to immediately review it on-site.

[0099] For example, when a new product's price is changed, store staff upload both the old and new price lists to the app. The server detects and displays the price difference, allowing staff to quickly check the changes on their mobile devices. The system can streamline these processes by using prompts such as, "Can you upload the old and new documents and detect the changes? Please visually highlight which parts have been changed."

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The server receives both old and new documents from users. When a user uploads documents using a terminal, these are input into the information processing device on the server. The input data is converted into a format suitable for subsequent processing and stored on the server.

[0103] Step 2:

[0104] The server extracts text data from the received documents using a character extraction device. Depending on the format of the documents, a Python text extraction library is used, and the content of each document is output as character data. This character data is used in the subsequent difference detection process.

[0105] Step 3:

[0106] The server uses a difference detection device to compare the text data of the old and new documents. Here, the difflib library is used to compare the data and identify the differences. The input is the extracted text data, and the output is information identifying and listing the differences.

[0107] Step 4:

[0108] The server generates an updated comparison table based on the detected differences via a table generator. Difference information is input, and tabular data including highlighted sections is generated. The generated table is visualized in a way that is intuitively understandable to the user.

[0109] Step 5:

[0110] The server sends the generated update comparison table from the output device to the user's mobile device. This process allows the table to be displayed on the device via an application, which the user then applies to their work within the store. The input is the update comparison table, and the output is a visualized table displayed on the user's device.

[0111] 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.

[0112] This invention combines a system that automatically detects changes associated with document updates and visually presents them to the user with a function that recognizes the user's emotions and adapts to their reactions. This system operates on a server and processes documents provided by the user's terminal.

[0113] First, the user uploads both the old and new documents to the server via their device. The server receives these documents and extracts their content using text extraction methods. The server uses generative AI models and diff algorithms to accurately detect the differences between the old and new documents. This comparison provides detailed information such as changes, additions, and deletions.

[0114] Next, the server uses an emotion engine to recognize the user's emotional state. In this process, it analyzes the user's voice and facial expression data collected from the device to estimate their emotions. The emotion engine provides information such as whether the user's emotions are positive or negative, or whether they are experiencing stress.

[0115] Based on this, the server uses a table generation mechanism to create the revised table. The revised table is a visual interface that highlights the changes, and is characterized by the fact that the highlighting color and display format can be adjusted according to the user's mood. For example, if the user is feeling stressed, the color can be changed to a more calming one.

[0116] Finally, the server sends the generated revised table to the user's terminal via an output method, and the user downloads it. The user can then check the revised table using a browser or appropriate document viewer and immediately understand the changes in the document.

[0117] As a concrete example, when a project manager uploads old and new versions of a specification document to the system, the server detects the additions and changes to the new specifications and reflects the changes in a table according to the project manager's emotional state. In this way, the burden on the user is reduced while allowing for efficient document review.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] The user uploads old and new documents to the server using their device. The user opens a browser, uses the designated interface, selects the documents by clicking the upload button, and sends them to the server.

[0121] Step 2:

[0122] The server receives the uploaded files and saves them in a temporary directory. Files received as HTTP requests are sorted into the appropriate folder according to their type.

[0123] Step 3:

[0124] The server uses text extraction methods to extract text data from the stored documents. During this process, different parsing libraries are used to obtain the text content depending on the file format, such as PDF or Word.

[0125] Step 4:

[0126] The server uses a difference detection method to compare the extracted text data of the old and new documents. A generative AI model is applied to detect changes between the two documents and classify the differences into categories such as additions, deletions, and modifications.

[0127] Step 5:

[0128] The server acquires user voice and facial expression data as real-time feedback from the device and analyzes it using an emotion engine. This involves using data acquired through the device's camera and microphone to identify the user's emotional state.

[0129] Step 6:

[0130] Based on the user's emotional data analyzed by the emotion engine, the server assembles a table using a table generation mechanism. Here, visual adjustments such as color scheme and font size are made according to the user's emotions, creating a table in a format suitable for the user's psychological state.

[0131] Step 7:

[0132] The server sends the generated revised table to the user's terminal via an output method. The user can then download the file by clicking the provided download link and open it in a browser or PDF viewer to check the changes.

[0133] (Example 2)

[0134] 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".

[0135] The process of identifying changes when updating documents and providing visual feedback based on those changes is often done manually, resulting in high time and labor costs. Furthermore, there is a need to improve the user experience by providing feedback that responds to the user's emotional state, but existing systems make it difficult to achieve this efficiently. These challenges need to be addressed.

[0136] 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.

[0137] In this invention, the server includes: information processing means for receiving old and new materials; data extraction means for extracting text data from the old and new materials; data comparison means for comparing the text data of the old and new materials and detecting differences; table generation means for classifying the detected differences and generating a table for visually highlighting them; emotion recognition means for analyzing input data to analyze the user's emotional state; adjustment means for adjusting the visual display based on the user's emotional state; and output means for outputting the generated table. This makes it possible to efficiently grasp changes in materials and provide interactive feedback that corresponds to the user's emotional state.

[0138] "Information processing means" refers to devices or processes that have the function of receiving old and new data into a server.

[0139] A "data extraction means" refers to a device or process that has the function of extracting text data from received materials.

[0140] A "data comparison tool" is a device or process used to compare text data from old and new documents and detect changes or differences.

[0141] A "table generation means" is a device or process for generating a table that classifies the detected differences and visually highlights them.

[0142] "Emotion recognition means" refers to devices or processes that analyze a user's voice and facial expression data to estimate the user's emotional state.

[0143] "Adjustment means" refers to devices or processes for adjusting visual display attributes based on the user's emotional state.

[0144] "Output means" refers to a device or process that sends the generated table to the user's terminal, enabling visual confirmation.

[0145] This invention is a system that accurately detects changes resulting from updates to documents and further presents them visually in a way that is appropriate to the user's emotional state. This system runs on a server and processes information by communicating with the user's terminal.

[0146] Users select old and new documents using a terminal and upload them to the system. The server then receives these documents via information processing. Once the documents reach the server, data extraction is activated to extract the text data from the documents. The server uses a generative AI model and a differential algorithm to compare the text data between the old and new documents. This makes it possible to accurately identify the changed, added, and deleted sections.

[0147] Next, the server uses emotion recognition to understand the user's emotional state. For this purpose, voice data and facial expression data collected from the terminal are analyzed to determine whether the user is feeling positive, negative, or stressed. After identifying the emotion, the server uses a table generation mechanism to generate a current state table that highlights the detected discrepancies. The highlighting color and visual interface of this current state table are adjusted according to the user's emotional state.

[0148] Finally, the server sends the generated revised table to the user's terminal via the output method. The user can then use a browser or document viewer to review this visual interface and efficiently understand the changes in the document.

[0149] As a concrete example, when a project manager uploads an old and new version of a specification document, the server identifies the differences and, if it determines that the manager's emotions are negative, provides the revised version with a more subdued color scheme. As a result, the project manager can improve work efficiency while reducing their visual and psychological burden.

[0150] An example of a prompt message is: "Upload the old and new documents and detect the changes. Also perform sentiment analysis and display the results in a format that reflects the user's sentiment."

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The user selects old and new documents using their terminal and uploads them to the server. The input is the file data of the old and new documents, and the output is the document data as it is stored on the server. Specifically, the user selects each file by clicking the upload button in the browser and submits it.

[0154] Step 2:

[0155] The server receives uploaded materials via information processing equipment and extracts text data from each material using data extraction equipment. The input is the material data stored on the server, and the output is the extracted text data. Specifically, the server digitally analyzes text from PDF and Word documents and converts it into a structured text format.

[0156] Step 3:

[0157] The server uses a generative AI model and data comparison tools to compare text data from old and new documents and detect differences. The input is extracted text data, and the output is difference data containing details of the differences. Specifically, the server uses natural language processing techniques to analyze sentences and paragraphs within the text and identify parts that have been added, deleted, or modified.

[0158] Step 4:

[0159] The server analyzes voice and facial expression data acquired from the user's terminal using emotion recognition technology to estimate the user's emotional state. The input is voice data and facial expression data, and the output is estimated emotional state data. Specifically, voice analysis software analyzes tone and speech patterns, and a facial recognition algorithm estimates emotions from facial expressions.

[0160] Step 5:

[0161] The server generates a revised table highlighting the differences based on the previously detected difference data and emotion state data, using a table generation mechanism. The input is the difference data and emotion state data, and the output is a visually adjusted table for distribution to the user. Specifically, if the emotion is negative, it is displayed with a softer highlight color, and other processing is performed.

[0162] Step 6:

[0163] The server sends the generated revised table to the user's terminal via an output device. The input is a visually adjusted table, and the output is data in a format viewable on the user's terminal. Specifically, the server converts the digital data into an appropriate file format and sends it to the user via the internet. The user can open this table in a browser or compatible application to check the changes in the document.

[0164] (Application Example 2)

[0165] 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".

[0166] In today's rapidly changing market environment, product placement and promotional content in physical stores are frequently updated. While it's crucial for staff to accurately and quickly grasp these changes, information overload and inappropriate communication methods can reduce work efficiency and increase employee stress. Therefore, there is a need for effective methods to recognize changes and communicate them appropriately, taking into account staff emotional states.

[0167] 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.

[0168] In this invention, the server includes data processing means for receiving old and new information, data extraction means for extracting character data from the old and new information, and difference detection means for comparing the character data and detecting difference areas. This makes it possible to quickly and appropriately communicate product and promotion change information to store staff. Furthermore, by including emotion recognition means for analyzing the emotional state of the user and display adjustment means for adjusting the display format of the generated change table, it is possible to provide information with less stress tailored to the state of individual staff members.

[0169] A "data processing means" is a means for receiving old and new information and passing them on to subsequent processing.

[0170] A "data extraction method" is a means for extracting character data from received information.

[0171] A "difference detection method" is a means of comparing the text data of old information and new information and identifying the differences between them.

[0172] The "table generation means" is a means for creating a visual change table based on the detected difference areas.

[0173] "Emotion recognition means" refers to a method for analyzing a user's emotional state and utilizing that information.

[0174] "Display adjustment means" refers to means for dynamically adjusting the display format of the generated change table based on the user's emotional state.

[0175] "Output means" refers to means for outputting the generated change sheet to a specified device.

[0176] The system for implementing the present invention mainly consists of a server, a smart device (such as smart glasses), and various software components connected thereto. Specifically, the server acts as the central component, receiving old and new information via data processing means. This information is converted into individual character data using data extraction means and compared by difference detection means. Subsequently, differences are identified and materialized as a change table by table generation means.

