System

Abstract

A system is provided.SOLUTION: A system including means for a user to input summary information, means for transmitting the summary information to a server, means for the server to analyze the summary information and extract necessary data, means for the server to organize and process the extracted data, means for a generated AI to generate an image or a diagram based on the data, means for the server to assemble materials based on the organized and processed data and the generated image or diagram, means for transmitting final materials to a terminal, and means for displaying the materials received by the terminal to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventionally, document creation has required a great deal of time and effort, and collecting, organizing, and visualizing data has required specialized knowledge and skills. This has made it difficult to efficiently create high-quality documents. Extracting necessary information from different data sources and processing it in a consistent manner has also been a challenging task. The present invention aims to solve these problems and provide a system that automatically generates high-quality documents by requiring users to simply input concise summary information. [Means for solving the problem]

[0005] The present invention is a system that includes a means for a user to input summary information, a means for transmitting the summary information to a server, a means for the server to analyze the summary information and extract necessary data, a means for the server to organize and process the extracted data, a means for a generation AI to generate images and charts based on the data, a means for the server to assemble materials based on the organized and processed data and the generated images and charts, a means for transmitting the final materials to a terminal, and a means for the terminal to display the materials received to the user. This allows for the efficient creation of high-quality materials without requiring much time and effort or specialized knowledge or skills. Furthermore, information extraction from different data sources and consistent processing are also performed automatically.

[0006] A "user" is an entity that uses the system to provide input information and request the creation of materials.

[0007] A "terminal" is a device through which a user inputs summary information and displays and downloads received materials.

[0008] "Summary information" is information that a user inputs into the system, including the main elements and key points of the material that the user wants to create.

[0009] The "server" is a computer system that analyzes the received summary information, extracts, organizes, and processes the necessary data, and then generates materials.

[0010] "Analysis" is the process by which the server receives summary information, understands its contents, and identifies the type and range of data required.

[0011] "Extraction" is the process in which the server retrieves the necessary data from an API or database based on the analysis results.

[0012] An "API" is an interface that allows a server to exchange data with other systems and services.

[0013] A "database" is a storage device where data is systematically stored and is a data source from which a server can extract the necessary information.

[0014] "Sorting and processing" is the process by which the server statically filters and arranges the extracted data, converting it into a format suitable for analysis and display.

[0015] "Generative AI" is an artificial intelligence technology that is installed on a server and automatically generates images and charts based on data.

[0016] "Images and charts" are graphics generated to visually represent data and are used to aid in the understanding of material.

[0017] A "document" is a final document containing organized data, images, and charts generated based on the summary information entered by the user.

[0018] "Send" is the action of sending the materials generated by the server to the terminal.

[0019] "Display" refers to the operation of the terminal to show the received material to the user. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] This invention is a generative AI integrated system that allows users to easily create high-quality documents. In this system, the server automatically extracts, organizes, and processes data based on the summary information entered by the user, and the generative AI generates images and charts, which are then output as documents.

[0042] The user accesses the system from a terminal and inputs the outline of the document they want to create. For example, they might input an instruction such as "Create a report based on sales data for fiscal year 2022." This outline is then sent to the server.

[0043] The server analyzes the received summary information and identifies the data the system needs. Based on this analysis, the server accesses an API or database to extract the required data. For example, if sales data is in a database, the server retrieves the relevant data using a query.

[0044] The acquired data is organized and processed on the server. In this process, the data is filtered and converted into an appropriate format. For example, monthly sales data is aggregated and sorted chronologically. In this way, the data is organized into a form that is easy to understand visually.

[0045] Next, generative AI works to generate images and charts based on the organized and processed data. For example, it can generate line graphs showing sales trends or infographics showing market trends. The generated images and charts are then incorporated as an integral part of the document.

[0046] The server then combines the final organized and processed data with the generated images and charts to create a document, such as a PDF report, so that the necessary information can be understood at a glance. This document is then sent to the device.

[0047] The terminal displays the materials sent from the server to the user. The user can view the materials on the terminal and download them as needed. This reduces the time and effort required for users to create materials, and allows users to obtain high-quality materials efficiently.

[0048] As a concrete example, consider the case where a company's sales department is creating an annual report. The salesperson (user) inputs summary information into a terminal to instruct the creation of the report. Based on the received information, the server extracts the necessary data from the sales database and generates a graph showing monthly sales trends. Finally, this information is integrated to create an annual report in PDF format, which is provided to the salesperson via the terminal.

[0049] In this way, the system of the present invention automates consistent data processing and visual document generation, thereby providing a highly efficient document creation environment for users.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The user inputs the outline information of the document from the terminal. For example, the user inputs an instruction such as "Create a report based on sales data for fiscal year 2022."

[0053] Step 2:

[0054] The terminal transmits the input summary information to the server.

[0055] Step 3:

[0056] The server analyzes the received summary information, which includes using natural language processing techniques to extract keywords and identify the type and scope of required data.

[0057] Step 4:

[0058] The server extracts the necessary data from an API or database based on the analysis results. For example, it references a database that stores sales data and executes the query "SELECT FROM sales WHERE year = 2022".

[0059] Step 5:

[0060] The server organizes and processes the extracted data. During this process, the data is filtered to extract data for specific months. It also calculates the total sales for each month and sorts them chronologically.

[0061] Step 6:

[0062] Generative AI generates images and charts based on the data organized and processed on the server. For example, it uses a graph creation tool to generate a line graph showing the increase or decrease in sales.

[0063] Step 7:

[0064] The server assembles the final document based on the organized and processed data and the generated images and charts, for example, by formatting it as a PDF report so that important information can be understood at a glance.

[0065] Step 8:

[0066] The server transmits the final generated material to the terminal.

[0067] Step 9:

[0068] The terminal displays the received materials to the user, who can then view the materials through the terminal and download them as needed.

[0069] In this way, the system automatically generates high-quality materials based on the summary information entered by the user through each step.

[0070] Example 1

[0071] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0072] Traditional document creation processes are often performed manually, resulting in the time-consuming and labor-intensive process of collecting, formatting, analyzing, and creating the final document. This manual process also increases the risk of human error, potentially resulting in low-quality documents. Furthermore, creating visually easy-to-understand documents requires advanced expertise. There is a need for a system that can solve these problems and automatically create high-quality documents.

[0073] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0074] In this invention, the server includes means for analyzing summary information and identifying necessary data, means for extracting the identified data from the database, and means for organizing and processing the extracted data, which makes it possible to automatically obtain and format data based on the summary information entered by the user, and generate visually easy-to-understand materials.

[0075] A "user" is a person or entity that utilizes the system to input instructions and summary information for creating materials.

[0076] "Terminal" means an electronic device used by a user to access the system, enter summary information and display received materials.

[0077] A "server" is a central computer that processes data for the entire system, extracts, organizes, processes, and generates documents based on instructions from users.

[0078] "Summary information" is data that indicates the basic requirements and instructions for creating a document entered by the user.

[0079] A "natural language processing library" is a software tool or library that the server uses to parse the summary information entered by the user.

[0080] A "database" is a digital storage system in which tangible data is stored.

[0081] A "generative AI model" is an artificial intelligence algorithm that runs on a server and generates images and charts based on organized and processed data.

[0082] "Material" is a collection of data created by the server using a generative AI model, and is the final document provided to the user.

[0083] "Images and charts" are visual content created by generative AI models based on organized and processed data.

[0084] This invention is a generative AI integrated system that allows users to easily create high-quality documents. Based on the summary information entered by the user, the server automatically extracts, organizes, and processes data, and the generative AI generates images and charts, which are then output as documents.

[0085] The user accesses the system from a terminal and inputs summary information for the document they want to create. For example, they might input an instruction such as "Create a report based on sales data for fiscal year 2022" into the terminal. This summary information is sent from the terminal to the server. In this case, the terminal can be thought of as inputting a form using a web browser.

[0086] The server analyzes the received summary information to identify the data the system requires. This analysis is performed using a natural language processing (NLP) library (e.g., spaCy or NLTK). Based on the results of this analysis, the server accesses an API or database to extract the required data. For example, if sales data is stored in a database, the server retrieves the data using an SQL query such as "SELECT FROM sales WHERE year=2022."

[0087] The acquired data is organized and processed on the server. In this process, the data is filtered using a data analysis library (e.g., pandas) and converted into an appropriate format. For example, monthly sales data is aggregated and sorted in chronological order.

[0088] Next, a generative AI model is put into action to generate images and charts based on the organized and processed data. Examples of generative AI models that are used include OpenAI's GPT-3 and DALLE-2. This generates line graphs showing sales trends and infographics showing market trends.

[0089] The server then combines the final organized and processed data with the generated images and charts to create a document. This document is often generated in PDF format and is formatted using a PDF generation library (e.g., ReportLab). This visually organizes the necessary information so that it can be understood at a glance.

[0090] The completed materials are sent from the server to the terminal, which displays them to the user, who can then view them on the terminal and download them as needed.

[0091] As a concrete example, consider the case where a company's sales department is creating an annual report. In this case, the sales department (user) inputs summary information into a terminal to instruct the creation of the report. For example, the user inputs a prompt such as, "Please create a sales data analysis report for fiscal year 2022." The server receives this, extracts the necessary data from the sales database, generates a graph showing monthly sales trends, and finally creates an annual report in PDF format and provides it to the sales department via the terminal.

[0092] In this way, the system of the present invention automates consistent data processing and visual document generation, thereby providing a highly efficient document creation environment for users.

[0093] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0094] Step 1:

[0095] The user accesses the system from a terminal and inputs summary information for the document they want to create. For example, they input instructions such as "Create a report based on sales data for fiscal year 2022." The input here is done by the user entering summary information into a form using a web browser. The input summary information is then used as input for the next step.

[0096] Step 2:

[0097] The terminal sends the summary information entered by the user to the server. This process is performed through an HTTP request (POST method). The input of the terminal is the summary information, and the output is the HTTP request received by the server.

[0098] Step 3:

[0099] The server analyzes the received summary information. This analysis is performed using a natural language processing library (e.g., spaCy or NLTK). Specifically, it extracts keywords and phrases from the summary information to identify the required data and clarify the type and range of data. The input is the summary information received via the HTTP request, and the output is the type and range of the identified data.

[0100] Step 4:

[0101] The server extracts the specified data from the database. It accesses the database (e.g. MySQL or PostgreSQL) using an SQL query (e.g. "SELECT FROM sales WHERE year=2022") to retrieve the relevant data. The input is the type and range of the specified data, and the output is the retrieved data.

[0102] Step 5:

[0103] The server organizes and processes the extracted data. This process uses a data analysis library (e.g., pandas) to filter the data and convert it into an appropriate format. For example, this may involve aggregating monthly sales data and sorting it in chronological order. The input is the data extracted from the database, and the output is the organized and processed data.

[0104] Step 6:

[0105] A generative AI model generates images and charts based on the organized and processed data. In this step, a generative AI model (e.g., OpenAI's GPT-3 or DALLE-2) is used to generate graphs and infographics. For example, a line graph showing sales increases or decreases or an infographic showing market trends may be generated. The input is the organized and processed data, and the output is the generated images and charts.

[0106] Step 7:

[0107] The server combines the organized and processed data with the generated images and charts to create documents. In this example, a PDF generation library (e.g., ReportLab) is used to generate documents in PDF format. For example, an annual report containing sales trend graphs and text is created. The input is the organized and processed data and the generated images and charts, and the output is a document in PDF format.

[0108] Step 8:

[0109] The server sends the completed document to the terminal. This process is performed using an HTTP response. The server's input is the completed PDF document, and the output is the HTTP response to the terminal.

[0110] Step 9:

[0111] The terminal displays the received PDF document to the user. The user can view the document on the terminal and download it as needed. The input is the PDF document sent from the server, and the output is the display and download to the user.

[0112] (Application example 1)

[0113] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0114] Data processing and report creation in conventional logistics centers relied heavily on manual work, which was time-consuming and labor-intensive, and also had accuracy issues. Furthermore, data visualization was insufficient, preventing managers from providing the information they needed to make quick decisions. For this reason, there is a demand for a system that automates efficient, high-quality data processing and document creation.

[0115] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0116] In this invention, the server includes: means for a user to input summary information; means for transmitting the summary information to the server; means for the server to analyze the summary information and extract necessary data; means for the server to organize and process the extracted data; means for a generation AI to generate images and charts based on the data; means for the server to assemble materials based on the organized and processed data and the generated images and charts; means for transmitting the final materials to a terminal; means for the terminal to display the materials received to the user; means for inputting prompts to acquire necessary data and generate visual charts; means for outputting business-related information such as shipment quantities and inventory levels as graphs based on the acquired data; and means for integrating the acquired data and the generated graphs to generate materials in report format. This automates data processing and document creation in logistics centers, enabling efficient and high-quality report creation.

[0117] "User" refers to a person who uses this system to input summary information and give instructions for creating materials.

[0118] "Summary information" refers to information that includes basic information and instructions for creating materials.

[0119] "Server" refers to the computer that analyzes the summary information sent by the user, extracts, organizes, and processes the necessary data, and generates the final document.

[0120] "Generative AI" refers to artificial intelligence technology that automatically generates images and charts based on extracted, organized, and processed data.

[0121] "Prompt" refers to text that describes instructions the user gives the system to obtain the required data and generate a visual chart.

[0122] "Materials" refers to report-style documents that integrate organized and processed data and generated images and charts.

[0123] "Graph" refers to graphs and charts generated to visually display numerical data or statistical information.

[0124] "Graph" refers to a visual representation, such as a line graph or bar graph, that is generated to visually show numerical data.

[0125] "Terminal" refers to a computer or smart device used by a user to input instructions for creating materials and to view the created materials.

[0126] A "logistics center" refers to a facility that stores, manages, and ships goods.

[0127] This invention is a system for streamlining data processing and document creation in a logistics center. The system is composed of the following main components:

[0128] 1. User operations

[0129] The administrator (user) of the logistics center accesses the system using a smartphone or computer terminal. The user enters summary information for document creation into the input field on the terminal. For example, the user enters a prompt statement such as "Create a shipping data and inventory report for March."

[0130] 2. Server Operation

[0131] The server receives the summary information sent by the user. The server analyzes the summary information and accesses an API or database to extract the necessary data. This includes data on shipment numbers and inventory levels from the logistics management system database. The server then organizes and processes the retrieved data. Specifically, it calculates monthly totals for shipment numbers and average inventory levels. The Python library "Pandas" is used to filter and aggregate the data.

[0132] 3. Image and chart generation

[0133] Based on the organized and processed data, the generative AI generates images and charts. Specifically, it uses the Python "Matplotlib" library to create line graphs and bar graphs. This generative AI operates according to the prompts entered by the user, providing visual information.

[0134] 4. Creating and outputting the final materials

[0135] The server integrates the organized data with the generated images and charts to generate the final report format. This document is output in PDF format or other formats. The Python "FPDF" library is used to generate the report. The server then sends the generated report to the user's terminal. The terminal displays the received report to the user, who can then download or print it as needed.

[0136] To give a concrete example, a user might enter the following prompt sentence:

[0137] "Please create a shipping data and inventory report for March. Specifically, please include graphs that visually show the progress of shipments and average inventory."

[0138] This system automates data processing and document creation at logistics centers, enabling efficient, high-quality reports to be created quickly.

[0139] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0140] Step 1:

[0141] A user accesses the system using a terminal and inputs the outline information for creating documents as a prompt statement. For example, the user inputs the instruction "Create shipping data and inventory report for March." This input information is sent to the server through the terminal interface.

[0142] Step 2:

[0143] The server receives the summary information sent by the user. It analyzes the received information and performs analysis processing to identify the required data. Specifically, it identifies the type and range of data, including shipping data and inventory information, based on the prompt text, and prepares to access the database or API.

[0144] Step 3:

[0145] The server accesses the API or database to extract the required data identified by querying the logistics management system database to retrieve data related to shipments and inventory levels for March. The inputs are the parsed prompt results and the database query, and the output is the retrieved raw data.

[0146] Step 4:

[0147] The server cleans and processes the extracted data. Specifically, it uses the Python library Pandas to filter the data and format it as needed. This step aggregates shipments by month and averages inventory levels. The input is the extracted raw data, and the output is the formatted data.

[0148] Step 5:

[0149] The generative AI generates images and charts based on the organized data. It uses Python's Matplotlib library to create line and bar graphs. Specifically, it plots the aggregated shipment data on a line graph and the average inventory level data on a bar graph. The input is the formatted data, and the output is the generated image file.

[0150] Step 6:

[0151] The server integrates the organized data with the generated images and charts to generate the final report format document, which is output in PDF format using the Python "FPDF" library. The input is the formatted data and generated image files, and the output is a report format PDF file.

[0152] Step 7:

[0153] The server sends the generated PDF report to the user's terminal. The user can use the terminal to view the received report and download or print it as needed. The input is the generated PDF file and the output is the report displayed on the terminal.

[0154] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0155] This invention is a generative AI integrated system that allows users to easily create high-quality documents, and also combines it with an emotion engine that recognizes the user's emotions. In this system, the server automatically extracts, organizes, and processes data based on the summary information entered by the user, and the generative AI generates images and charts. The emotion engine also recognizes the user's emotional state and adapts the content and design of the document accordingly.

[0156] Users access the system from their terminals and input outline information for the materials they wish to create. This outline information is then sent from the terminal to the server.

[0157] The server analyzes the received summary information, which includes using natural language processing techniques to extract keywords and identify the type and scope of required data.

[0158] The server then accesses an API or database to extract the necessary data, which is then organized and processed within the server. This process involves filtering, aggregating, and sorting the data chronologically.

[0159] The emotion engine works here and analyzes the user's voice or text input to recognize their emotional state. For example, if the user's input has a positive tone, the emotion engine will analyze it as a positive emotion.

[0160] The generative AI generates images and charts based on the organized and processed data, taking into account the analysis results of the emotion engine. For example, if the user's emotion is positive, it generates a chart with a bright color scheme and a positive message.

[0161] The server then assembles the document based on the processed data and the generated images and charts. For example, it formats it as a PDF report, applying content and design based on the analysis results of the emotion engine. This document is then sent to the device.

[0162] The terminal displays the materials sent from the server to the user, who can then view the materials through the terminal and download them as needed.

[0163] As a concrete example, consider the case where a company's sales department is creating an annual report. The salesperson (user) inputs the instruction "Create a report based on sales data for fiscal year 2022" into their device. The server analyzes this information, extracts the necessary data from the sales database, and organizes and processes it. Furthermore, if the emotion engine analyzes the tone of the text entered by the salesperson as positive, the generative AI generates a positive message and a brightly designed graph. Finally, the annual report is completed in PDF format and provided to the salesperson via their device.

[0164] In this way, the system of the present invention not only automates consistent data processing and visual document generation, but also provides optimal content and design according to the user's emotional state, thereby providing a more advanced and efficient document creation environment for users.

[0165] The processing flow will be explained below.

[0166] Step 1:

[0167] The user inputs the outline of the document from the terminal. For example, the user inputs instructions such as "Create a report with a positive tone based on sales data for fiscal year 2022."

[0168] Step 2:

[0169] The terminal transmits the input summary information to the server.

[0170] Step 3:

[0171] The server analyzes the summary information it receives. This analysis involves using natural language processing technology to extract keywords and identify the type and scope of data required. For example, it extracts keywords such as "sales data" and "2022."

[0172] Step 4:

[0173] The server extracts the necessary data from the API or database based on the analysis results. For example, it executes the query "SELECT FROM sales WHERE year = 2022" against the sales database to retrieve sales data for 2022.

[0174] Step 5:

[0175] The server organizes and processes the extracted data. This process involves filtering the data, aggregating monthly sales, and sorting them by time. For example, 12 months of sales data can be totaled by month and sorted in chronological order.

[0176] Step 6:

[0177] The device acquires the user's emotions and sends the data to the server. For example, if the user voice-types "Please write the report in a positive tone," this voice data is sent to the server.

[0178] Step 7:

[0179] The server's emotion engine analyzes the user's emotion data. For example, it recognizes the emotion as positive from the voice data and records the analysis result.

[0180] Step 8:

[0181] The generative AI generates images and charts based on the analysis results of the emotion engine, using data organized and processed on the server. For example, it generates line graphs with a positive design and infographics with a bright tone.

[0182] Step 9:

[0183] The server assembles the final document based on the organized and processed data and the generated images and charts, for example, creating a PDF report with a positive message and a bright design.

[0184] Step 10:

[0185] The server transmits the final generated material to the terminal.

[0186] Step 11:

[0187] The terminal displays the received documents to the user. The user can view the documents through the terminal, check their contents, and download them as needed. For example, a sales department employee displays the generated annual report on the terminal and checks its contents.

[0188] In this way, the system of the present invention automatically generates optimal materials based on the summary information and emotion data entered by the user through a series of steps, allowing users to not only efficiently create high-quality materials, but also receive materials with content and design appropriate to their emotions.

[0189] Example 2

[0190] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0191] In today's world, it is important for users to create high-quality documents quickly. However, with conventional systems, it has been difficult to consistently automate the process of extracting, organizing, and processing data, as well as generating visual graphics. Furthermore, there has been no system that can provide optimal design and content based on the user's emotional state. As a result, users have had to spend a lot of time and effort creating documents.

[0192] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input summary information; a means for transmitting the summary information to the server; a means for the server to analyze the summary information and extract necessary data; a means for the server to organize and process the extracted data; a means for an emotion engine to analyze the user's emotional state; a means for a generation AI to generate images and charts based on the data and the emotion analysis results; a means for the server to assemble materials based on the organized and processed data and the generated images and charts; a means for transmitting the final materials to a terminal; and a means for the terminal to display the received materials to the user. This enables users to quickly create high-quality materials through a consistent, automated process. In addition, user satisfaction can be improved by providing materials with optimal design and content based on the user's emotional state.

[0193] A "user" is an entity that accesses the system and inputs summary information for the creation of materials.

[0194] A "terminal" is a hardware device or software environment that is operated by a user and that communicates with a server.

[0195] The "server" is a central processing unit that analyzes the summary information received from the user, extracts the necessary data, organizes and processes it, generates images and charts using AI, and assembles materials.

[0196] "Summary information" refers to the original information that the user inputs to create a document, and includes specific data and instructions.

[0197] "Analysis" is the process of identifying keywords and the type and range of required data from the summary information.

[0198] "Data" refers to information elements such as numbers, text, and images required to create the materials desired by the user.

[0199] "Extraction" is the process of retrieving data identified through analysis from external resources such as APIs or databases.

[0200] "Organization" is the process of organizing the extracted data by filtering, aggregating, sorting, etc.

[0201] "Processing" is the process of converting organized data into a form that is easier to handle visually or logically.