[0177] Users receive the contents of the change sheet via a smart device. The server analyzes the user's emotional state using emotion recognition and dynamically adjusts the color scheme and notification method of the change sheet using display adjustment mechanisms. This makes it possible to provide information efficiently while reducing user stress. Specific hardware used includes smart devices such as smart glasses (Google® Glass®, etc.), while a general cloud platform is used on the server side.

[0178] Furthermore, the software utilizes OCR libraries (such as Tesseract) for text extraction, emotion recognition libraries (such as Affectiva SDK) for emotion analysis, and UI frameworks (such as OpenCV) for visual display. These components work together to support the overall operation of the system.

[0179] For example, if a physical store frequently updates its product displays, staff members wear smart glasses while patrolling the store. The server sends a change sheet generated based on the store's update information to the glasses, and presents the information in an appropriate color scheme and notification format according to the staff member's emotional state. This helps ensure that important information is not overlooked and that work proceeds smoothly. By inputting a prompt such as "Check the display placement of new products and suggest ways to reduce notification stress" into the generating AI model, the optimal method of providing information is suggested.

[0180] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0181] Step 1:

[0182] The server receives both old and new information from users. This provides the underlying dataset for information that needs updating. The received data is stored for subsequent processing.

[0183] Step 2:

[0184] The server extracts text data from the received information using data extraction methods. This process uses an OCR library (e.g., Tesseract) to convert text from paper documents and images into a digital format. As a result, the information is output in a format that can be used as text data.

[0185] Step 3:

[0186] The server compares the text data of the old and new information using a difference detection mechanism. In this step, a comparison algorithm is used to clarify the differences in the text content. The output is a dataset showing the detected differences.

[0187] Step 4:

[0188] The server generates a change table from the difference detection results using a table generation mechanism. In this process, the table is structured in a format that clearly indicates the changes as a visual interface. This provides an output that allows the user to visually confirm the changes.

[0189] Step 5:

[0190] The server analyzes the user's emotional state using emotion recognition technology. It analyzes voice and facial expression data acquired from the user's device to evaluate whether the user's emotions are positive, negative, or stressful. The output is data reflecting the user's emotional state.

[0191] Step 6:

[0192] The server adjusts the display format of the change table based on the user's emotional state using display adjustment mechanisms. Specifically, if the user is experiencing stress, the display colors and font size are changed to more calming ones, among other customizations tailored to the user.

[0193] Step 7:

[0194] The server uses an output mechanism to send the generated change sheet to the user's terminal. As a final output, a visually adjusted change sheet is presented on the user's smart device, allowing the user to review its contents and utilize them in their work.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] [Second Embodiment]

[0199] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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".

[0211] This invention is a system that efficiently and automatically detects changes between old and new documents and presents them to the user in a visually easy-to-understand format. This system primarily operates on a server and processes documents provided by the user's terminal.

[0212] Users upload old and new documents to the server via their devices. The server receives these documents and extracts the content of each document using text extraction methods. This text data is then analyzed using appropriate libraries and parsers selected according to the format of the documents.

[0213] Next, the server uses difference detection methods to compare the text data of the old and new documents. Generative AI models and algorithms are used in this comparison to detect specific changes. At this stage, the locations where changes have been made, the content that has been added, and the content that has been deleted are identified and all categorized.

[0214] The server uses this differential information to create the current revised table using a table generation mechanism. This table is designed so that users can easily see the changes, and the changed parts are highlighted with color coding and annotations.

[0215] Finally, the server sends the generated revised table to the user's terminal via an output device. The user can then visually confirm the revised table using a browser or PDF viewer and quickly understand the changes in the document.

[0216] For example, if a user uploads both an old and new version of their company's product specifications to this system, the server will detect additions, changes, and deletions to the specifications and provide them to the user as a single revised specification sheet. This allows the user to easily see which parts have been changed, significantly streamlining the verification process.

[0217] The following describes the processing flow.

[0218] Step 1:

[0219] The user uploads both old and new documents to the server using their device. The user selects these documents through a file selection menu and sends them to the server by pressing the upload button.

[0220] Step 2:

[0221] The server receives the uploaded materials and saves them to storage. In this process, when a file is sent via an HTTP request, the server retrieves it and stores it in a temporary storage directory.

[0222] Step 3:

[0223] The server uses a text extraction method to extract text data from the stored documents. In this process, if the document is a PDF file, it uses the PDF extraction library; if it's a Word file, it uses the Word extraction library to extract the document content in text format.

[0224] Step 4:

[0225] The server uses difference detection means to compare the extracted text data of the old and new documents. In this process, a generative AI model or difference algorithm is applied to detect the differences between the two documents. The focus is mainly on identifying changes, additions, and deletions.

[0226] Step 5:

[0227] The server creates a current revision table using a table generation mechanism based on the detected difference information. This table clearly displays the detected differences, and changes are highlighted in color, making it easy for the user to review them.

[0228] Step 6:

[0229] The server outputs the completed revised table and sends it to the user's terminal. In this step, the server provides the generated revised table as an HTTP response and presents the user with a download link.

[0230] Step 7:

[0231] The user uses their terminal to download the current revised table provided by the server and check its contents. The user then opens the table using a browser or appropriate document viewer to visually understand the changes.

[0232] (Example 1)

[0233] 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".

[0234] When multiple pieces of information are updated, there is a need for a system that can efficiently detect these changes and present them in a visually easy-to-understand format. However, existing systems require manual verification of information changes, which is time-consuming and labor-intensive. Furthermore, there are insufficient means to present changes in an integrated manner, making it difficult to provide information in a way that users can easily understand.

[0235] 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.

[0236] In this invention, the server includes information processing means for receiving old and new information; data extraction means for extracting character data from the old and new information; difference detection means for comparing the character data of the old and new information and detecting the differences; table generation means for classifying the detected differences and generating a change table; output means for outputting the generated change table; and generation means for generating prompt statements that highlight the differences using a generation AI model and performing correspondence based on them. This allows users to quickly grasp the updated parts of the information. In addition, changes are color-coded and visually highlighted, providing intuitive understanding and significantly improving the efficiency of the verification process.

[0237] "Information processing means" refers to means that have the function of receiving old and new information and processing that information appropriately.

[0238] A "data extraction means" is a means that has the function of extracting character data from received information and converting it into the required format.

[0239] A "difference detection means" is a means that has the function of comparing extracted character data and identifying the parts that are different.

[0240] The "table generation means" is a means that has the function of classifying the detected different parts and generating a change table that can be visually confirmed.

[0241] "Output means" refers to means that have the function of sending the generated change sheet to the user's terminal and outputting it so that the user can confirm it.

[0242] A "generative AI model" is an artificial intelligence model used to explicitly indicate different parts based on a given prompt statement.

[0243] A "prompt statement" is a statement used to give specific instructions to a generative AI model, and in this invention, it is used to clarify different parts.

[0244] This invention provides a system that efficiently detects updated information and presents it to the user in a visually easy-to-understand format. The following describes specific embodiments for implementing this system.

[0245] The user selects the old and new information they want to compare from their device and uploads it to the server. The server receives this information and extracts the text data using appropriate data extraction methods depending on the format, such as PDF or Word document. Specifically, a PDF parser or document reading library can be used.

[0246] The server utilizes a generative AI model as a means of detecting differences, using prompts to identify the differences. An example of such a prompt is an instruction such as, "Identify the differences between the old and new information and clearly indicate them." Based on the generated prompts, the AI ​​model compares the old and new information and clearly indicates the differences.

[0247] Next, the server classifies the detected differences and uses a table generation mechanism to create a visually easy-to-understand change table. In this change table, text changes are color-coded, ensuring that users can intuitively understand the changes.

[0248] Finally, the server sends the generated change sheet to the user's terminal via an output device. The user can then review the change sheet on their terminal using a PDF viewer or similar software, allowing them to quickly and accurately understand the updated information.

[0249] For example, if a user uploads an old version of a business manual along with a new version, the server analyzes each section of the manual and generates a change list indicating which parts have been updated. Sections that have been changed are marked with a yellow marker, allowing users to see at a glance where the changes are.

[0250] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0251] Step 1:

[0252] The user uses a terminal to select old and new information and upload it to the server. The input is information files in PDF or Word format. The server receives these files and saves them to the specified folder.

[0253] Step 2:

[0254] The server uses a data extraction method to extract character data from the received file. The input is the information file received in step 1. For data extraction, a PDF parser or document reading library is used to convert the character data into text format and output it.

[0255] Step 3:

[0256] The server uses a generative AI model to compare the text data of the old and new information. The input is the text data converted in step 2. The generative AI model is given the instruction, "Identify the differences between the old and new information and clearly indicate them," as a prompt. The AI ​​model detects the differences and identifies the parts that are different. Based on this, it outputs data on the changes.

[0257] Step 4:

[0258] The server classifies the difference data and creates a change table using a table generation mechanism. The input is the difference data generated in step 3. Based on the difference data, the server generates and outputs a visually easy-to-understand table with changes color-coded.

[0259] Step 5:

[0260] The server sends the generated change sheet to the user's terminal via an output method. The input is the change sheet generated in step 4. The output is a change sheet in PDF format that is easily accessible to the user. The user opens this with a PDF viewer on their terminal and checks the updated information.

[0261] (Application Example 1)

[0262] 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."

[0263] Traditional information management in physical stores often involved manual changes to product information and campaign details, leading to errors and wasted time. Furthermore, it was difficult for staff to accurately grasp the changes and provide appropriate guidance to customers.

[0264] 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.

[0265] In this invention, the server includes an information processing device for receiving old and new documents, a character extraction device for extracting character data from the old and new documents, and a difference detection device for comparing the character data of the old and new documents and detecting differences. This enables the rapid and accurate notification of changes to staff using mobile terminals when changes occur in documents at a physical store, and facilitates the smooth provision of information to customers.

[0266] An "information processing device for receiving old and new materials" is a device that receives materials uploaded by users and converts them into a format that can be used in subsequent processing steps.

[0267] A "character extraction device" is a device that extracts character data related to the content from received materials and organizes it as data that can be analyzed using text.

[0268] A "difference detection device" is a device that compares text data between old and new documents and has the function of detecting the differences between the two, i.e., the points of difference.

[0269] A "table generation device" is a device that organizes and classifies the detected differences and automatically generates an updated comparison table in a visually easy-to-understand format.

[0270] An "output device" is a device that transmits and displays the generated update comparison table and other information in an appropriate format to the user's terminal or display.

[0271] A "highlighting display device" is a device that visually highlights important changes and differences within a generated update comparison table, allowing users to quickly identify them.

[0272] A "portable terminal device" is a device that provides users with generated information in a portable format for use in actual work environments.

[0273] The system implementing this invention can improve the efficiency of information updates in store operations by comparing old and new documents, automatically detecting and displaying the changes contained therein.

[0274] The server first receives the old and new documents uploaded by the user via an information processing device. Text data is extracted from these documents using a character extraction device. This process utilizes Python's text analysis libraries for advanced text extraction and analysis. Next, a difference detection device compares this text data and detects the differences. Libraries such as difflib are used for difference detection.

[0275] Once all differences are detected, the table generator automatically uses this information to create an updated comparison table. This table is designed so that users can intuitively understand the changes, as the changes are color-coded using a highlighting device. The table is finally sent from the output device to the user's mobile device. On the mobile device, a React Native-based application displays this information, allowing store staff to immediately review it on-site.

[0276] For example, when a new product's price is changed, store staff upload both the old and new price lists to the app. The server detects and displays the price difference, allowing staff to quickly check the changes on their mobile devices. The system can streamline these processes by using prompts such as, "Can you upload the old and new documents and detect the changes? Please visually highlight which parts have been changed."

[0277] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0278] Step 1:

[0279] The server receives old materials and new materials from the user. When the user uploads materials using the terminal, this is input into the information processing device on the server. The input data is converted into a format suitable for subsequent processing and saved on the server.

[0280] Step 2:

[0281] The server extracts text data from the received materials using a character extraction device. Depending on the format of the materials, a Python text extraction library is used, and the content of each material is output as character data. This character data is used in the subsequent difference detection process.

[0282] Step 3:

[0283] The server uses a difference detection device to compare the character data of the old materials and the new materials. Here, the difflib library is used to collate the data and identify the different parts. The input is the extracted character data, and the output is the identification and listing of the different parts.

[0284] Step 4:

[0285] The server generates an updated comparison table based on the detected different parts through a table generation device. The difference information is input, and table-formatted data including highlighted parts is generated. The generated table is visualized so that the user can intuitively understand it.

[0286] Step 5:

[0287] The server sends the generated updated comparison table from the output device to the user's mobile terminal. Through this process, the table is displayed on the terminal via an application, and the user applies it to the work in the store. The input is the updated comparison table, and the output is the visualized table displayed on the user's terminal.

[0288] 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.

[0289] This invention combines a system that automatically detects changes associated with document updates and visually presents them to the user with a function that recognizes the user's emotions and adapts to their reactions. This system operates on a server and processes documents provided by the user's terminal.

[0290] First, the user uploads both the old and new documents to the server via their device. The server receives these documents and extracts their content using text extraction methods. The server uses generative AI models and diff algorithms to accurately detect the differences between the old and new documents. This comparison provides detailed information such as changes, additions, and deletions.

[0291] Next, the server uses an emotion engine to recognize the user's emotional state. In this process, it analyzes the user's voice and facial expression data collected from the device to estimate their emotions. The emotion engine provides information such as whether the user's emotions are positive or negative, or whether they are experiencing stress.

[0292] Based on this, the server uses a table generation mechanism to create the revised table. The revised table is a visual interface that highlights the changes, and is characterized by the fact that the highlighting color and display format can be adjusted according to the user's mood. For example, if the user is feeling stressed, the color can be changed to a more calming one.

[0293] Finally, the server sends the generated revised table to the user's terminal via an output method, and the user downloads it. The user can then check the revised table using a browser or appropriate document viewer and immediately understand the changes in the document.

[0294] As a concrete example, when a project manager uploads old and new versions of a specification document to the system, the server detects the additions and changes to the new specifications and reflects the changes in a table according to the project manager's emotional state. In this way, the burden on the user is reduced while allowing for efficient document review.

[0295] The following describes the processing flow.

[0296] Step 1:

[0297] The user uploads old and new documents to the server using their device. The user opens a browser, uses the designated interface, selects the documents by clicking the upload button, and sends them to the server.

[0298] Step 2:

[0299] The server receives the uploaded files and saves them in a temporary directory. Files received as HTTP requests are sorted into the appropriate folder according to their type.

[0300] Step 3:

[0301] The server uses text extraction methods to extract text data from the stored documents. During this process, different parsing libraries are used to obtain the text content depending on the file format, such as PDF or Word.

[0302] Step 4:

[0303] The server uses a difference detection method to compare the extracted text data of the old and new documents. A generative AI model is applied to detect changes between the two documents and classify the differences into categories such as additions, deletions, and modifications.

[0304] Step 5:

[0305] As real-time feedback from the terminal, the server acquires the user's voice and facial expression data and analyzes it with an emotion engine. For this, technologies that use the data acquired through the terminal's camera and microphone to identify the user's emotional state are used.

[0306] Step 6:

[0307] Based on the user's emotion data analyzed by the emotion engine, the server assembles a current revised form using form generation means. Here, visual adjustments such as color tones and font sizes according to the user's emotions are made, and a form in a format suitable for the user's psychological state is created.

[0308] Step 7:

[0309] The server transmits the generated current revised form to the user's terminal by means of output. The user can click on the provided download link to obtain the file and open it with a browser or PDF viewer to check the changes.

[0310] (Example 2)

[0311] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0312] The process of identifying the changes accompanying the update of the material and providing visual feedback based on it has often been performed manually in the past, and the cost of time and labor is high. Furthermore, by providing feedback according to the user's emotional state, it is required to improve the user experience, but it is difficult to efficiently achieve this with existing systems. There is a need to solve such problems.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0314] In this invention, the server includes: information processing means for receiving old and new materials; data extraction means for extracting text data from the old and new materials; data comparison means for comparing the text data of the old and new materials and detecting differences; table generation means for classifying the detected differences and generating a table for visually highlighting them; emotion recognition means for analyzing input data to analyze the user's emotional state; adjustment means for adjusting the visual display based on the user's emotional state; and output means for outputting the generated table. This makes it possible to efficiently grasp changes in materials and provide interactive feedback that corresponds to the user's emotional state.

[0315] "Information processing means" refers to devices or processes that have the function of receiving old and new data into a server.

[0316] A "data extraction means" refers to a device or process that has the function of extracting text data from received materials.

[0317] A "data comparison tool" is a device or process used to compare text data from old and new documents and detect changes or differences.

[0318] A "table generation means" is a device or process for generating a table that classifies the detected differences and visually highlights them.

[0319] "Emotion recognition means" refers to devices or processes that analyze a user's voice and facial expression data to estimate the user's emotional state.

[0320] "Adjustment means" refers to devices or processes for adjusting visual display attributes based on the user's emotional state.

[0321] "Output means" refers to a device or process that sends the generated table to the user's terminal, enabling visual confirmation.

[0322] This invention is a system that accurately detects changes resulting from updates to documents and further presents them visually in a way that is appropriate to the user's emotional state. This system runs on a server and processes information by communicating with the user's terminal.

[0323] Users select old and new documents using a terminal and upload them to the system. The server then receives these documents via information processing. Once the documents reach the server, data extraction is activated to extract the text data from the documents. The server uses a generative AI model and a differential algorithm to compare the text data between the old and new documents. This makes it possible to accurately identify the changed, added, and deleted sections.

[0324] Next, the server uses emotion recognition to understand the user's emotional state. For this purpose, voice data and facial expression data collected from the terminal are analyzed to determine whether the user is feeling positive, negative, or stressed. After identifying the emotion, the server uses a table generation mechanism to generate a current state table that highlights the detected discrepancies. The highlighting color and visual interface of this current state table are adjusted according to the user's emotional state.

[0325] Finally, the server sends the generated revised table to the user's terminal via the output method. The user can then use a browser or document viewer to review this visual interface and efficiently understand the changes in the document.

[0326] As a concrete example, when a project manager uploads an old and new version of a specification document, the server identifies the differences and, if it determines that the manager's emotions are negative, provides the revised version with a more subdued color scheme. As a result, the project manager can improve work efficiency while reducing their visual and psychological burden.

[0327] An example of a prompt message is: "Upload the old and new documents and detect the changes. Also perform sentiment analysis and display the results in a format that reflects the user's sentiment."

[0328] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0329] Step 1:

[0330] The user selects old and new documents using their terminal and uploads them to the server. The input is the file data of the old and new documents, and the output is the document data as it is stored on the server. Specifically, the user selects each file by clicking the upload button in the browser and submits it.

[0331] Step 2:

[0332] The server receives uploaded materials via information processing equipment and extracts text data from each material using data extraction equipment. The input is the material data stored on the server, and the output is the extracted text data. Specifically, the server digitally analyzes text from PDF and Word documents and converts it into a structured text format.

[0333] Step 3:

[0334] The server uses a generative AI model and data comparison tools to compare text data from old and new documents and detect differences. The input is extracted text data, and the output is difference data containing details of the differences. Specifically, the server uses natural language processing techniques to analyze sentences and paragraphs within the text and identify parts that have been added, deleted, or modified.

[0335] Step 4:

[0336] The server analyzes voice and facial expression data acquired from the user's terminal using emotion recognition technology to estimate the user's emotional state. The input is voice data and facial expression data, and the output is estimated emotional state data. Specifically, voice analysis software analyzes tone and speech patterns, and a facial recognition algorithm estimates emotions from facial expressions.

[0337] Step 5:

[0338] The server generates a revised table highlighting the differences based on the previously detected difference data and emotion state data, using a table generation mechanism. The input is the difference data and emotion state data, and the output is a visually adjusted table for distribution to the user. Specifically, if the emotion is negative, it is displayed with a softer highlight color, and other processing is performed.

[0339] Step 6:

[0340] The server sends the generated revised table to the user's terminal via an output device. The input is a visually adjusted table, and the output is data in a format viewable on the user's terminal. Specifically, the server converts the digital data into an appropriate file format and sends it to the user via the internet. The user can open this table in a browser or compatible application to check the changes in the document.

[0341] (Application Example 2)

[0342] 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."

[0343] In today's rapidly changing market environment, product placement and promotional content in physical stores are frequently updated. While it's crucial for staff to accurately and quickly grasp these changes, information overload and inappropriate communication methods can reduce work efficiency and increase employee stress. Therefore, there is a need for effective methods to recognize changes and communicate them appropriately, taking into account staff emotional states.

[0344] 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.