[0202] An "emotion engine" is a combination of software or hardware that analyzes a user's input and behavior to determine their current emotional state.

[0203] "Generative AI" is an artificial intelligence model that automatically generates images and charts contained in documents based on organized and processed data and the results of sentiment analysis.

[0204] "Images and diagrams" are graphic elements created by generative AI to represent visual information.

[0205] A "material" is a formatted collection of information such as a report, presentation, or document that is ultimately provided to a user.

[0206] "Transmission" is the communication act in which the server transfers the final material to the terminal.

[0207] "Display" refers to the process in which the terminal visually presents the received materials to the user.

[0208] MODE FOR CARRYING OUT THE INVENTION

[0209] This invention is a generative AI integrated system that allows users to easily create high-quality materials, and also combines it with an emotion engine that recognizes the user's emotions. This system has the following hardware and software configuration.

[0210] Hardware Configuration

[0211] 1. Terminal: The device operated by the user (e.g., PC, tablet, smartphone).

[0212] 2. Server: A central processing unit that analyzes, extracts, and processes data for AI generation.

[0213] Software Configuration

[0214] 1. Natural language processing libraries: Python-based nltk and spaCy are used to analyze summary information.

[0215] 2. Database management system: Use MySQL or PostgreSQL to manage the necessary data.

[0216] 3. Pandas library: Used to filter, aggregate, and time-order the extracted data.

[0217] 4. Sentiment Analysis Tool: Analyze the user's emotional state using NLTK's VADER Sentiment Analysis.

[0218] 5. Generative AI model: Using OpenAI's GPT-3 and DALL-E, images and charts are generated based on user input data and sentiment analysis results.

[0219] 6. Report generation library: Use the Python ReportLab library to compile reports in PDF format.

[0220] Implementation method

[0221] The user accesses the system from a terminal and inputs the outline of the document they want to create. Specifically, they input the instruction to "create a report based on sales data for fiscal year 2022." The terminal then sends this outline to the server.

[0222] The server then analyzes the received summary information using a Python-based natural language processing library. During this analysis, keywords such as "2022 fiscal year," "sales," and "report" are extracted to identify the type and scope of data required.

[0223] The server then accesses an API or database to extract sales data, which it then uses the Pandas library to filter, aggregate, and sort (for example, to sum sales data by month for January through December of fiscal year 2022).

[0224] In parallel, the emotion engine analyzes the user's input text and voice data. It uses NLTK's VADER Sentiment Analysis tool to identify the user's emotional state. For example, if the input text has a positive tone, it will be analyzed as having a positive emotion.

[0225] Based on this organized and processed data and the results of emotion analysis, the generative AI generates images and charts. If the user's emotional state is positive, the generative AI will generate charts with bright colors and positive messages.

[0226] The server then assembles the final document based on the generated images, charts, and organized and processed data. For example, it compiles the data into a PDF annual report using Python's ReportLab library. The document's design and content are adapted to the user's emotional state.

[0227] Finally, the server sends the completed document to the terminal, which displays it to the user, who can then view the document and download it as needed.

[0228] Prompt Sentence Examples

[0229] An example of a prompt sentence to input to the generative AI model is as follows:

[0230] "Create an annual report based on your 2022 sales data, with positive messaging and brightly designed graphs."

[0231] In this way, the system is designed to enable users to quickly and easily create high-quality materials, while also providing optimal design and content according to the user's emotional state.

[0232] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0233] Step 1:

[0234] A user accesses the system using a terminal and inputs the outline of the document they want to create. For example, they might input, "Create an annual report based on sales data for fiscal year 2022." This input is sent to the server through the terminal's user interface.

[0235] Step 2:

[0236] The terminal sends the summary information entered by the user to the server, which receives the input data in text format.

[0237] Step 3:

[0238] The server analyzes the received summary information using Python's natural language processing library (nltk or spaCy) to extract keywords such as "2022," "sales," and "annual report," and identify the type and scope of the data. The input to this analysis process is the text data of the summary information, and the output is the extracted keywords and data range.

[0239] Step 4:

[0240] The server extracts the necessary data from a database (MySQL or PostgreSQL) or API based on the specified keywords and data range. For example, it extracts "sales data" for "fiscal year 2022." The input for this process is the specified keywords and data range, and the output is the corresponding sales data.

[0241] Step 5:

[0242] The server organizes and processes the extracted data using the Pandas library. Specifically, it filters, aggregates, and sorts the data by month. The input to this process is the extracted sales data, and the output is the organized and processed data.

[0243] Step 6:

[0244] The server's emotion engine analyzes the user's input text. It uses NLTK's VADER Sentiment Analysis tool to identify the user's emotional state. For example, if the input text has a positive tone, it outputs a positive emotion. The input of this process is the user's text data, and the output is the emotional state.

[0245] Step 7:

[0246] The server's generative AI (GPT-3 or DALL-E) generates images and charts based on the organized and processed data and the results of sentiment analysis. Based on the emotional state, the generative AI selects designs such as brightly colored charts and positive messages. The inputs to this process are the organized and processed data and the emotional state, and the output is images and charts.

[0247] Step 8:

[0248] The server assembles documents using the generated images and charts, along with the organized and processed data. For example, it creates a PDF report using the Python ReportLab library. The inputs to this process are the images, charts, and organized and processed data, and the output is the final document (PDF report).

[0249] Step 9:

[0250] The server sends the completed document to the terminal. The input of this sending process is the completed document, and the output is the successful transfer of the document to the terminal.

[0251] Step 10:

[0252] The terminal displays the received material to the user, who can then view and download it as needed. The input to this process is the material sent from the server, and the output is the material displayed to the user.

[0253] Through these specific processing steps, the present invention provides a process for users to create efficient and high-quality materials, and realizes optimal document generation according to the needs and feelings of the user.

[0254] (Application example 2)

[0255] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0256] Conventional document creation systems require users to manually create the content and design of documents, which is time-consuming and labor-intensive and inefficient. Furthermore, dynamic document generation that takes into account the user's emotional state is difficult, making it impossible to provide optimal documents for individual users. The present invention aims to solve these problems by providing a system that allows users to easily create high-quality documents and provides optimal documents that correspond to the user's emotional state.

[0257] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input summary information; means for transmitting the summary information to the server; means for the server to analyze the summary information and extract necessary data; means for the server to organize and process the extracted data; means for a generation AI to generate images and charts based on the data; means for the server to assemble materials based on the organized and processed data and the generated images and charts; means for transmitting the final materials to the terminal; means for the terminal to display the received materials to the user; means for analyzing the user's emotional state using emotion analysis means; and means for the generation AI to adapt the content and design of the materials based on the user's emotional state. This makes it possible to easily and automatically generate dynamic, high-quality materials that take the user's emotional state into consideration.

[0258] "Users" are people or organizations that use this system and create materials.

[0259] "Summary information" refers to basic data and instructions regarding the content and theme of the material the user wants to create.

[0260] The "server" is a computer system that performs centralized calculations and management to analyze the input summary information, extract, organize, and process the necessary data, and generate the final materials.

[0261] "Data Extraction Method" refers to the technology or process that retrieves the data required from the Summary Information from the API or database.

[0262] "Means of organizing and processing" refers to the techniques or processes that filter, aggregate, sort, or otherwise format the extracted data for a specific purpose.

[0263] "Generative AI" is an artificial intelligence technology that generates images and charts based on organized and processed data, and determines the content and design of materials.

[0264] "Emotion analysis means" is a technology that analyzes the user's emotional state from voice or text input, and reflects the analysis results in the system.

[0265] "Means for assembling materials" refers to the technology or process for compiling the final materials into a single document format based on the images and diagrams created by the generative AI.

[0266] "Terminal" refers to a computer, smartphone, or other digital device used by a user.

[0267] "Transmission means" refers to the technology or process for transmitting the final material from the server to the terminal.

[0268] "Display means" refers to the technique or process by which the final material is displayed to the user on the terminal.

[0269] This invention is a system that generates high-quality driving analysis reports so that users can easily understand their daily driving conditions in autonomous vehicles. This system collects sensor data and driving situation data from autonomous vehicles in real time, analyzes them on a server, and with the cooperation of a generation AI model, provides users with an optimized driving report. Furthermore, by using emotion analysis means, the system adjusts the design and content according to the user's emotional state.

[0270] The server first receives and analyzes the summary information entered by the user. It then extracts the necessary data from an API or database, and organizes and processes the extracted data by filtering, aggregating, and sorting it chronologically. Based on this organized and processed data, the generative AI generates images and charts. Based on the generated images, charts, and text data, the server assembles the final document in PDF or HTML format.

[0271] In particular, for owners of autonomous vehicles, data is collected from various sensors inside the vehicle (speed sensor, GPS sensor, lidar sensor), and by analyzing the driver's emotional state in real time using a camera, the content and design of the generated driving report are adapted to the user's emotional state.

[0272] As a specific use case, consider the case where a user launches the application and inputs, "I would like to create a driving safety report today." Data such as speed data, location data, driving patterns, and the driver's emotional state are collected from the autonomous vehicle. Based on this data, the server creates a driving safety report by sending the following prompt sentence to the generation AI.

[0273] Example prompt sentence:

[0274] I want to write a driving safety report today.

[0275] Data collected:

[0276] Speed ​​data: average speed 60 km / h, maximum speed 100 km / h

[0277] Location data: urban, suburban

[0278] Driving pattern: sudden acceleration 3 times, sudden braking 2 times

[0279] Driver's emotional state: Slightly stressed facial expression

[0280] Based on this, prepare a driving safety report and summarize it in language that is easy for the driver to understand.

[0281] The server analyzes this data and generates a driving report based on a generative AI model (e.g., OpenAI's GPT-4). The report is then sent to the user's smartphone or car's instrument panel display and displayed to the user, who can then receive specific advice on how to improve their driving safety.

[0282] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0283] Step 1:

[0284] The user inputs summary information. Using a terminal in the autonomous vehicle or a smartphone, the user inputs instructions such as, "I would like to create a driving safety report today." This input becomes the initial data for the system.

[0285] Step 2:

[0286] The terminal sends the input summary information to the server. When the user inputs the summary information, the terminal sends this information to the server. At this time, the input summary information is "I would like to create a driving safety report."

[0287] Step 3:

[0288] The server analyzes the summary information and extracts the necessary data. The server then analyzes the received summary information using natural language processing technology and extracts the necessary data categories (speed data, location data, driving patterns, etc.). The output is a categorization of the necessary data for the summary information input.

[0289] Step 4:

[0290] The server collects various sensor data from the vehicle. Based on the extracted data type, the server collects data from various sensors in the vehicle (speed sensor, GPS sensor, lidar sensor, etc.). The input is a categorized data request, and the output is specific sensor data.

[0291] Step 5:

[0292] The server analyzes the user's emotional state using emotion analysis means. The driver's facial expressions are captured using a camera in the vehicle and analyzed using the Emotion API. The input is the captured image data, and the output is the driver's emotional state.

[0293] Step 6:

[0294] The server organizes and processes the collected sensor data and emotion data. It uses a data analysis library (e.g., Python's pandas or numpy) to filter, aggregate, and sort the data. The input is the collected sensor data and emotion data, and the output is the organized and processed data.

[0295] Step 7:

[0296] The generation AI generates prompt sentences based on the organized and processed data and generates the contents of the driving report.Using the generation AI (e.g., OpenAI GPT-4), the following prompt sentences are prepared and the contents of the driving report are generated.