[0345] In this invention, the server includes data processing means for receiving old and new information, data extraction means for extracting character data from the old and new information, and difference detection means for comparing the character data and detecting difference areas. This makes it possible to quickly and appropriately communicate product and promotion change information to store staff. Furthermore, by including emotion recognition means for analyzing the emotional state of the user and display adjustment means for adjusting the display format of the generated change table, it is possible to provide information with less stress tailored to the state of individual staff members.

[0346] A "data processing means" is a means for receiving old and new information and passing them on to subsequent processing.

[0347] A "data extraction method" is a means for extracting character data from received information.

[0348] A "difference detection method" is a means of comparing the text data of old information and new information and identifying the differences between them.

[0349] The "table generation means" is a means for creating a visual change table based on the detected difference areas.

[0350] "Emotion recognition means" refers to a method for analyzing a user's emotional state and utilizing that information.

[0351] "Display adjustment means" refers to means for dynamically adjusting the display format of the generated change table based on the user's emotional state.

[0352] "Output means" refers to means for outputting the generated change sheet to a specified device.

[0353] The system for implementing the present invention mainly consists of a server, a smart device (such as smart glasses), and various software components connected thereto. Specifically, the server acts as the central component, receiving old and new information via data processing means. This information is converted into individual character data using data extraction means and compared by difference detection means. Subsequently, differences are identified and materialized as a change table by table generation means.

[0354] Users receive the contents of the change sheet via a smart device. The server analyzes the user's emotional state using emotion recognition and dynamically adjusts the color scheme and notification method of the change sheet using display adjustment mechanisms. This makes it possible to provide information efficiently while reducing user stress. Specific hardware used includes smart glasses (such as Google Glass) as an example of a smart device, and a general cloud platform is used on the server side.

[0355] Furthermore, the software utilizes OCR libraries (such as Tesseract) for text extraction, emotion recognition libraries (such as Affectiva SDK) for emotion analysis, and UI frameworks (such as OpenCV) for visual display. These components work together to support the overall operation of the system.

[0356] For example, if a physical store frequently updates its product displays, staff members wear smart glasses while patrolling the store. The server sends a change sheet generated based on the store's update information to the glasses, and presents the information in an appropriate color scheme and notification format according to the staff member's emotional state. This helps ensure that important information is not overlooked and that work proceeds smoothly. By inputting a prompt such as "Check the display placement of new products and suggest ways to reduce notification stress" into the generating AI model, the optimal method of providing information is suggested.

[0357] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0358] Step 1:

[0359] The server receives both old and new information from users. This provides the underlying dataset for information that needs updating. The received data is stored for subsequent processing.

[0360] Step 2:

[0361] The server extracts text data from the received information using data extraction methods. This process uses an OCR library (e.g., Tesseract) to convert text from paper documents and images into a digital format. As a result, the information is output in a format that can be used as text data.

[0362] Step 3:

[0363] The server compares the text data of the old and new information using a difference detection mechanism. In this step, a comparison algorithm is used to clarify the differences in the text content. The output is a dataset showing the detected differences.

[0364] Step 4:

[0365] The server generates a change table from the difference detection results using a table generation mechanism. In this process, the table is structured in a format that clearly indicates the changes as a visual interface. This provides an output that allows the user to visually confirm the changes.

[0366] Step 5:

[0367] The server analyzes the user's emotional state using emotion recognition technology. It analyzes voice and facial expression data acquired from the user's device to evaluate whether the user's emotions are positive, negative, or stressful. The output is data reflecting the user's emotional state.

[0368] Step 6:

[0369] The server adjusts the display format of the change table based on the user's emotional state using display adjustment mechanisms. Specifically, if the user is experiencing stress, the display colors and font size are changed to more calming ones, among other customizations tailored to the user.

[0370] Step 7:

[0371] The server uses an output mechanism to send the generated change sheet to the user's terminal. As a final output, a visually adjusted change sheet is presented on the user's smart device, allowing the user to review its contents and utilize them in their work.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] [Third Embodiment]

[0376] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0377] 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.

[0378] 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).

[0379] 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.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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".

[0388] This invention is a system that efficiently and automatically detects changes between old and new documents and presents them to the user in a visually easy-to-understand format. This system primarily operates on a server and processes documents provided by the user's terminal.

[0389] Users upload old and new documents to the server via their devices. The server receives these documents and extracts the content of each document using text extraction methods. This text data is then analyzed using appropriate libraries and parsers selected according to the format of the documents.

[0390] Next, the server uses difference detection methods to compare the text data of the old and new documents. Generative AI models and algorithms are used in this comparison to detect specific changes. At this stage, the locations where changes have been made, the content that has been added, and the content that has been deleted are identified and all categorized.

[0391] The server uses this differential information to create the current revised table using a table generation mechanism. This table is designed so that users can easily see the changes, and the changed parts are highlighted with color coding and annotations.

[0392] Finally, the server sends the generated revised table to the user's terminal via an output device. The user can then visually confirm the revised table using a browser or PDF viewer and quickly understand the changes in the document.

[0393] For example, if a user uploads both an old and new version of their company's product specifications to this system, the server will detect additions, changes, and deletions to the specifications and provide them to the user as a single revised specification sheet. This allows the user to easily see which parts have been changed, significantly streamlining the verification process.

[0394] The following describes the processing flow.

[0395] Step 1:

[0396] The user uploads both old and new documents to the server using their device. The user selects these documents through a file selection menu and sends them to the server by pressing the upload button.

[0397] Step 2:

[0398] The server receives the uploaded materials and saves them to storage. In this process, when a file is sent via an HTTP request, the server retrieves it and stores it in a temporary storage directory.

[0399] Step 3:

[0400] The server uses a text extraction method to extract text data from the stored documents. In this process, if the document is a PDF file, it uses the PDF extraction library; if it's a Word file, it uses the Word extraction library to extract the document content in text format.

[0401] Step 4:

[0402] The server uses difference detection means to compare the extracted text data of the old and new documents. In this process, a generative AI model or difference algorithm is applied to detect the differences between the two documents. The focus is mainly on identifying changes, additions, and deletions.

[0403] Step 5:

[0404] The server creates a current revision table using a table generation mechanism based on the detected difference information. This table clearly displays the detected differences, and changes are highlighted in color, making it easy for the user to review them.

[0405] Step 6:

[0406] The server outputs the completed revised table and sends it to the user's terminal. In this step, the server provides the generated revised table as an HTTP response and presents the user with a download link.

[0407] Step 7:

[0408] The user uses their terminal to download the current revised table provided by the server and check its contents. The user then opens the table using a browser or appropriate document viewer to visually understand the changes.

[0409] (Example 1)

[0410] 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."

[0411] When multiple pieces of information are updated, there is a need for a system that can efficiently detect these changes and present them in a visually easy-to-understand format. However, existing systems require manual verification of information changes, which is time-consuming and labor-intensive. Furthermore, there are insufficient means to present changes in an integrated manner, making it difficult to provide information in a way that users can easily understand.

[0412] 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.

[0413] In this invention, the server includes information processing means for receiving old and new information; data extraction means for extracting character data from the old and new information; difference detection means for comparing the character data of the old and new information and detecting the differences; table generation means for classifying the detected differences and generating a change table; output means for outputting the generated change table; and generation means for generating prompt statements that highlight the differences using a generation AI model and performing correspondence based on them. This allows users to quickly grasp the updated parts of the information. In addition, changes are color-coded and visually highlighted, providing intuitive understanding and significantly improving the efficiency of the verification process.

[0414] "Information processing means" refers to means that have the function of receiving old and new information and processing that information appropriately.

[0415] A "data extraction means" is a means that has the function of extracting character data from received information and converting it into the required format.

[0416] A "difference detection means" is a means that has the function of comparing extracted character data and identifying the parts that are different.

[0417] The "table generation means" is a means that has the function of classifying the detected different parts and generating a change table that can be visually confirmed.

[0418] "Output means" refers to means that have the function of sending the generated change sheet to the user's terminal and outputting it so that the user can confirm it.

[0419] A "generative AI model" is an artificial intelligence model used to explicitly indicate different parts based on a given prompt statement.

[0420] A "prompt statement" is a statement used to give specific instructions to a generative AI model, and in this invention, it is used to clarify different parts.

[0421] This invention provides a system that efficiently detects updated information and presents it to the user in a visually easy-to-understand format. The following describes specific embodiments for implementing this system.

[0422] The user selects the old and new information they want to compare from their device and uploads it to the server. The server receives this information and extracts the text data using appropriate data extraction methods depending on the format, such as PDF or Word document. Specifically, a PDF parser or document reading library can be used.

[0423] The server utilizes a generative AI model as a means of detecting differences, using prompts to identify the differences. An example of such a prompt is an instruction such as, "Identify the differences between the old and new information and clearly indicate them." Based on the generated prompts, the AI ​​model compares the old and new information and clearly indicates the differences.

[0424] Next, the server classifies the detected differences and uses a table generation mechanism to create a visually easy-to-understand change table. In this change table, text changes are color-coded, ensuring that users can intuitively understand the changes.

[0425] Finally, the server sends the generated change sheet to the user's terminal via an output device. The user can then review the change sheet on their terminal using a PDF viewer or similar software, allowing them to quickly and accurately understand the updated information.

[0426] For example, if a user uploads an old version of a business manual along with a new version, the server analyzes each section of the manual and generates a change list indicating which parts have been updated. Sections that have been changed are marked with a yellow marker, allowing users to see at a glance where the changes are.

[0427] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0428] Step 1:

[0429] The user uses a terminal to select old and new information and upload it to the server. The input is information files in PDF or Word format. The server receives these files and saves them to the specified folder.

[0430] Step 2:

[0431] The server uses a data extraction method to extract character data from the received file. The input is the information file received in step 1. For data extraction, a PDF parser or document reading library is used to convert the character data into text format and output it.

[0432] Step 3:

[0433] The server uses a generative AI model to compare the text data of the old and new information. The input is the text data converted in step 2. The generative AI model is given the instruction, "Identify the differences between the old and new information and clearly indicate them," as a prompt. The AI ​​model detects the differences and identifies the parts that are different. Based on this, it outputs data on the changes.

[0434] Step 4:

[0435] The server classifies the difference data and creates a change table using a table generation mechanism. The input is the difference data generated in step 3. Based on the difference data, the server generates and outputs a visually easy-to-understand table with changes color-coded.

[0436] Step 5:

[0437] The server sends the generated change sheet to the user's terminal via an output method. The input is the change sheet generated in step 4. The output is a change sheet in PDF format that is easily accessible to the user. The user opens this with a PDF viewer on their terminal and checks the updated information.