[0297] Example prompt:

[0298] I want to write a driving safety report today.

[0299] Data collected:

[0300] Speed ​​data: average speed 60 km / h, maximum speed 100 km / h

[0301] Location data: urban, suburban

[0302] Driving pattern: sudden acceleration 3 times, sudden braking 2 times

[0303] Driver's emotional state: Slightly stressed facial expression

[0304] Based on this, prepare a driving safety report and summarize it in language that is easy for the driver to understand.

[0305] The input is the organized and processed data and prompt statements, and the output is the content of the generated driving report.

[0306] Step 8:

[0307] The server assembles a driving report based on the generated images, charts, and text data. It integrates the images, charts, and text information obtained from the generation AI into PDF or HTML format. The input is the content of the generated driving report, and the output is the final driving report.

[0308] Step 9:

[0309] The server sends the final driving report to the terminal. The completed driving report (PDF or HTML format) is sent to the user's terminal. The input is the driving report, and the output is the completion of sending it to the user's terminal.

[0310] Step 10:

[0311] The terminal displays the received driving report to the user. The terminal displays the received driving report on the display, which the user can view. The input is the received driving report, and the output is the user's visual confirmation.

[0312] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0313] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0314] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0315] [Second embodiment]

[0316] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0317] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0318] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0319] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0320] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0321] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0322] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0323] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0324] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0325] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0326] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0327] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0328] This invention is a generative AI integrated system that allows users to easily create high-quality documents. In this system, the server automatically extracts, organizes, and processes data based on the summary information entered by the user, and the generative AI generates images and charts, which are then output as documents.

[0329] The user accesses the system from a terminal and inputs the outline of the document they want to create. For example, they might input an instruction such as "Create a report based on sales data for fiscal year 2022." This outline is then sent to the server.

[0330] The server analyzes the received summary information and identifies the data the system needs. Based on this analysis, the server accesses an API or database to extract the required data. For example, if sales data is in a database, the server retrieves the relevant data using a query.

[0331] The acquired data is organized and processed on the server. In this process, the data is filtered and converted into an appropriate format. For example, monthly sales data is aggregated and sorted chronologically. In this way, the data is organized into a form that is easy to understand visually.

[0332] Next, generative AI works to generate images and charts based on the organized and processed data. For example, it can generate line graphs showing sales trends or infographics showing market trends. The generated images and charts are then incorporated as an integral part of the document.

[0333] The server then combines the final organized and processed data with the generated images and charts to create a document, such as a PDF report, so that the necessary information can be understood at a glance. This document is then sent to the device.

[0334] The terminal displays the materials sent from the server to the user. The user can view the materials on the terminal and download them as needed. This reduces the time and effort required for users to create materials, and allows users to obtain high-quality materials efficiently.

[0335] As a concrete example, consider the case where a company's sales department is creating an annual report. The salesperson (user) inputs summary information into a terminal to instruct the creation of the report. Based on the received information, the server extracts the necessary data from the sales database and generates a graph showing monthly sales trends. Finally, this information is integrated to create an annual report in PDF format, which is provided to the salesperson via the terminal.

[0336] In this way, the system of the present invention automates consistent data processing and visual document generation, thereby providing a highly efficient document creation environment for users.

[0337] The processing flow will be explained below.

[0338] Step 1:

[0339] The user inputs the outline information of the document from the terminal. For example, the user inputs an instruction such as "Create a report based on sales data for fiscal year 2022."

[0340] Step 2:

[0341] The terminal transmits the input summary information to the server.

[0342] Step 3:

[0343] The server analyzes the received summary information, which includes using natural language processing techniques to extract keywords and identify the type and scope of required data.

[0344] Step 4:

[0345] The server extracts the necessary data from an API or database based on the analysis results. For example, it references a database that stores sales data and executes the query "SELECT FROM sales WHERE year = 2022".

[0346] Step 5:

[0347] The server organizes and processes the extracted data. During this process, the data is filtered to extract data for specific months. It also calculates the total sales for each month and sorts them chronologically.

[0348] Step 6:

[0349] Generative AI generates images and charts based on the data organized and processed on the server. For example, it uses a graph creation tool to generate a line graph showing the increase or decrease in sales.

[0350] Step 7:

[0351] The server assembles the final document based on the organized and processed data and the generated images and charts, for example, by formatting it as a PDF report so that important information can be understood at a glance.

[0352] Step 8:

[0353] The server transmits the final generated material to the terminal.

[0354] Step 9:

[0355] The terminal displays the received materials to the user, who can then view the materials through the terminal and download them as needed.

[0356] In this way, the system automatically generates high-quality materials based on the summary information entered by the user through each step.

[0357] Example 1

[0358] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0359] Traditional document creation processes are often performed manually, resulting in the time-consuming and labor-intensive process of collecting, formatting, analyzing, and creating the final document. This manual process also increases the risk of human error, potentially resulting in low-quality documents. Furthermore, creating visually easy-to-understand documents requires advanced expertise. There is a need for a system that can solve these problems and automatically create high-quality documents.

[0360] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0361] In this invention, the server includes means for analyzing summary information and identifying necessary data, means for extracting the identified data from the database, and means for organizing and processing the extracted data, which makes it possible to automatically obtain and format data based on the summary information entered by the user, and generate visually easy-to-understand materials.

[0362] A "user" is a person or entity that utilizes the system to input instructions and summary information for creating materials.

[0363] "Terminal" means an electronic device used by a user to access the system, enter summary information and display received materials.

[0364] A "server" is a central computer that processes data for the entire system, extracts, organizes, processes, and generates documents based on instructions from users.

[0365] "Summary information" is data that indicates the basic requirements and instructions for creating a document entered by the user.

[0366] A "natural language processing library" is a software tool or library that the server uses to parse the summary information entered by the user.

[0367] A "database" is a digital storage system in which tangible data is stored.

[0368] A "generative AI model" is an artificial intelligence algorithm that runs on a server and generates images and charts based on organized and processed data.

[0369] "Material" is a collection of data created by the server using a generative AI model, and is the final document provided to the user.

[0370] "Images and charts" are visual content created by generative AI models based on organized and processed data.

[0371] This invention is a generative AI integrated system that allows users to easily create high-quality documents. Based on the summary information entered by the user, the server automatically extracts, organizes, and processes data, and the generative AI generates images and charts, which are then output as documents.

[0372] The user accesses the system from a terminal and inputs summary information for the document they want to create. For example, they might input an instruction such as "Create a report based on sales data for fiscal year 2022" into the terminal. This summary information is sent from the terminal to the server. In this case, the terminal can be thought of as inputting a form using a web browser.

[0373] The server analyzes the received summary information to identify the data the system requires. This analysis is performed using a natural language processing (NLP) library (e.g., spaCy or NLTK). Based on the results of this analysis, the server accesses an API or database to extract the required data. For example, if sales data is stored in a database, the server retrieves the data using an SQL query such as "SELECT FROM sales WHERE year=2022."

[0374] The acquired data is organized and processed on the server. In this process, the data is filtered using a data analysis library (e.g., pandas) and converted into an appropriate format. For example, monthly sales data is aggregated and sorted in chronological order.

[0375] Next, a generative AI model is put into action to generate images and charts based on the organized and processed data. Examples of generative AI models that are used include OpenAI's GPT-3 and DALLE-2. This generates line graphs showing sales trends and infographics showing market trends.

[0376] The server then combines the final organized and processed data with the generated images and charts to create a document. This document is often generated in PDF format and is formatted using a PDF generation library (e.g., ReportLab). This visually organizes the necessary information so that it can be understood at a glance.

[0377] The completed materials are sent from the server to the terminal, which displays them to the user, who can then view them on the terminal and download them as needed.

[0378] As a concrete example, consider the case where a company's sales department is creating an annual report. In this case, the sales department (user) inputs summary information into a terminal to instruct the creation of the report. For example, the user inputs a prompt such as, "Please create a sales data analysis report for fiscal year 2022." The server receives this, extracts the necessary data from the sales database, generates a graph showing monthly sales trends, and finally creates an annual report in PDF format and provides it to the sales department via the terminal.

[0379] In this way, the system of the present invention automates consistent data processing and visual document generation, thereby providing a highly efficient document creation environment for users.

[0380] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0381] Step 1:

[0382] The user accesses the system from a terminal and inputs summary information for the document they want to create. For example, they input instructions such as "Create a report based on sales data for fiscal year 2022." The input here is done by the user entering summary information into a form using a web browser. The input summary information is then used as input for the next step.

[0383] Step 2:

[0384] The terminal sends the summary information entered by the user to the server. This process is performed through an HTTP request (POST method). The input of the terminal is the summary information, and the output is the HTTP request received by the server.

[0385] Step 3:

[0386] The server analyzes the received summary information. This analysis is performed using a natural language processing library (e.g., spaCy or NLTK). Specifically, it extracts keywords and phrases from the summary information to identify the required data and clarify the type and range of data. The input is the summary information received via the HTTP request, and the output is the type and range of the identified data.

[0387] Step 4:

[0388] The server extracts the specified data from the database. It accesses the database (e.g. MySQL or PostgreSQL) using an SQL query (e.g. "SELECT FROM sales WHERE year=2022") to retrieve the relevant data. The input is the type and range of the specified data, and the output is the retrieved data.

[0389] Step 5:

[0390] The server organizes and processes the extracted data. This process uses a data analysis library (e.g., pandas) to filter the data and convert it into an appropriate format. For example, this may involve aggregating monthly sales data and sorting it in chronological order. The input is the data extracted from the database, and the output is the organized and processed data.

[0391] Step 6:

[0392] A generative AI model generates images and charts based on the organized and processed data. In this step, a generative AI model (e.g., OpenAI's GPT-3 or DALLE-2) is used to generate graphs and infographics. For example, a line graph showing sales increases or decreases or an infographic showing market trends may be generated. The input is the organized and processed data, and the output is the generated images and charts.

[0393] Step 7:

[0394] The server combines the organized and processed data with the generated images and charts to create documents. In this example, a PDF generation library (e.g., ReportLab) is used to generate documents in PDF format. For example, an annual report containing sales trend graphs and text is created. The input is the organized and processed data and the generated images and charts, and the output is a document in PDF format.

[0395] Step 8:

[0396] The server sends the completed document to the terminal. This process is performed using an HTTP response. The server's input is the completed PDF document, and the output is the HTTP response to the terminal.

[0397] Step 9:

[0398] The terminal displays the received PDF document to the user. The user can view the document on the terminal and download it as needed. The input is the PDF document sent from the server, and the output is the display and download to the user.

[0399] (Application example 1)

[0400] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0401] Data processing and report creation in conventional logistics centers relied heavily on manual work, which was time-consuming and labor-intensive, and also had accuracy issues. Furthermore, data visualization was insufficient, preventing managers from providing the information they needed to make quick decisions. For this reason, there is a demand for a system that automates efficient, high-quality data processing and document creation.

[0402] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0403] In this invention, the server includes: means for a user to input summary information; means for transmitting the summary information to the server; means for the server to analyze the summary information and extract necessary data; means for the server to organize and process the extracted data; means for a generation AI to generate images and charts based on the data; means for the server to assemble materials based on the organized and processed data and the generated images and charts; means for transmitting the final materials to a terminal; means for the terminal to display the materials received to the user; means for inputting prompts to acquire necessary data and generate visual charts; means for outputting business-related information such as shipment quantities and inventory levels as graphs based on the acquired data; and means for integrating the acquired data and the generated graphs to generate materials in report format. This automates data processing and document creation in logistics centers, enabling efficient and high-quality report creation.