[0438] (Application Example 1)

[0439] 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."

[0440] Traditional information management in physical stores often involved manual changes to product information and campaign details, leading to errors and wasted time. Furthermore, it was difficult for staff to accurately grasp the changes and provide appropriate guidance to customers.

[0441] 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.

[0442] In this invention, the server includes an information processing device for receiving old and new documents, a character extraction device for extracting character data from the old and new documents, and a difference detection device for comparing the character data of the old and new documents and detecting differences. This enables the rapid and accurate notification of changes to staff using mobile terminals when changes occur in documents at a physical store, and facilitates the smooth provision of information to customers.

[0443] An "information processing device for receiving old and new materials" is a device that receives materials uploaded by users and converts them into a format that can be used in subsequent processing steps.

[0444] A "character extraction device" is a device that extracts character data related to the content from received materials and organizes it as data that can be analyzed using text.

[0445] A "difference detection device" is a device that compares text data between old and new documents and has the function of detecting the differences between the two, i.e., the points of difference.

[0446] A "table generation device" is a device that organizes and classifies the detected differences and automatically generates an updated comparison table in a visually easy-to-understand format.

[0447] An "output device" is a device that transmits and displays the generated update comparison table and other information in an appropriate format to the user's terminal or display.

[0448] A "highlighting display device" is a device that visually highlights important changes and differences within a generated update comparison table, allowing users to quickly identify them.

[0449] A "portable terminal device" is a device that provides users with generated information in a portable format for use in actual work environments.

[0450] The system implementing this invention can improve the efficiency of information updates in store operations by comparing old and new documents, automatically detecting and displaying the changes contained therein.

[0451] The server first receives the old and new documents uploaded by the user via an information processing device. Text data is extracted from these documents using a character extraction device. This process utilizes Python's text analysis libraries for advanced text extraction and analysis. Next, a difference detection device compares this text data and detects the differences. Libraries such as difflib are used for difference detection.

[0452] Once all differences are detected, the table generator automatically uses this information to create an updated comparison table. This table is designed so that users can intuitively understand the changes, as the changes are color-coded using a highlighting device. The table is finally sent from the output device to the user's mobile device. On the mobile device, a React Native-based application displays this information, allowing store staff to immediately review it on-site.

[0453] For example, when a new product's price is changed, store staff upload both the old and new price lists to the app. The server detects and displays the price difference, allowing staff to quickly check the changes on their mobile devices. The system can streamline these processes by using prompts such as, "Can you upload the old and new documents and detect the changes? Please visually highlight which parts have been changed."

[0454] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0455] Step 1:

[0456] The server receives both old and new documents from users. When a user uploads documents using a terminal, these are input into the information processing device on the server. The input data is converted into a format suitable for subsequent processing and stored on the server.

[0457] Step 2:

[0458] The server extracts text data from the received documents using a character extraction device. Depending on the format of the documents, a Python text extraction library is used, and the content of each document is output as character data. This character data is used in the subsequent difference detection process.

[0459] Step 3:

[0460] The server uses a difference detection device to compare the text data of the old and new documents. Here, the difflib library is used to compare the data and identify the differences. The input is the extracted text data, and the output is information identifying and listing the differences.

[0461] Step 4:

[0462] The server generates an updated comparison table based on the detected differences via a table generator. Difference information is input, and tabular data including highlighted sections is generated. The generated table is visualized in a way that is intuitively understandable to the user.

[0463] Step 5:

[0464] The server sends the generated update comparison table from the output device to the user's mobile device. This process allows the table to be displayed on the device via an application, which the user then applies to their work within the store. The input is the update comparison table, and the output is a visualized table displayed on the user's device.

[0465] 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.

[0466] This invention combines a system that automatically detects changes associated with document updates and visually presents them to the user with a function that recognizes the user's emotions and adapts to their reactions. This system operates on a server and processes documents provided by the user's terminal.

[0467] First, the user uploads both the old and new documents to the server via their device. The server receives these documents and extracts their content using text extraction methods. The server uses generative AI models and diff algorithms to accurately detect the differences between the old and new documents. This comparison provides detailed information such as changes, additions, and deletions.

[0468] Next, the server uses an emotion engine to recognize the user's emotional state. In this process, it analyzes the user's voice and facial expression data collected from the device to estimate their emotions. The emotion engine provides information such as whether the user's emotions are positive or negative, or whether they are experiencing stress.

[0469] Based on this, the server uses a table generation mechanism to create the revised table. The revised table is a visual interface that highlights the changes, and is characterized by the fact that the highlighting color and display format can be adjusted according to the user's mood. For example, if the user is feeling stressed, the color can be changed to a more calming one.

[0470] Finally, the server sends the generated revised table to the user's terminal via an output method, and the user downloads it. The user can then check the revised table using a browser or appropriate document viewer and immediately understand the changes in the document.

[0471] As a concrete example, when a project manager uploads old and new versions of a specification document to the system, the server detects the additions and changes to the new specifications and reflects the changes in a table according to the project manager's emotional state. In this way, the burden on the user is reduced while allowing for efficient document review.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] The user uploads old and new documents to the server using their device. The user opens a browser, uses the designated interface, selects the documents by clicking the upload button, and sends them to the server.

[0475] Step 2:

[0476] The server receives the uploaded files and saves them in a temporary directory. Files received as HTTP requests are sorted into the appropriate folder according to their type.

[0477] Step 3:

[0478] The server uses text extraction methods to extract text data from the stored documents. During this process, different parsing libraries are used to obtain the text content depending on the file format, such as PDF or Word.

[0479] Step 4:

[0480] The server uses a difference detection method to compare the extracted text data of the old and new documents. A generative AI model is applied to detect changes between the two documents and classify the differences into categories such as additions, deletions, and modifications.

[0481] Step 5:

[0482] The server acquires user voice and facial expression data as real-time feedback from the device and analyzes it using an emotion engine. This involves using data acquired through the device's camera and microphone to identify the user's emotional state.

[0483] Step 6:

[0484] Based on the user's emotional data analyzed by the emotion engine, the server assembles a table using a table generation mechanism. Here, visual adjustments such as color scheme and font size are made according to the user's emotions, creating a table in a format suitable for the user's psychological state.

[0485] Step 7:

[0486] The server sends the generated revised table to the user's terminal via an output method. The user can then download the file by clicking the provided download link and open it in a browser or PDF viewer to check the changes.

[0487] (Example 2)

[0488] 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."

[0489] The process of identifying changes when updating documents and providing visual feedback based on those changes is often done manually, resulting in high time and labor costs. Furthermore, there is a need to improve the user experience by providing feedback that responds to the user's emotional state, but existing systems make it difficult to achieve this efficiently. These challenges need to be addressed.

[0490] 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.

[0491] In this invention, the server includes: information processing means for receiving old and new materials; data extraction means for extracting text data from the old and new materials; data comparison means for comparing the text data of the old and new materials and detecting differences; table generation means for classifying the detected differences and generating a table for visually highlighting them; emotion recognition means for analyzing input data to analyze the user's emotional state; adjustment means for adjusting the visual display based on the user's emotional state; and output means for outputting the generated table. This makes it possible to efficiently grasp changes in materials and provide interactive feedback that corresponds to the user's emotional state.

[0492] "Information processing means" refers to devices or processes that have the function of receiving old and new data into a server.

[0493] A "data extraction means" refers to a device or process that has the function of extracting text data from received materials.

[0494] A "data comparison tool" is a device or process used to compare text data from old and new documents and detect changes or differences.

[0495] A "table generation means" is a device or process for generating a table that classifies the detected differences and visually highlights them.

[0496] "Emotion recognition means" refers to devices or processes that analyze a user's voice and facial expression data to estimate the user's emotional state.

[0497] "Adjustment means" refers to devices or processes for adjusting visual display attributes based on the user's emotional state.

[0498] "Output means" refers to a device or process that sends the generated table to the user's terminal, enabling visual confirmation.

[0499] This invention is a system that accurately detects changes resulting from updates to documents and further presents them visually in a way that is appropriate to the user's emotional state. This system runs on a server and processes information by communicating with the user's terminal.

[0500] Users select old and new documents using a terminal and upload them to the system. The server then receives these documents via information processing. Once the documents reach the server, data extraction is activated to extract the text data from the documents. The server uses a generative AI model and a differential algorithm to compare the text data between the old and new documents. This makes it possible to accurately identify the changed, added, and deleted sections.

[0501] Next, the server uses emotion recognition to understand the user's emotional state. For this purpose, voice data and facial expression data collected from the terminal are analyzed to determine whether the user is feeling positive, negative, or stressed. After identifying the emotion, the server uses a table generation mechanism to generate a current state table that highlights the detected discrepancies. The highlighting color and visual interface of this current state table are adjusted according to the user's emotional state.

[0502] Finally, the server sends the generated revised table to the user's terminal via the output method. The user can then use a browser or document viewer to review this visual interface and efficiently understand the changes in the document.

[0503] As a concrete example, when a project manager uploads an old and new version of a specification document, the server identifies the differences and, if it determines that the manager's emotions are negative, provides the revised version with a more subdued color scheme. As a result, the project manager can improve work efficiency while reducing their visual and psychological burden.

[0504] An example of a prompt message is: "Upload the old and new documents and detect the changes. Also perform sentiment analysis and display the results in a format that reflects the user's sentiment."

[0505] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0506] Step 1:

[0507] The user selects old and new documents using their terminal and uploads them to the server. The input is the file data of the old and new documents, and the output is the document data as it is stored on the server. Specifically, the user selects each file by clicking the upload button in the browser and submits it.

[0508] Step 2:

[0509] The server receives uploaded materials via information processing equipment and extracts text data from each material using data extraction equipment. The input is the material data stored on the server, and the output is the extracted text data. Specifically, the server digitally analyzes text from PDF and Word documents and converts it into a structured text format.

[0510] Step 3:

[0511] The server uses a generative AI model and data comparison tools to compare text data from old and new documents and detect differences. The input is extracted text data, and the output is difference data containing details of the differences. Specifically, the server uses natural language processing techniques to analyze sentences and paragraphs within the text and identify parts that have been added, deleted, or modified.

[0512] Step 4:

[0513] The server analyzes voice and facial expression data acquired from the user's terminal using emotion recognition technology to estimate the user's emotional state. The input is voice data and facial expression data, and the output is estimated emotional state data. Specifically, voice analysis software analyzes tone and speech patterns, and a facial recognition algorithm estimates emotions from facial expressions.