[0404] "User" refers to a person who uses this system to input summary information and give instructions for creating materials.

[0405] "Summary information" refers to information that includes basic information and instructions for creating materials.

[0406] "Server" refers to the computer that analyzes the summary information sent by the user, extracts, organizes, and processes the necessary data, and generates the final document.

[0407] "Generative AI" refers to artificial intelligence technology that automatically generates images and charts based on extracted, organized, and processed data.

[0408] "Prompt" refers to text that describes instructions the user gives the system to obtain the required data and generate a visual chart.

[0409] "Materials" refers to report-style documents that integrate organized and processed data and generated images and charts.

[0410] "Graph" refers to graphs and charts generated to visually display numerical data or statistical information.

[0411] "Graph" refers to a visual representation, such as a line graph or bar graph, that is generated to visually show numerical data.

[0412] "Terminal" refers to a computer or smart device used by a user to input instructions for creating materials and to view the created materials.

[0413] A "logistics center" refers to a facility that stores, manages, and ships goods.

[0414] This invention is a system for streamlining data processing and document creation in a logistics center. The system is composed of the following main components:

[0415] 1. User operations

[0416] The administrator (user) of the logistics center accesses the system using a smartphone or computer terminal. The user enters summary information for document creation into the input field on the terminal. For example, the user enters a prompt statement such as "Create a shipping data and inventory report for March."

[0417] 2. Server Operation

[0418] The server receives the summary information sent by the user. The server analyzes the summary information and accesses an API or database to extract the necessary data. This includes data on shipment numbers and inventory levels from the logistics management system database. The server then organizes and processes the retrieved data. Specifically, it calculates monthly totals for shipment numbers and average inventory levels. The Python library "Pandas" is used to filter and aggregate the data.

[0419] 3. Image and chart generation

[0420] Based on the organized and processed data, the generative AI generates images and charts. Specifically, it uses the Python "Matplotlib" library to create line graphs and bar graphs. This generative AI operates according to the prompts entered by the user, providing visual information.

[0421] 4. Creating and outputting the final materials

[0422] The server integrates the organized data with the generated images and charts to generate the final report format. This document is output in PDF format or other formats. The Python "FPDF" library is used to generate the report. The server then sends the generated report to the user's terminal. The terminal displays the received report to the user, who can then download or print it as needed.

[0423] To give a concrete example, a user might enter the following prompt sentence:

[0424] "Please create a shipping data and inventory report for March. Specifically, please include graphs that visually show the progress of shipments and average inventory."

[0425] This system automates data processing and document creation at logistics centers, enabling efficient, high-quality reports to be created quickly.

[0426] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0427] Step 1:

[0428] A user accesses the system using a terminal and inputs the outline information for creating documents as a prompt statement. For example, the user inputs the instruction "Create shipping data and inventory report for March." This input information is sent to the server through the terminal interface.

[0429] Step 2:

[0430] The server receives the summary information sent by the user. It analyzes the received information and performs analysis processing to identify the required data. Specifically, it identifies the type and range of data, including shipping data and inventory information, based on the prompt text, and prepares to access the database or API.

[0431] Step 3:

[0432] The server accesses the API or database to extract the required data identified by querying the logistics management system database to retrieve data related to shipments and inventory levels for March. The inputs are the parsed prompt results and the database query, and the output is the retrieved raw data.

[0433] Step 4:

[0434] The server cleans and processes the extracted data. Specifically, it uses the Python library Pandas to filter the data and format it as needed. This step aggregates shipments by month and averages inventory levels. The input is the extracted raw data, and the output is the formatted data.

[0435] Step 5:

[0436] The generative AI generates images and charts based on the organized data. It uses Python's Matplotlib library to create line and bar graphs. Specifically, it plots the aggregated shipment data on a line graph and the average inventory level data on a bar graph. The input is the formatted data, and the output is the generated image file.

[0437] Step 6:

[0438] The server integrates the organized data with the generated images and charts to generate the final report format document, which is output in PDF format using the Python "FPDF" library. The input is the formatted data and generated image files, and the output is a report format PDF file.

[0439] Step 7:

[0440] The server sends the generated PDF report to the user's terminal. The user can use the terminal to view the received report and download or print it as needed. The input is the generated PDF file and the output is the report displayed on the terminal.

[0441] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0442] This invention is a generative AI integrated system that allows users to easily create high-quality documents, and also combines it with an emotion engine that recognizes the user's emotions. In this system, the server automatically extracts, organizes, and processes data based on the summary information entered by the user, and the generative AI generates images and charts. The emotion engine also recognizes the user's emotional state and adapts the content and design of the document accordingly.

[0443] Users access the system from their terminals and input outline information for the materials they wish to create. This outline information is then sent from the terminal to the server.

[0444] The server analyzes the received summary information, which includes using natural language processing techniques to extract keywords and identify the type and scope of required data.

[0445] The server then accesses an API or database to extract the necessary data, which is then organized and processed within the server. This process involves filtering, aggregating, and sorting the data chronologically.

[0446] The emotion engine works here and analyzes the user's voice or text input to recognize their emotional state. For example, if the user's input has a positive tone, the emotion engine will analyze it as a positive emotion.

[0447] The generative AI generates images and charts based on the organized and processed data, taking into account the analysis results of the emotion engine. For example, if the user's emotion is positive, it generates a chart with a bright color scheme and a positive message.

[0448] The server then assembles the document based on the processed data and the generated images and charts. For example, it formats it as a PDF report, applying content and design based on the analysis results of the emotion engine. This document is then sent to the device.

[0449] The terminal displays the materials sent from the server to the user, who can then view the materials through the terminal and download them as needed.

[0450] As a concrete example, consider the case where a company's sales department is creating an annual report. The salesperson (user) inputs the instruction "Create a report based on sales data for fiscal year 2022" into their device. The server analyzes this information, extracts the necessary data from the sales database, and organizes and processes it. Furthermore, if the emotion engine analyzes the tone of the text entered by the salesperson as positive, the generative AI generates a positive message and a brightly designed graph. Finally, the annual report is completed in PDF format and provided to the salesperson via their device.

[0451] In this way, the system of the present invention not only automates consistent data processing and visual document generation, but also provides optimal content and design according to the user's emotional state, thereby providing a more advanced and efficient document creation environment for users.

[0452] The processing flow will be explained below.

[0453] Step 1:

[0454] The user inputs the outline of the document from the terminal. For example, the user inputs instructions such as "Create a report with a positive tone based on sales data for fiscal year 2022."

[0455] Step 2:

[0456] The terminal transmits the input summary information to the server.

[0457] Step 3:

[0458] The server analyzes the summary information it receives. This analysis involves using natural language processing technology to extract keywords and identify the type and scope of data required. For example, it extracts keywords such as "sales data" and "2022."

[0459] Step 4:

[0460] The server extracts the necessary data from the API or database based on the analysis results. For example, it executes the query "SELECT FROM sales WHERE year = 2022" against the sales database to retrieve sales data for 2022.

[0461] Step 5:

[0462] The server organizes and processes the extracted data. This process involves filtering the data, aggregating monthly sales, and sorting them by time. For example, 12 months of sales data can be totaled by month and sorted in chronological order.

[0463] Step 6:

[0464] The device acquires the user's emotions and sends the data to the server. For example, if the user voice-types "Please write the report in a positive tone," this voice data is sent to the server.

[0465] Step 7:

[0466] The server's emotion engine analyzes the user's emotion data. For example, it recognizes the emotion as positive from the voice data and records the analysis result.

[0467] Step 8:

[0468] The generative AI generates images and charts based on the analysis results of the emotion engine, using data organized and processed on the server. For example, it generates line graphs with a positive design and infographics with a bright tone.

[0469] Step 9:

[0470] The server assembles the final document based on the organized and processed data and the generated images and charts, for example, creating a PDF report with a positive message and a bright design.

[0471] Step 10:

[0472] The server transmits the final generated material to the terminal.

[0473] Step 11:

[0474] The terminal displays the received documents to the user. The user can view the documents through the terminal, check their contents, and download them as needed. For example, a sales department employee displays the generated annual report on the terminal and checks its contents.

[0475] In this way, the system of the present invention automatically generates optimal materials based on the summary information and emotion data entered by the user through a series of steps, allowing users to not only efficiently create high-quality materials, but also receive materials with content and design appropriate to their emotions.

[0476] Example 2

[0477] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0478] In today's world, it is important for users to create high-quality documents quickly. However, with conventional systems, it has been difficult to consistently automate the process of extracting, organizing, and processing data, as well as generating visual graphics. Furthermore, there has been no system that can provide optimal design and content based on the user's emotional state. As a result, users have had to spend a lot of time and effort creating documents.

[0479] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input summary information; a means for transmitting the summary information to the server; a means for the server to analyze the summary information and extract necessary data; a means for the server to organize and process the extracted data; a means for an emotion engine to analyze the user's emotional state; a means for a generation AI to generate images and charts based on the data and the emotion analysis results; a means for the server to assemble materials based on the organized and processed data and the generated images and charts; a means for transmitting the final materials to a terminal; and a means for the terminal to display the received materials to the user. This enables users to quickly create high-quality materials through a consistent, automated process. In addition, user satisfaction can be improved by providing materials with optimal design and content based on the user's emotional state.

[0480] A "user" is an entity that accesses the system and inputs summary information for the creation of materials.

[0481] A "terminal" is a hardware device or software environment that is operated by a user and that communicates with a server.

[0482] The "server" is a central processing unit that analyzes the summary information received from the user, extracts the necessary data, organizes and processes it, generates images and charts using AI, and assembles materials.

[0483] "Summary information" refers to the original information that the user inputs to create a document, and includes specific data and instructions.

[0484] "Analysis" is the process of identifying keywords and the type and range of required data from the summary information.

[0485] "Data" refers to information elements such as numbers, text, and images required to create the materials desired by the user.

[0486] "Extraction" is the process of retrieving data identified through analysis from external resources such as APIs or databases.

[0487] "Organization" is the process of organizing the extracted data by filtering, aggregating, sorting, etc.

[0488] "Processing" is the process of converting organized data into a form that is easier to handle visually or logically.

[0489] An "emotion engine" is a combination of software or hardware that analyzes a user's input and behavior to determine their current emotional state.

[0490] "Generative AI" is an artificial intelligence model that automatically generates images and charts contained in documents based on organized and processed data and the results of sentiment analysis.

[0491] "Images and diagrams" are graphic elements created by generative AI to represent visual information.

[0492] A "material" is a formatted collection of information such as a report, presentation, or document that is ultimately provided to a user.

[0493] "Transmission" is the communication act in which the server transfers the final material to the terminal.

[0494] "Display" refers to the process in which the terminal visually presents the received materials to the user.

[0495] MODE FOR CARRYING OUT THE INVENTION

[0496] This invention is a generative AI integrated system that allows users to easily create high-quality materials, and also combines it with an emotion engine that recognizes the user's emotions. This system has the following hardware and software configuration.

[0497] Hardware Configuration

[0498] 1. Terminal: The device operated by the user (e.g., PC, tablet, smartphone).

[0499] 2. Server: A central processing unit that analyzes, extracts, and processes data for AI generation.

[0500] Software Configuration

[0501] 1. Natural language processing libraries: Python-based nltk and spaCy are used to analyze summary information.

[0502] 2. Database management system: Use MySQL or PostgreSQL to manage the necessary data.

[0503] 3. Pandas library: Used to filter, aggregate, and time-order the extracted data.

[0504] 4. Sentiment Analysis Tool: Analyze the user's emotional state using NLTK's VADER Sentiment Analysis.

[0505] 5. Generative AI model: Using OpenAI's GPT-3 and DALL-E, images and charts are generated based on user input data and sentiment analysis results.

[0506] 6. Report generation library: Use the Python ReportLab library to compile reports in PDF format.

[0507] Implementation method

[0508] The user accesses the system from a terminal and inputs the outline of the document they want to create. Specifically, they input the instruction to "create a report based on sales data for fiscal year 2022." The terminal then sends this outline to the server.

[0509] The server then analyzes the received summary information using a Python-based natural language processing library. During this analysis, keywords such as "2022 fiscal year," "sales," and "report" are extracted to identify the type and scope of data required.

[0510] The server then accesses an API or database to extract sales data, which it then uses the Pandas library to filter, aggregate, and sort (for example, to sum sales data by month for January through December of fiscal year 2022).

[0511] In parallel, the emotion engine analyzes the user's input text and voice data. It uses NLTK's VADER Sentiment Analysis tool to identify the user's emotional state. For example, if the input text has a positive tone, it will be analyzed as having a positive emotion.

[0512] Based on this organized and processed data and the results of emotion analysis, the generative AI generates images and charts. If the user's emotional state is positive, the generative AI will generate charts with bright colors and positive messages.

[0513] The server then assembles the final document based on the generated images, charts, and organized and processed data. For example, it compiles the data into a PDF annual report using Python's ReportLab library. The document's design and content are adapted to the user's emotional state.

[0514] Finally, the server sends the completed document to the terminal, which displays it to the user, who can then view the document and download it as needed.

[0515] Prompt Sentence Examples

[0516] An example of a prompt sentence to input to the generative AI model is as follows:

[0517] "Create an annual report based on your 2022 sales data, with positive messaging and brightly designed graphs."

[0518] In this way, the system is designed to enable users to quickly and easily create high-quality materials, while also providing optimal design and content according to the user's emotional state.

[0519] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0520] Step 1:

[0521] A user accesses the system using a terminal and inputs the outline of the document they want to create. For example, they might input, "Create an annual report based on sales data for fiscal year 2022." This input is sent to the server through the terminal's user interface.

[0522] Step 2:

[0523] The terminal sends the summary information entered by the user to the server, which receives the input data in text format.

[0524] Step 3:

[0525] The server analyzes the received summary information using Python's natural language processing library (nltk or spaCy) to extract keywords such as "2022," "sales," and "annual report," and identify the type and scope of the data. The input to this analysis process is the text data of the summary information, and the output is the extracted keywords and data range.

[0526] Step 4:

[0527] The server extracts the necessary data from a database (MySQL or PostgreSQL) or API based on the specified keywords and data range. For example, it extracts "sales data" for "fiscal year 2022." The input for this process is the specified keywords and data range, and the output is the corresponding sales data.

[0528] Step 5:

[0529] The server organizes and processes the extracted data using the Pandas library. Specifically, it filters, aggregates, and sorts the data by month. The input to this process is the extracted sales data, and the output is the organized and processed data.

[0530] Step 6:

[0531] The server's emotion engine analyzes the user's input text. It uses NLTK's VADER Sentiment Analysis tool to identify the user's emotional state. For example, if the input text has a positive tone, it outputs a positive emotion. The input of this process is the user's text data, and the output is the emotional state.

[0532] Step 7:

[0533] The server's generative AI (GPT-3 or DALL-E) generates images and charts based on the organized and processed data and the results of sentiment analysis. Based on the emotional state, the generative AI selects designs such as brightly colored charts and positive messages. The inputs to this process are the organized and processed data and the emotional state, and the output is images and charts.

[0534] Step 8:

[0535] The server assembles documents using the generated images and charts, along with the organized and processed data. For example, it creates a PDF report using the Python ReportLab library. The inputs to this process are the images, charts, and organized and processed data, and the output is the final document (PDF report).

[0536] Step 9:

[0537] The server sends the completed document to the terminal. The input of this sending process is the completed document, and the output is the successful transfer of the document to the terminal.

[0538] Step 10:

[0539] The terminal displays the received material to the user, who can then view and download it as needed. The input to this process is the material sent from the server, and the output is the material displayed to the user.

[0540] Through these specific processing steps, the present invention provides a process for users to create efficient and high-quality materials, and realizes optimal document generation according to the needs and feelings of the user.

[0541] (Application example 2)

[0542] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0543] Conventional document creation systems require users to manually create the content and design of documents, which is time-consuming and labor-intensive and inefficient. Furthermore, dynamic document generation that takes into account the user's emotional state is difficult, making it impossible to provide optimal documents for individual users. The present invention aims to solve these problems by providing a system that allows users to easily create high-quality documents and provides optimal documents that correspond to the user's emotional state.

[0544] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input summary information; means for transmitting the summary information to the server; means for the server to analyze the summary information and extract necessary data; means for the server to organize and process the extracted data; means for a generation AI to generate images and charts based on the data; means for the server to assemble materials based on the organized and processed data and the generated images and charts; means for transmitting the final materials to the terminal; means for the terminal to display the received materials to the user; means for analyzing the user's emotional state using emotion analysis means; and means for the generation AI to adapt the content and design of the materials based on the user's emotional state. This makes it possible to easily and automatically generate dynamic, high-quality materials that take the user's emotional state into consideration.

[0545] "Users" are people or organizations that use this system and create materials.

[0546] "Summary information" refers to basic data and instructions regarding the content and theme of the material the user wants to create.

[0547] The "server" is a computer system that performs centralized calculations and management to analyze the input summary information, extract, organize, and process the necessary data, and generate the final materials.

[0548] "Data Extraction Method" refers to the technology or process that retrieves the data required from the Summary Information from the API or database.

[0549] "Means of organizing and processing" refers to the techniques or processes that filter, aggregate, sort, or otherwise format the extracted data for a specific purpose.

[0550] "Generative AI" is an artificial intelligence technology that generates images and charts based on organized and processed data, and determines the content and design of materials.

[0551] "Emotion analysis means" is a technology that analyzes the user's emotional state from voice or text input, and reflects the analysis results in the system.

[0552] "Means for assembling materials" refers to the technology or process for compiling the final materials into a single document format based on the images and diagrams created by the generative AI.

[0553] "Terminal" refers to a computer, smartphone, or other digital device used by a user.

[0554] "Transmission means" refers to the technology or process for transmitting the final material from the server to the terminal.

[0555] "Display means" refers to the technique or process by which the final material is displayed to the user on the terminal.

[0556] This invention is a system that generates high-quality driving analysis reports so that users can easily understand their daily driving conditions in autonomous vehicles. This system collects sensor data and driving situation data from autonomous vehicles in real time, analyzes them on a server, and with the cooperation of a generation AI model, provides users with an optimized driving report. Furthermore, by using emotion analysis means, the system adjusts the design and content according to the user's emotional state.

[0557] The server first receives and analyzes the summary information entered by the user. It then extracts the necessary data from an API or database, and organizes and processes the extracted data by filtering, aggregating, and sorting it chronologically. Based on this organized and processed data, the generative AI generates images and charts. Based on the generated images, charts, and text data, the server assembles the final document in PDF or HTML format.

[0558] In particular, for owners of autonomous vehicles, data is collected from various sensors inside the vehicle (speed sensor, GPS sensor, lidar sensor), and by analyzing the driver's emotional state in real time using a camera, the content and design of the generated driving report are adapted to the user's emotional state.

[0559] As a specific use case, consider the case where a user launches the application and inputs, "I would like to create a driving safety report today." Data such as speed data, location data, driving patterns, and the driver's emotional state are collected from the autonomous vehicle. Based on this data, the server creates a driving safety report by sending the following prompt sentence to the generation AI.

[0560] Example prompt sentence:

[0561] I want to write a driving safety report today.

[0562] Data collected:

[0563] Speed ​​data: average speed 60 km / h, maximum speed 100 km / h

[0564] Location data: urban, suburban

[0565] Driving pattern: sudden acceleration 3 times, sudden braking 2 times

[0566] Driver's emotional state: Slightly stressed facial expression

[0567] Based on this, prepare a driving safety report and summarize it in language that is easy for the driver to understand.

[0568] The server analyzes this data and generates a driving report based on a generative AI model (e.g., OpenAI's GPT-4). The report is then sent to the user's smartphone or car's instrument panel display and displayed to the user, who can then receive specific advice on how to improve their driving safety.

[0569] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0570] Step 1:

[0571] The user inputs summary information. Using a terminal in the autonomous vehicle or a smartphone, the user inputs instructions such as, "I would like to create a driving safety report today." This input becomes the initial data for the system.

[0572] Step 2:

[0573] The terminal sends the input summary information to the server. When the user inputs the summary information, the terminal sends this information to the server. At this time, the input summary information is "I would like to create a driving safety report."

[0574] Step 3:

[0575] The server analyzes the summary information and extracts the necessary data. The server then analyzes the received summary information using natural language processing technology and extracts the necessary data categories (speed data, location data, driving patterns, etc.). The output is a categorization of the necessary data for the summary information input.

[0576] Step 4:

[0577] The server collects various sensor data from the vehicle. Based on the extracted data type, the server collects data from various sensors in the vehicle (speed sensor, GPS sensor, lidar sensor, etc.). The input is a categorized data request, and the output is specific sensor data.

[0578] Step 5:

[0579] The server analyzes the user's emotional state using emotion analysis means. The driver's facial expressions are captured using a camera in the vehicle and analyzed using the Emotion API. The input is the captured image data, and the output is the driver's emotional state.

[0580] Step 6:

[0581] The server organizes and processes the collected sensor data and emotion data. It uses a data analysis library (e.g., Python's pandas or numpy) to filter, aggregate, and sort the data. The input is the collected sensor data and emotion data, and the output is the organized and processed data.

[0582] Step 7:

[0583] The generation AI generates prompt sentences based on the organized and processed data and generates the contents of the driving report.Using the generation AI (e.g., OpenAI GPT-4), the following prompt sentences are prepared and the contents of the driving report are generated.

[0584] Example prompt:

[0585] I want to write a driving safety report today.

[0586] Data collected:

[0587] Speed ​​data: average speed 60 km / h, maximum speed 100 km / h

[0588] Location data: urban, suburban

[0589] Driving pattern: sudden acceleration 3 times, sudden braking 2 times

[0590] Driver's emotional state: Slightly stressed facial expression

[0591] Based on this, prepare a driving safety report and summarize it in language that is easy for the driver to understand.

[0592] The input is the organized and processed data and prompt statements, and the output is the content of the generated driving report.

[0593] Step 8:

[0594] The server assembles a driving report based on the generated images, charts, and text data. It integrates the images, charts, and text information obtained from the generation AI into PDF or HTML format. The input is the content of the generated driving report, and the output is the final driving report.

[0595] Step 9:

[0596] The server sends the final driving report to the terminal. The completed driving report (PDF or HTML format) is sent to the user's terminal. The input is the driving report, and the output is the completion of sending it to the user's terminal.

[0597] Step 10:

[0598] The terminal displays the received driving report to the user. The terminal displays the received driving report on the display, which the user can view. The input is the received driving report, and the output is the user's visual confirmation.