[0514] Step 5:

[0515] The server generates a revised table highlighting the differences based on the previously detected difference data and emotion state data, using a table generation mechanism. The input is the difference data and emotion state data, and the output is a visually adjusted table for distribution to the user. Specifically, if the emotion is negative, it is displayed with a softer highlight color, and other processing is performed.

[0516] Step 6:

[0517] The server sends the generated revised table to the user's terminal via an output device. The input is a visually adjusted table, and the output is data in a format viewable on the user's terminal. Specifically, the server converts the digital data into an appropriate file format and sends it to the user via the internet. The user can open this table in a browser or compatible application to check the changes in the document.

[0518] (Application Example 2)

[0519] 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."

[0520] In today's rapidly changing market environment, product placement and promotional content in physical stores are frequently updated. While it's crucial for staff to accurately and quickly grasp these changes, information overload and inappropriate communication methods can reduce work efficiency and increase employee stress. Therefore, there is a need for effective methods to recognize changes and communicate them appropriately, taking into account staff emotional states.

[0521] 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.

[0522] In this invention, the server includes data processing means for receiving old and new information, data extraction means for extracting character data from the old and new information, and difference detection means for comparing the character data and detecting difference areas. This makes it possible to quickly and appropriately communicate product and promotion change information to store staff. Furthermore, by including emotion recognition means for analyzing the emotional state of the user and display adjustment means for adjusting the display format of the generated change table, it is possible to provide information with less stress tailored to the state of individual staff members.

[0523] A "data processing means" is a means for receiving old and new information and passing them on to subsequent processing.

[0524] A "data extraction method" is a means for extracting character data from received information.

[0525] A "difference detection method" is a means of comparing the text data of old information and new information and identifying the differences between them.

[0526] The "table generation means" is a means for creating a visual change table based on the detected difference areas.

[0527] "Emotion recognition means" refers to a method for analyzing a user's emotional state and utilizing that information.

[0528] "Display adjustment means" refers to means for dynamically adjusting the display format of the generated change table based on the user's emotional state.

[0529] "Output means" refers to means for outputting the generated change sheet to a specified device.

[0530] The system for implementing the present invention mainly consists of a server, a smart device (such as smart glasses), and various software components connected thereto. Specifically, the server acts as the central component, receiving old and new information via data processing means. This information is converted into individual character data using data extraction means and compared by difference detection means. Subsequently, differences are identified and materialized as a change table by table generation means.

[0531] Users receive the contents of the change sheet via a smart device. The server analyzes the user's emotional state using emotion recognition and dynamically adjusts the color scheme and notification method of the change sheet using display adjustment mechanisms. This makes it possible to provide information efficiently while reducing user stress. Specific hardware used includes smart glasses (such as Google Glass) as an example of a smart device, and a general cloud platform is used on the server side.

[0532] Furthermore, the software utilizes OCR libraries (such as Tesseract) for text extraction, emotion recognition libraries (such as Affectiva SDK) for emotion analysis, and UI frameworks (such as OpenCV) for visual display. These components work together to support the overall operation of the system.

[0533] For example, if a physical store frequently updates its product displays, staff members wear smart glasses while patrolling the store. The server sends a change sheet generated based on the store's update information to the glasses, and presents the information in an appropriate color scheme and notification format according to the staff member's emotional state. This helps ensure that important information is not overlooked and that work proceeds smoothly. By inputting a prompt such as "Check the display placement of new products and suggest ways to reduce notification stress" into the generating AI model, the optimal method of providing information is suggested.

[0534] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0535] Step 1:

[0536] The server receives both old and new information from users. This provides the underlying dataset for information that needs updating. The received data is stored for subsequent processing.

[0537] Step 2:

[0538] The server extracts text data from the received information using data extraction methods. This process uses an OCR library (e.g., Tesseract) to convert text from paper documents and images into a digital format. As a result, the information is output in a format that can be used as text data.

[0539] Step 3:

[0540] The server compares the text data of the old and new information using a difference detection mechanism. In this step, a comparison algorithm is used to clarify the differences in the text content. The output is a dataset showing the detected differences.

[0541] Step 4:

[0542] The server generates a change table from the difference detection results using a table generation mechanism. In this process, the table is structured in a format that clearly indicates the changes as a visual interface. This provides an output that allows the user to visually confirm the changes.

[0543] Step 5:

[0544] The server analyzes the user's emotional state using emotion recognition technology. It analyzes voice and facial expression data acquired from the user's device to evaluate whether the user's emotions are positive, negative, or stressful. The output is data reflecting the user's emotional state.

[0545] Step 6:

[0546] The server adjusts the display format of the change table based on the user's emotional state using display adjustment mechanisms. Specifically, if the user is experiencing stress, the display colors and font size are changed to more calming ones, among other customizations tailored to the user.

[0547] Step 7:

[0548] The server uses an output mechanism to send the generated change sheet to the user's terminal. As a final output, a visually adjusted change sheet is presented on the user's smart device, allowing the user to review its contents and utilize them in their work.

[0549] 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.

[0550] 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.

[0551] 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.

[0552] [Fourth Embodiment]

[0553] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0554] 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.

[0555] 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).

[0556] 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.

[0557] 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.

[0558] 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).

[0559] 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.

[0560] 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.

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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".

[0566] This invention is a system that efficiently and automatically detects changes between old and new documents and presents them to the user in a visually easy-to-understand format. This system primarily operates on a server and processes documents provided by the user's terminal.

[0567] Users upload old and new documents to the server via their devices. The server receives these documents and extracts the content of each document using text extraction methods. This text data is then analyzed using appropriate libraries and parsers selected according to the format of the documents.

[0568] Next, the server uses difference detection methods to compare the text data of the old and new documents. Generative AI models and algorithms are used in this comparison to detect specific changes. At this stage, the locations where changes have been made, the content that has been added, and the content that has been deleted are identified and all categorized.

[0569] The server uses this differential information to create the current revised table using a table generation mechanism. This table is designed so that users can easily see the changes, and the changed parts are highlighted with color coding and annotations.

[0570] Finally, the server sends the generated revised table to the user's terminal via an output device. The user can then visually confirm the revised table using a browser or PDF viewer and quickly understand the changes in the document.

[0571] For example, if a user uploads both an old and new version of their company's product specifications to this system, the server will detect additions, changes, and deletions to the specifications and provide them to the user as a single revised specification sheet. This allows the user to easily see which parts have been changed, significantly streamlining the verification process.

[0572] The following describes the processing flow.

[0573] Step 1:

[0574] The user uploads both old and new documents to the server using their device. The user selects these documents through a file selection menu and sends them to the server by pressing the upload button.

[0575] Step 2:

[0576] The server receives the uploaded materials and saves them to storage. In this process, when a file is sent via an HTTP request, the server retrieves it and stores it in a temporary storage directory.

[0577] Step 3:

[0578] The server uses a text extraction method to extract text data from the stored documents. In this process, if the document is a PDF file, it uses the PDF extraction library; if it's a Word file, it uses the Word extraction library to extract the document content in text format.

[0579] Step 4:

[0580] The server uses difference detection means to compare the extracted text data of the old and new documents. In this process, a generative AI model or difference algorithm is applied to detect the differences between the two documents. The focus is mainly on identifying changes, additions, and deletions.

[0581] Step 5:

[0582] The server creates a current revision table using a table generation mechanism based on the detected difference information. This table clearly displays the detected differences, and changes are highlighted in color, making it easy for the user to review them.

[0583] Step 6:

[0584] The server outputs the completed revised table and sends it to the user's terminal. In this step, the server provides the generated revised table as an HTTP response and presents the user with a download link.

[0585] Step 7:

[0586] The user uses their terminal to download the current revised table provided by the server and check its contents. The user then opens the table using a browser or appropriate document viewer to visually understand the changes.

[0587] (Example 1)

[0588] 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".

[0589] When multiple pieces of information are updated, there is a need for a system that can efficiently detect these changes and present them in a visually easy-to-understand format. However, existing systems require manual verification of information changes, which is time-consuming and labor-intensive. Furthermore, there are insufficient means to present changes in an integrated manner, making it difficult to provide information in a way that users can easily understand.

[0590] 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.

[0591] In this invention, the server includes information processing means for receiving old and new information; data extraction means for extracting character data from the old and new information; difference detection means for comparing the character data of the old and new information and detecting the differences; table generation means for classifying the detected differences and generating a change table; output means for outputting the generated change table; and generation means for generating prompt statements that highlight the differences using a generation AI model and performing correspondence based on them. This allows users to quickly grasp the updated parts of the information. In addition, changes are color-coded and visually highlighted, providing intuitive understanding and significantly improving the efficiency of the verification process.

[0592] "Information processing means" refers to means that have the function of receiving old and new information and processing that information appropriately.

[0593] A "data extraction means" is a means that has the function of extracting character data from received information and converting it into the required format.

[0594] A "difference detection means" is a means that has the function of comparing extracted character data and identifying the parts that are different.

[0595] The "table generation means" is a means that has the function of classifying the detected different parts and generating a change table that can be visually confirmed.

[0596] "Output means" refers to means that have the function of sending the generated change sheet to the user's terminal and outputting it so that the user can confirm it.

[0597] A "generative AI model" is an artificial intelligence model used to explicitly indicate different parts based on a given prompt statement.

[0598] A "prompt statement" is a statement used to give specific instructions to a generative AI model, and in this invention, it is used to clarify different parts.

[0599] This invention provides a system that efficiently detects updated information and presents it to the user in a visually easy-to-understand format. The following describes specific embodiments for implementing this system.

[0600] The user selects the old and new information they want to compare from their device and uploads it to the server. The server receives this information and extracts the text data using appropriate data extraction methods depending on the format, such as PDF or Word document. Specifically, a PDF parser or document reading library can be used.

[0601] The server utilizes a generative AI model as a means of detecting differences, using prompts to identify the differences. An example of such a prompt is an instruction such as, "Identify the differences between the old and new information and clearly indicate them." Based on the generated prompts, the AI ​​model compares the old and new information and clearly indicates the differences.

[0602] Next, the server classifies the detected differences and uses a table generation mechanism to create a visually easy-to-understand change table. In this change table, text changes are color-coded, ensuring that users can intuitively understand the changes.

[0603] Finally, the server sends the generated change sheet to the user's terminal via an output device. The user can then review the change sheet on their terminal using a PDF viewer or similar software, allowing them to quickly and accurately understand the updated information.

[0604] For example, if a user uploads an old version of a business manual along with a new version, the server analyzes each section of the manual and generates a change list indicating which parts have been updated. Sections that have been changed are marked with a yellow marker, allowing users to see at a glance where the changes are.

[0605] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0606] Step 1:

[0607] The user uses a terminal to select old and new information and upload it to the server. The input is information files in PDF or Word format. The server receives these files and saves them to the specified folder.

[0608] Step 2:

[0609] The server uses a data extraction method to extract character data from the received file. The input is the information file received in step 1. For data extraction, a PDF parser or document reading library is used to convert the character data into text format and output it.

[0610] Step 3:

[0611] The server uses a generative AI model to compare the text data of the old and new information. The input is the text data converted in step 2. The generative AI model is given the instruction, "Identify the differences between the old and new information and clearly indicate them," as a prompt. The AI ​​model detects the differences and identifies the parts that are different. Based on this, it outputs data on the changes.

[0612] Step 4:

[0613] The server classifies the difference data and creates a change table using a table generation mechanism. The input is the difference data generated in step 3. Based on the difference data, the server generates and outputs a visually easy-to-understand table with changes color-coded.

[0614] Step 5:

[0615] The server sends the generated change sheet to the user's terminal via an output method. The input is the change sheet generated in step 4. The output is a change sheet in PDF format that is easily accessible to the user. The user opens this with a PDF viewer on their terminal and checks the updated information.

[0616] (Application Example 1)

[0617] 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".

[0618] Traditional information management in physical stores often involved manual changes to product information and campaign details, leading to errors and wasted time. Furthermore, it was difficult for staff to accurately grasp the changes and provide appropriate guidance to customers.

[0619] 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.

[0620] In this invention, the server includes an information processing device for receiving old and new documents, a character extraction device for extracting character data from the old and new documents, and a difference detection device for comparing the character data of the old and new documents and detecting differences. This enables the rapid and accurate notification of changes to staff using mobile terminals when changes occur in documents at a physical store, and facilitates the smooth provision of information to customers.

[0621] An "information processing device for receiving old and new materials" is a device that receives materials uploaded by users and converts them into a format that can be used in subsequent processing steps.

[0622] A "character extraction device" is a device that extracts character data related to the content from received materials and organizes it as data that can be analyzed using text.

[0623] A "difference detection device" is a device that compares text data between old and new documents and has the function of detecting the differences between the two, i.e., the points of difference.

[0624] A "table generation device" is a device that organizes and classifies the detected differences and automatically generates an updated comparison table in a visually easy-to-understand format.

[0625] An "output device" is a device that transmits and displays the generated update comparison table and other information in an appropriate format to the user's terminal or display.

[0626] A "highlighting display device" is a device that visually highlights important changes and differences within a generated update comparison table, allowing users to quickly identify them.

[0627] A "portable terminal device" is a device that provides users with generated information in a portable format for use in actual work environments.

[0628] The system implementing this invention can improve the efficiency of information updates in store operations by comparing old and new documents, automatically detecting and displaying the changes contained therein.

[0629] The server first receives the old and new documents uploaded by the user via an information processing device. Text data is extracted from these documents using a character extraction device. This process utilizes Python's text analysis libraries for advanced text extraction and analysis. Next, a difference detection device compares this text data and detects the differences. Libraries such as difflib are used for difference detection.

[0630] Once all differences are detected, the table generator automatically uses this information to create an updated comparison table. This table is designed so that users can intuitively understand the changes, as the changes are color-coded using a highlighting device. The table is finally sent from the output device to the user's mobile device. On the mobile device, a React Native-based application displays this information, allowing store staff to immediately review it on-site.

[0631] For example, when a new product's price is changed, store staff upload both the old and new price lists to the app. The server detects and displays the price difference, allowing staff to quickly check the changes on their mobile devices. The system can streamline these processes by using prompts such as, "Can you upload the old and new documents and detect the changes? Please visually highlight which parts have been changed."

[0632] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0633] Step 1:

[0634] The server receives both old and new documents from users. When a user uploads documents using a terminal, these are input into the information processing device on the server. The input data is converted into a format suitable for subsequent processing and stored on the server.

[0635] Step 2:

[0636] The server extracts text data from the received documents using a character extraction device. Depending on the format of the documents, a Python text extraction library is used, and the content of each document is output as character data. This character data is used in the subsequent difference detection process.

[0637] Step 3:

[0638] The server uses a difference detection device to compare the text data of the old and new documents. Here, the difflib library is used to compare the data and identify the differences. The input is the extracted text data, and the output is information identifying and listing the differences.

[0639] Step 4:

[0640] The server generates an updated comparison table based on the detected differences via a table generator. Difference information is input, and tabular data including highlighted sections is generated. The generated table is visualized in a way that is intuitively understandable to the user.

[0641] Step 5:

[0642] The server sends the generated update comparison table from the output device to the user's mobile device. This process allows the table to be displayed on the device via an application, which the user then applies to their work within the store. The input is the update comparison table, and the output is a visualized table displayed on the user's device.

[0643] 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.

[0644] This invention combines a system that automatically detects changes associated with document updates and visually presents them to the user with a function that recognizes the user's emotions and adapts to their reactions. This system operates on a server and processes documents provided by the user's terminal.

[0645] First, the user uploads both the old and new documents to the server via their device. The server receives these documents and extracts their content using text extraction methods. The server uses generative AI models and diff algorithms to accurately detect the differences between the old and new documents. This comparison provides detailed information such as changes, additions, and deletions.

[0646] Next, the server uses an emotion engine to recognize the user's emotional state. In this process, it analyzes the user's voice and facial expression data collected from the device to estimate their emotions. The emotion engine provides information such as whether the user's emotions are positive or negative, or whether they are experiencing stress.

[0647] Based on this, the server uses a table generation mechanism to create the revised table. The revised table is a visual interface that highlights the changes, and is characterized by the fact that the highlighting color and display format can be adjusted according to the user's mood. For example, if the user is feeling stressed, the color can be changed to a more calming one.

[0648] Finally, the server sends the generated revised table to the user's terminal via an output method, and the user downloads it. The user can then check the revised table using a browser or appropriate document viewer and immediately understand the changes in the document.

[0649] As a concrete example, when a project manager uploads old and new versions of a specification document to the system, the server detects the additions and changes to the new specifications and reflects the changes in a table according to the project manager's emotional state. In this way, the burden on the user is reduced while allowing for efficient document review.

[0650] The following describes the processing flow.

[0651] Step 1:

[0652] The user uploads old and new documents to the server using their device. The user opens a browser, uses the designated interface, selects the documents by clicking the upload button, and sends them to the server.

[0653] Step 2:

[0654] The server receives the uploaded files and saves them in a temporary directory. Files received as HTTP requests are sorted into the appropriate folder according to their type.

[0655] Step 3:

[0656] The server uses text extraction methods to extract text data from the stored documents. During this process, different parsing libraries are used to obtain the text content depending on the file format, such as PDF or Word.

[0657] Step 4:

[0658] The server uses a difference detection method to compare the extracted text data of the old and new documents. A generative AI model is applied to detect changes between the two documents and classify the differences into categories such as additions, deletions, and modifications.

[0659] Step 5:

[0660] The server acquires user voice and facial expression data as real-time feedback from the device and analyzes it using an emotion engine. This involves using data acquired through the device's camera and microphone to identify the user's emotional state.

[0661] Step 6:

[0662] Based on the user's emotional data analyzed by the emotion engine, the server assembles a table using a table generation mechanism. Here, visual adjustments such as color scheme and font size are made according to the user's emotions, creating a table in a format suitable for the user's psychological state.

[0663] Step 7:

[0664] The server sends the generated revised table to the user's terminal via an output method. The user can then download the file by clicking the provided download link and open it in a browser or PDF viewer to check the changes.

[0665] (Example 2)

[0666] 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".

[0667] The process of identifying changes when updating documents and providing visual feedback based on those changes is often done manually, resulting in high time and labor costs. Furthermore, there is a need to improve the user experience by providing feedback that responds to the user's emotional state, but existing systems make it difficult to achieve this efficiently. These challenges need to be addressed.

[0668] 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.

[0669] In this invention, the server includes: information processing means for receiving old and new materials; data extraction means for extracting text data from the old and new materials; data comparison means for comparing the text data of the old and new materials and detecting differences; table generation means for classifying the detected differences and generating a table for visually highlighting them; emotion recognition means for analyzing input data to analyze the user's emotional state; adjustment means for adjusting the visual display based on the user's emotional state; and output means for outputting the generated table. This makes it possible to efficiently grasp changes in materials and provide interactive feedback that corresponds to the user's emotional state.

[0670] "Information processing means" refers to devices or processes that have the function of receiving old and new data into a server.

[0671] A "data extraction means" refers to a device or process that has the function of extracting text data from received materials.

[0672] A "data comparison tool" is a device or process used to compare text data from old and new documents and detect changes or differences.

[0673] A "table generation means" is a device or process for generating a table that classifies the detected differences and visually highlights them.

[0674] "Emotion recognition means" refers to devices or processes that analyze a user's voice and facial expression data to estimate the user's emotional state.

[0675] "Adjustment means" refers to devices or processes for adjusting visual display attributes based on the user's emotional state.

[0676] "Output means" refers to a device or process that sends the generated table to the user's terminal, enabling visual confirmation.

[0677] This invention is a system that accurately detects changes resulting from updates to documents and further presents them visually in a way that is appropriate to the user's emotional state. This system runs on a server and processes information by communicating with the user's terminal.

[0678] Users select old and new documents using a terminal and upload them to the system. The server then receives these documents via information processing. Once the documents reach the server, data extraction is activated to extract the text data from the documents. The server uses a generative AI model and a differential algorithm to compare the text data between the old and new documents. This makes it possible to accurately identify the changed, added, and deleted sections.

[0679] Next, the server uses emotion recognition to understand the user's emotional state. For this purpose, voice data and facial expression data collected from the terminal are analyzed to determine whether the user is feeling positive, negative, or stressed. After identifying the emotion, the server uses a table generation mechanism to generate a current state table that highlights the detected discrepancies. The highlighting color and visual interface of this current state table are adjusted according to the user's emotional state.

[0680] Finally, the server sends the generated revised table to the user's terminal via the output method. The user can then use a browser or document viewer to review this visual interface and efficiently understand the changes in the document.

[0681] As a concrete example, when a project manager uploads an old and new version of a specification document, the server identifies the differences and, if it determines that the manager's emotions are negative, provides the revised version with a more subdued color scheme. As a result, the project manager can improve work efficiency while reducing their visual and psychological burden.