[0599] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0600] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0601] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0602] [Third embodiment]

[0603] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0604] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0605] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0606] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0607] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0608] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0609] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0610] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0611] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0612] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0613] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0614] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0615] This invention is a generative AI integrated system that allows users to easily create high-quality documents. In this system, the server automatically extracts, organizes, and processes data based on the summary information entered by the user, and the generative AI generates images and charts, which are then output as documents.

[0616] The user accesses the system from a terminal and inputs the outline of the document they want to create. For example, they might input an instruction such as "Create a report based on sales data for fiscal year 2022." This outline is then sent to the server.

[0617] The server analyzes the received summary information and identifies the data the system needs. Based on this analysis, the server accesses an API or database to extract the required data. For example, if sales data is in a database, the server retrieves the relevant data using a query.

[0618] The acquired data is organized and processed on the server. In this process, the data is filtered and converted into an appropriate format. For example, monthly sales data is aggregated and sorted chronologically. In this way, the data is organized into a form that is easy to understand visually.

[0619] Next, generative AI works to generate images and charts based on the organized and processed data. For example, it can generate line graphs showing sales trends or infographics showing market trends. The generated images and charts are then incorporated as an integral part of the document.

[0620] The server then combines the final organized and processed data with the generated images and charts to create a document, such as a PDF report, so that the necessary information can be understood at a glance. This document is then sent to the device.

[0621] The terminal displays the materials sent from the server to the user. The user can view the materials on the terminal and download them as needed. This reduces the time and effort required for users to create materials, and allows users to obtain high-quality materials efficiently.

[0622] As a concrete example, consider the case where a company's sales department is creating an annual report. The salesperson (user) inputs summary information into a terminal to instruct the creation of the report. Based on the received information, the server extracts the necessary data from the sales database and generates a graph showing monthly sales trends. Finally, this information is integrated to create an annual report in PDF format, which is provided to the salesperson via the terminal.

[0623] In this way, the system of the present invention automates consistent data processing and visual document generation, thereby providing a highly efficient document creation environment for users.

[0624] The processing flow will be explained below.

[0625] Step 1:

[0626] The user inputs the outline information of the document from the terminal. For example, the user inputs an instruction such as "Create a report based on sales data for fiscal year 2022."

[0627] Step 2:

[0628] The terminal transmits the input summary information to the server.

[0629] Step 3:

[0630] The server analyzes the received summary information, which includes using natural language processing techniques to extract keywords and identify the type and scope of required data.

[0631] Step 4:

[0632] The server extracts the necessary data from an API or database based on the analysis results. For example, it references a database that stores sales data and executes the query "SELECT FROM sales WHERE year = 2022".

[0633] Step 5:

[0634] The server organizes and processes the extracted data. During this process, the data is filtered to extract data for specific months. It also calculates the total sales for each month and sorts them chronologically.

[0635] Step 6:

[0636] Generative AI generates images and charts based on the data organized and processed on the server. For example, it uses a graph creation tool to generate a line graph showing the increase or decrease in sales.

[0637] Step 7:

[0638] The server assembles the final document based on the organized and processed data and the generated images and charts, for example, by formatting it as a PDF report so that important information can be understood at a glance.

[0639] Step 8:

[0640] The server transmits the final generated material to the terminal.

[0641] Step 9:

[0642] The terminal displays the received materials to the user, who can then view the materials through the terminal and download them as needed.

[0643] In this way, the system automatically generates high-quality materials based on the summary information entered by the user through each step.

[0644] Example 1

[0645] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0646] Traditional document creation processes are often performed manually, resulting in the time-consuming and labor-intensive process of collecting, formatting, analyzing, and creating the final document. This manual process also increases the risk of human error, potentially resulting in low-quality documents. Furthermore, creating visually easy-to-understand documents requires advanced expertise. There is a need for a system that can solve these problems and automatically create high-quality documents.

[0647] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0648] In this invention, the server includes means for analyzing summary information and identifying necessary data, means for extracting the identified data from the database, and means for organizing and processing the extracted data, which makes it possible to automatically obtain and format data based on the summary information entered by the user, and generate visually easy-to-understand materials.

[0649] A "user" is a person or entity that utilizes the system to input instructions and summary information for creating materials.

[0650] "Terminal" means an electronic device used by a user to access the system, enter summary information and display received materials.

[0651] A "server" is a central computer that processes data for the entire system, extracts, organizes, processes, and generates documents based on instructions from users.

[0652] "Summary information" is data that indicates the basic requirements and instructions for creating a document entered by the user.

[0653] A "natural language processing library" is a software tool or library that the server uses to parse the summary information entered by the user.

[0654] A "database" is a digital storage system in which tangible data is stored.

[0655] A "generative AI model" is an artificial intelligence algorithm that runs on a server and generates images and charts based on organized and processed data.

[0656] "Material" is a collection of data created by the server using a generative AI model, and is the final document provided to the user.

[0657] "Images and charts" are visual content created by generative AI models based on organized and processed data.

[0658] This invention is a generative AI integrated system that allows users to easily create high-quality documents. Based on the summary information entered by the user, the server automatically extracts, organizes, and processes data, and the generative AI generates images and charts, which are then output as documents.

[0659] The user accesses the system from a terminal and inputs summary information for the document they want to create. For example, they might input an instruction such as "Create a report based on sales data for fiscal year 2022" into the terminal. This summary information is sent from the terminal to the server. In this case, the terminal can be thought of as inputting a form using a web browser.

[0660] The server analyzes the received summary information to identify the data the system requires. This analysis is performed using a natural language processing (NLP) library (e.g., spaCy or NLTK). Based on the results of this analysis, the server accesses an API or database to extract the required data. For example, if sales data is stored in a database, the server retrieves the data using an SQL query such as "SELECT FROM sales WHERE year=2022."

[0661] The acquired data is organized and processed on the server. In this process, the data is filtered using a data analysis library (e.g., pandas) and converted into an appropriate format. For example, monthly sales data is aggregated and sorted in chronological order.

[0662] Next, a generative AI model is put into action to generate images and charts based on the organized and processed data. Examples of generative AI models that are used include OpenAI's GPT-3 and DALLE-2. This generates line graphs showing sales trends and infographics showing market trends.

[0663] The server then combines the final organized and processed data with the generated images and charts to create a document. This document is often generated in PDF format and is formatted using a PDF generation library (e.g., ReportLab). This visually organizes the necessary information so that it can be understood at a glance.

[0664] The completed materials are sent from the server to the terminal, which displays them to the user, who can then view them on the terminal and download them as needed.

[0665] As a concrete example, consider the case where a company's sales department is creating an annual report. In this case, the sales department (user) inputs summary information into a terminal to instruct the creation of the report. For example, the user inputs a prompt such as, "Please create a sales data analysis report for fiscal year 2022." The server receives this, extracts the necessary data from the sales database, generates a graph showing monthly sales trends, and finally creates an annual report in PDF format and provides it to the sales department via the terminal.

[0666] In this way, the system of the present invention automates consistent data processing and visual document generation, thereby providing a highly efficient document creation environment for users.

[0667] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0668] Step 1:

[0669] The user accesses the system from a terminal and inputs summary information for the document they want to create. For example, they input instructions such as "Create a report based on sales data for fiscal year 2022." The input here is done by the user entering summary information into a form using a web browser. The input summary information is then used as input for the next step.

[0670] Step 2:

[0671] The terminal sends the summary information entered by the user to the server. This process is performed through an HTTP request (POST method). The input of the terminal is the summary information, and the output is the HTTP request received by the server.

[0672] Step 3:

[0673] The server analyzes the received summary information. This analysis is performed using a natural language processing library (e.g., spaCy or NLTK). Specifically, it extracts keywords and phrases from the summary information to identify the required data and clarify the type and range of data. The input is the summary information received via the HTTP request, and the output is the type and range of the identified data.

[0674] Step 4:

[0675] The server extracts the specified data from the database. It accesses the database (e.g. MySQL or PostgreSQL) using an SQL query (e.g. "SELECT FROM sales WHERE year=2022") to retrieve the relevant data. The input is the type and range of the specified data, and the output is the retrieved data.

[0676] Step 5:

[0677] The server organizes and processes the extracted data. This process uses a data analysis library (e.g., pandas) to filter the data and convert it into an appropriate format. For example, this may involve aggregating monthly sales data and sorting it in chronological order. The input is the data extracted from the database, and the output is the organized and processed data.

[0678] Step 6:

[0679] A generative AI model generates images and charts based on the organized and processed data. In this step, a generative AI model (e.g., OpenAI's GPT-3 or DALLE-2) is used to generate graphs and infographics. For example, a line graph showing sales increases or decreases or an infographic showing market trends may be generated. The input is the organized and processed data, and the output is the generated images and charts.

[0680] Step 7:

[0681] The server combines the organized and processed data with the generated images and charts to create documents. In this example, a PDF generation library (e.g., ReportLab) is used to generate documents in PDF format. For example, an annual report containing sales trend graphs and text is created. The input is the organized and processed data and the generated images and charts, and the output is a document in PDF format.

[0682] Step 8:

[0683] The server sends the completed document to the terminal. This process is performed using an HTTP response. The server's input is the completed PDF document, and the output is the HTTP response to the terminal.

[0684] Step 9:

[0685] The terminal displays the received PDF document to the user. The user can view the document on the terminal and download it as needed. The input is the PDF document sent from the server, and the output is the display and download to the user.

[0686] (Application example 1)

[0687] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0688] Data processing and report creation in conventional logistics centers relied heavily on manual work, which was time-consuming and labor-intensive, and also had accuracy issues. Furthermore, data visualization was insufficient, preventing managers from providing the information they needed to make quick decisions. For this reason, there is a demand for a system that automates efficient, high-quality data processing and document creation.

[0689] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0690] In this invention, the server includes: means for a user to input summary information; means for transmitting the summary information to the server; means for the server to analyze the summary information and extract necessary data; means for the server to organize and process the extracted data; means for a generation AI to generate images and charts based on the data; means for the server to assemble materials based on the organized and processed data and the generated images and charts; means for transmitting the final materials to a terminal; means for the terminal to display the materials received to the user; means for inputting prompts to acquire necessary data and generate visual charts; means for outputting business-related information such as shipment quantities and inventory levels as graphs based on the acquired data; and means for integrating the acquired data and the generated graphs to generate materials in report format. This automates data processing and document creation in logistics centers, enabling efficient and high-quality report creation.

[0691] "User" refers to a person who uses this system to input summary information and give instructions for creating materials.

[0692] "Summary information" refers to information that includes basic information and instructions for creating materials.

[0693] "Server" refers to the computer that analyzes the summary information sent by the user, extracts, organizes, and processes the necessary data, and generates the final document.

[0694] "Generative AI" refers to artificial intelligence technology that automatically generates images and charts based on extracted, organized, and processed data.

[0695] "Prompt" refers to text that describes instructions the user gives the system to obtain the required data and generate a visual chart.

[0696] "Materials" refers to report-style documents that integrate organized and processed data and generated images and charts.

[0697] "Graph" refers to graphs and charts generated to visually display numerical data or statistical information.

[0698] "Graph" refers to a visual representation, such as a line graph or bar graph, that is generated to visually show numerical data.

[0699] "Terminal" refers to a computer or smart device used by a user to input instructions for creating materials and to view the created materials.

[0700] A "logistics center" refers to a facility that stores, manages, and ships goods.