[0682] An example of a prompt message is: "Upload the old and new documents and detect the changes. Also perform sentiment analysis and display the results in a format that reflects the user's sentiment."

[0683] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0684] Step 1:

[0685] The user selects old and new documents using their terminal and uploads them to the server. The input is the file data of the old and new documents, and the output is the document data as it is stored on the server. Specifically, the user selects each file by clicking the upload button in the browser and submits it.

[0686] Step 2:

[0687] The server receives uploaded materials via information processing equipment and extracts text data from each material using data extraction equipment. The input is the material data stored on the server, and the output is the extracted text data. Specifically, the server digitally analyzes text from PDF and Word documents and converts it into a structured text format.

[0688] Step 3:

[0689] The server uses a generative AI model and data comparison tools to compare text data from old and new documents and detect differences. The input is extracted text data, and the output is difference data containing details of the differences. Specifically, the server uses natural language processing techniques to analyze sentences and paragraphs within the text and identify parts that have been added, deleted, or modified.

[0690] Step 4:

[0691] The server analyzes voice and facial expression data acquired from the user's terminal using emotion recognition technology to estimate the user's emotional state. The input is voice data and facial expression data, and the output is estimated emotional state data. Specifically, voice analysis software analyzes tone and speech patterns, and a facial recognition algorithm estimates emotions from facial expressions.

[0692] Step 5:

[0693] The server generates a revised table highlighting the differences based on the previously detected difference data and emotion state data, using a table generation mechanism. The input is the difference data and emotion state data, and the output is a visually adjusted table for distribution to the user. Specifically, if the emotion is negative, it is displayed with a softer highlight color, and other processing is performed.

[0694] Step 6:

[0695] The server sends the generated revised table to the user's terminal via an output device. The input is a visually adjusted table, and the output is data in a format viewable on the user's terminal. Specifically, the server converts the digital data into an appropriate file format and sends it to the user via the internet. The user can open this table in a browser or compatible application to check the changes in the document.

[0696] (Application Example 2)

[0697] 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".

[0698] In today's rapidly changing market environment, product placement and promotional content in physical stores are frequently updated. While it's crucial for staff to accurately and quickly grasp these changes, information overload and inappropriate communication methods can reduce work efficiency and increase employee stress. Therefore, there is a need for effective methods to recognize changes and communicate them appropriately, taking into account staff emotional states.

[0699] 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.

[0700] In this invention, the server includes data processing means for receiving old and new information, data extraction means for extracting character data from the old and new information, and difference detection means for comparing the character data and detecting difference areas. This makes it possible to quickly and appropriately communicate product and promotion change information to store staff. Furthermore, by including emotion recognition means for analyzing the emotional state of the user and display adjustment means for adjusting the display format of the generated change table, it is possible to provide information with less stress tailored to the state of individual staff members.

[0701] A "data processing means" is a means for receiving old and new information and passing them on to subsequent processing.

[0702] A "data extraction method" is a means for extracting character data from received information.

[0703] A "difference detection method" is a means of comparing the text data of old information and new information and identifying the differences between them.

[0704] The "table generation means" is a means for creating a visual change table based on the detected difference areas.

[0705] "Emotion recognition means" refers to a method for analyzing a user's emotional state and utilizing that information.

[0706] "Display adjustment means" refers to means for dynamically adjusting the display format of the generated change table based on the user's emotional state.

[0707] "Output means" refers to means for outputting the generated change sheet to a specified device.

[0708] The system for implementing the present invention mainly consists of a server, a smart device (such as smart glasses), and various software components connected thereto. Specifically, the server acts as the central component, receiving old and new information via data processing means. This information is converted into individual character data using data extraction means and compared by difference detection means. Subsequently, differences are identified and materialized as a change table by table generation means.

[0709] Users receive the contents of the change sheet via a smart device. The server analyzes the user's emotional state using emotion recognition and dynamically adjusts the color scheme and notification method of the change sheet using display adjustment mechanisms. This makes it possible to provide information efficiently while reducing user stress. Specific hardware used includes smart glasses (such as Google Glass) as an example of a smart device, and a general cloud platform is used on the server side.

[0710] Furthermore, the software utilizes OCR libraries (such as Tesseract) for text extraction, emotion recognition libraries (such as Affectiva SDK) for emotion analysis, and UI frameworks (such as OpenCV) for visual display. These components work together to support the overall operation of the system.

[0711] For example, if a physical store frequently updates its product displays, staff members wear smart glasses while patrolling the store. The server sends a change sheet generated based on the store's update information to the glasses, and presents the information in an appropriate color scheme and notification format according to the staff member's emotional state. This helps ensure that important information is not overlooked and that work proceeds smoothly. By inputting a prompt such as "Check the display placement of new products and suggest ways to reduce notification stress" into the generating AI model, the optimal method of providing information is suggested.

[0712] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0713] Step 1:

[0714] The server receives both old and new information from users. This provides the underlying dataset for information that needs updating. The received data is stored for subsequent processing.

[0715] Step 2:

[0716] The server extracts text data from the received information using data extraction methods. This process uses an OCR library (e.g., Tesseract) to convert text from paper documents and images into a digital format. As a result, the information is output in a format that can be used as text data.

[0717] Step 3:

[0718] The server compares the text data of the old and new information using a difference detection mechanism. In this step, a comparison algorithm is used to clarify the differences in the text content. The output is a dataset showing the detected differences.

[0719] Step 4:

[0720] The server generates a change table from the difference detection results using a table generation mechanism. In this process, the table is structured in a format that clearly indicates the changes as a visual interface. This provides an output that allows the user to visually confirm the changes.

[0721] Step 5:

[0722] The server analyzes the user's emotional state using emotion recognition technology. It analyzes voice and facial expression data acquired from the user's device to evaluate whether the user's emotions are positive, negative, or stressful. The output is data reflecting the user's emotional state.

[0723] Step 6:

[0724] The server adjusts the display format of the change table based on the user's emotional state using display adjustment mechanisms. Specifically, if the user is experiencing stress, the display colors and font size are changed to more calming ones, among other customizations tailored to the user.

[0725] Step 7:

[0726] The server uses an output mechanism to send the generated change sheet to the user's terminal. As a final output, a visually adjusted change sheet is presented on the user's smart device, allowing the user to review its contents and utilize them in their work.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0734] 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.

[0735] 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."

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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 as being incorporated by reference.

[0748] The following is further disclosed regarding the embodiments described above.

[0749] (Claim 1)

[0750] Information processing means for receiving old and new materials,

[0751] A text extraction means for extracting text data from old and new documents,

[0752] A difference detection means that compares the text data of old and new documents and detects the differences,

[0753] A table generation means that classifies the detected differences and generates a revised table,

[0754] An output means for outputting the generated current revised table,

[0755] A system that includes this.

[0756] (Claim 2)

[0757] The system according to claim 1, wherein the information processing means has a function to receive uploads of materials from users.

[0758] (Claim 3)

[0759] The system according to claim 1, wherein the table generation means has a function to highlight the detected differences.

[0760]

[0761] "Example 1"

[0762] (Claim 1)

[0763] Information processing means for receiving old information and new information,

[0764] A data extraction means for extracting character data from old and new information,

[0765] A difference detection means that compares the text data of old and new information and detects the differences,

[0766] A table generation means that classifies the detected different parts and generates a change table,

[0767] An output means for outputting the generated change table,

[0768] A generation means that generates prompt sentences that explicitly indicate the differences using a generative AI model, and performs correspondence based on those prompt sentences,

[0769] ...

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, wherein the information processing means has a function to receive information transmitted from a user.

[0773] (Claim 3)

[0774] The system according to claim 1, wherein the table generation means has a function to visually highlight the detected different parts by color coding or annotation.

[0775] "Application Example 1"

[0776] (Claim 1)

[0777] An information processing device that receives old and new data,

[0778] A character extraction device that extracts character data from old and new documents,

[0779] A difference detection device that compares the text data of old and new documents and detects the differences,

[0780] A table generation device that classifies the detected differences and generates an updated comparison table,

[0781] An output device that outputs the generated update comparison table,

[0782] A highlighting device that highlights the changes,

[0783] A portable terminal device that enables immediate confirmation of information when used in a physical store,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, wherein the information processing device has a function to receive data transmitted from a user.

[0787] (Claim 3)

[0788] The system according to claim 1, wherein the table generation device has a function to visually highlight the identified differences.

[0789] "Example 2 of combining an emotion engine"

[0790] (Claim 1)

[0791] Information processing means for receiving old and new materials,

[0792] A data extraction method for extracting text data from old and new documents,

[0793] A data comparison means for comparing text data of old and new documents and detecting differences,

[0794] A table generation means that generates a table that classifies the detected differences and visually highlights them,

[0795] An emotion recognition means that analyzes input data to analyze the user's emotional state,

[0796] An adjustment means for adjusting the visual display based on the user's emotional state,

[0797] An output means for outputting the generated table,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The information processing means has a function to receive uploads of materials from users.

[0801] The system according to claim 1, further comprising a function to adjust the display using analysis data obtained from emotion recognition.

[0802] (Claim 3)

[0803] The system according to claim 1, wherein the table generation means has a function to highlight the detected differences in a color corresponding to the user's emotional state.

[0804] "Application example 2 when combining with an emotional engine"

[0805] (Claim 1)

[0806] A data processing means for receiving old and new information,

[0807] A data extraction means for extracting character data from old and new information,

[0808] A difference detection means that compares the text data of old and new information and detects the difference area,

[0809] A table generation means that classifies the detected difference areas and generates a change table,

[0810] An emotion recognition method for analyzing the user's emotional state,

[0811] A display adjustment means that adjusts the display format of the generated change table based on the user's emotions,

[0812] An output means that outputs a change table generated on a specified device,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, wherein the data processing means has a function to receive information uploaded from a user.

[0816] (Claim 3)

[0817] The system according to claim 1, wherein the table generation means has a function to visually highlight the detected difference region. [Explanation of Symbols]

[0818] 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. Information processing means for receiving old and new materials, A text extraction means for extracting text data from old and new documents, A difference detection means that compares the text data of old and new documents and detects the differences, A table generation means that classifies the detected differences and generates a revised table, An output means for outputting the generated current revised table, A system that includes this.

2. The system according to claim 1, wherein the information processing means has a function to receive uploads of materials from users.

3. The system according to claim 1, wherein the table generation means has a function to highlight the detected differences.

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