[0701] This invention is a system for streamlining data processing and document creation in a logistics center. The system is composed of the following main components:

[0702] 1. User operations

[0703] The administrator (user) of the logistics center accesses the system using a smartphone or computer terminal. The user enters summary information for document creation into the input field on the terminal. For example, the user enters a prompt statement such as "Create a shipping data and inventory report for March."

[0704] 2. Server Operation

[0705] The server receives the summary information sent by the user. The server analyzes the summary information and accesses an API or database to extract the necessary data. This includes data on shipment numbers and inventory levels from the logistics management system database. The server then organizes and processes the retrieved data. Specifically, it calculates monthly totals for shipment numbers and average inventory levels. The Python library "Pandas" is used to filter and aggregate the data.

[0706] 3. Image and chart generation

[0707] Based on the organized and processed data, the generative AI generates images and charts. Specifically, it uses the Python "Matplotlib" library to create line graphs and bar graphs. This generative AI operates according to the prompts entered by the user, providing visual information.

[0708] 4. Creating and outputting the final materials

[0709] The server integrates the organized data with the generated images and charts to generate the final report format. This document is output in PDF format or other formats. The Python "FPDF" library is used to generate the report. The server then sends the generated report to the user's terminal. The terminal displays the received report to the user, who can then download or print it as needed.

[0710] To give a concrete example, a user might enter the following prompt sentence:

[0711] "Please create a shipping data and inventory report for March. Specifically, please include graphs that visually show the progress of shipments and average inventory."

[0712] This system automates data processing and document creation at logistics centers, enabling efficient, high-quality reports to be created quickly.

[0713] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0714] Step 1:

[0715] A user accesses the system using a terminal and inputs the outline information for creating documents as a prompt statement. For example, the user inputs the instruction "Create shipping data and inventory report for March." This input information is sent to the server through the terminal interface.

[0716] Step 2:

[0717] The server receives the summary information sent by the user. It analyzes the received information and performs analysis processing to identify the required data. Specifically, it identifies the type and range of data, including shipping data and inventory information, based on the prompt text, and prepares to access the database or API.

[0718] Step 3:

[0719] The server accesses the API or database to extract the required data identified by querying the logistics management system database to retrieve data related to shipments and inventory levels for March. The inputs are the parsed prompt results and the database query, and the output is the retrieved raw data.

[0720] Step 4:

[0721] The server cleans and processes the extracted data. Specifically, it uses the Python library Pandas to filter the data and format it as needed. This step aggregates shipments by month and averages inventory levels. The input is the extracted raw data, and the output is the formatted data.

[0722] Step 5:

[0723] The generative AI generates images and charts based on the organized data. It uses Python's Matplotlib library to create line and bar graphs. Specifically, it plots the aggregated shipment data on a line graph and the average inventory level data on a bar graph. The input is the formatted data, and the output is the generated image file.

[0724] Step 6:

[0725] The server integrates the organized data with the generated images and charts to generate the final report format document, which is output in PDF format using the Python "FPDF" library. The input is the formatted data and generated image files, and the output is a report format PDF file.

[0726] Step 7:

[0727] The server sends the generated PDF report to the user's terminal. The user can use the terminal to view the received report and download or print it as needed. The input is the generated PDF file and the output is the report displayed on the terminal.

[0728] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0729] This invention is a generative AI integrated system that allows users to easily create high-quality documents, and also combines it with an emotion engine that recognizes the user's emotions. In this system, the server automatically extracts, organizes, and processes data based on the summary information entered by the user, and the generative AI generates images and charts. The emotion engine also recognizes the user's emotional state and adapts the content and design of the document accordingly.

[0730] Users access the system from their terminals and input outline information for the materials they wish to create. This outline information is then sent from the terminal to the server.

[0731] The server analyzes the received summary information, which includes using natural language processing techniques to extract keywords and identify the type and scope of required data.

[0732] The server then accesses an API or database to extract the necessary data, which is then organized and processed within the server. This process involves filtering, aggregating, and sorting the data chronologically.

[0733] The emotion engine works here and analyzes the user's voice or text input to recognize their emotional state. For example, if the user's input has a positive tone, the emotion engine will analyze it as a positive emotion.

[0734] The generative AI generates images and charts based on the organized and processed data, taking into account the analysis results of the emotion engine. For example, if the user's emotion is positive, it generates a chart with a bright color scheme and a positive message.

[0735] The server then assembles the document based on the processed data and the generated images and charts. For example, it formats it as a PDF report, applying content and design based on the analysis results of the emotion engine. This document is then sent to the device.

[0736] The terminal displays the materials sent from the server to the user, who can then view the materials through the terminal and download them as needed.

[0737] As a concrete example, consider the case where a company's sales department is creating an annual report. The salesperson (user) inputs the instruction "Create a report based on sales data for fiscal year 2022" into their device. The server analyzes this information, extracts the necessary data from the sales database, and organizes and processes it. Furthermore, if the emotion engine analyzes the tone of the text entered by the salesperson as positive, the generative AI generates a positive message and a brightly designed graph. Finally, the annual report is completed in PDF format and provided to the salesperson via their device.

[0738] In this way, the system of the present invention not only automates consistent data processing and visual document generation, but also provides optimal content and design according to the user's emotional state, thereby providing a more advanced and efficient document creation environment for users.

[0739] The processing flow will be explained below.

[0740] Step 1:

[0741] The user inputs the outline of the document from the terminal. For example, the user inputs instructions such as "Create a report with a positive tone based on sales data for fiscal year 2022."

[0742] Step 2:

[0743] The terminal transmits the input summary information to the server.

[0744] Step 3:

[0745] The server analyzes the summary information it receives. This analysis involves using natural language processing technology to extract keywords and identify the type and scope of data required. For example, it extracts keywords such as "sales data" and "2022."

[0746] Step 4:

[0747] The server extracts the necessary data from the API or database based on the analysis results. For example, it executes the query "SELECT FROM sales WHERE year = 2022" against the sales database to retrieve sales data for 2022.

[0748] Step 5:

[0749] The server organizes and processes the extracted data. This process involves filtering the data, aggregating monthly sales, and sorting them by time. For example, 12 months of sales data can be totaled by month and sorted in chronological order.

[0750] Step 6:

[0751] The device acquires the user's emotions and sends the data to the server. For example, if the user voice-types "Please write the report in a positive tone," this voice data is sent to the server.

[0752] Step 7:

[0753] The server's emotion engine analyzes the user's emotion data. For example, it recognizes the emotion as positive from the voice data and records the analysis result.

[0754] Step 8:

[0755] The generative AI generates images and charts based on the analysis results of the emotion engine, using data organized and processed on the server. For example, it generates line graphs with a positive design and infographics with a bright tone.

[0756] Step 9:

[0757] The server assembles the final document based on the organized and processed data and the generated images and charts, for example, creating a PDF report with a positive message and a bright design.

[0758] Step 10:

[0759] The server transmits the final generated material to the terminal.

[0760] Step 11:

[0761] The terminal displays the received documents to the user. The user can view the documents through the terminal, check their contents, and download them as needed. For example, a sales department employee displays the generated annual report on the terminal and checks its contents.

[0762] In this way, the system of the present invention automatically generates optimal materials based on the summary information and emotion data entered by the user through a series of steps, allowing users to not only efficiently create high-quality materials, but also receive materials with content and design appropriate to their emotions.

[0763] Example 2

[0764] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0765] In today's world, it is important for users to create high-quality documents quickly. However, with conventional systems, it has been difficult to consistently automate the process of extracting, organizing, and processing data, as well as generating visual graphics. Furthermore, there has been no system that can provide optimal design and content based on the user's emotional state. As a result, users have had to spend a lot of time and effort creating documents.

[0766] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input summary information; a means for transmitting the summary information to the server; a means for the server to analyze the summary information and extract necessary data; a means for the server to organize and process the extracted data; a means for an emotion engine to analyze the user's emotional state; a means for a generation AI to generate images and charts based on the data and the emotion analysis results; a means for the server to assemble materials based on the organized and processed data and the generated images and charts; a means for transmitting the final materials to a terminal; and a means for the terminal to display the received materials to the user. This enables users to quickly create high-quality materials through a consistent, automated process. In addition, user satisfaction can be improved by providing materials with optimal design and content based on the user's emotional state.

[0767] A "user" is an entity that accesses the system and inputs summary information for the creation of materials.

[0768] A "terminal" is a hardware device or software environment that is operated by a user and that communicates with a server.

[0769] The "server" is a central processing unit that analyzes the summary information received from the user, extracts the necessary data, organizes and processes it, generates images and charts using AI, and assembles materials.

[0770] "Summary information" refers to the original information that the user inputs to create a document, and includes specific data and instructions.

[0771] "Analysis" is the process of identifying keywords and the type and range of required data from the summary information.

[0772] "Data" refers to information elements such as numbers, text, and images required to create the materials desired by the user.

[0773] "Extraction" is the process of retrieving data identified through analysis from external resources such as APIs or databases.

[0774] "Organization" is the process of organizing the extracted data by filtering, aggregating, sorting, etc.

[0775] "Processing" is the process of converting organized data into a form that is easier to handle visually or logically.

[0776] An "emotion engine" is a combination of software or hardware that analyzes a user's input and behavior to determine their current emotional state.

[0777] "Generative AI" is an artificial intelligence model that automatically generates images and charts contained in documents based on organized and processed data and the results of sentiment analysis.

[0778] "Images and diagrams" are graphic elements created by generative AI to represent visual information.

[0779] A "material" is a formatted collection of information such as a report, presentation, or document that is ultimately provided to a user.

[0780] "Transmission" is the communication act in which the server transfers the final material to the terminal.

[0781] "Display" refers to the process in which the terminal visually presents the received materials to the user.

[0782] MODE FOR CARRYING OUT THE INVENTION

[0783] This invention is a generative AI integrated system that allows users to easily create high-quality materials, and also combines it with an emotion engine that recognizes the user's emotions. This system has the following hardware and software configuration.

[0784] Hardware Configuration

[0785] 1. Terminal: The device operated by the user (e.g., PC, tablet, smartphone).

[0786] 2. Server: A central processing unit that analyzes, extracts, and processes data for AI generation.

[0787] Software Configuration

[0788] 1. Natural language processing libraries: Python-based nltk and spaCy are used to analyze summary information.

[0789] 2. Database management system: Use MySQL or PostgreSQL to manage the necessary data.

[0790] 3. Pandas library: Used to filter, aggregate, and time-order the extracted data.

[0791] 4. Sentiment Analysis Tool: Analyze the user's emotional state using NLTK's VADER Sentiment Analysis.

[0792] 5. Generative AI model: Using OpenAI's GPT-3 and DALL-E, images and charts are generated based on user input data and sentiment analysis results.

[0793] 6. Report generation library: Use the Python ReportLab library to compile reports in PDF format.

[0794] Implementation method

[0795] The user accesses the system from a terminal and inputs the outline of the document they want to create. Specifically, they input the instruction to "create a report based on sales data for fiscal year 2022." The terminal then sends this outline to the server.

[0796] The server then analyzes the received summary information using a Python-based natural language processing library. During this analysis, keywords such as "2022 fiscal year," "sales," and "report" are extracted to identify the type and scope of data required.

[0797] The server then accesses an API or database to extract sales data, which it then uses the Pandas library to filter, aggregate, and sort (for example, to sum sales data by month for January through December of fiscal year 2022).

[0798] In parallel, the emotion engine analyzes the user's input text and voice data. It uses NLTK's VADER Sentiment Analysis tool to identify the user's emotional state. For example, if the input text has a positive tone, it will be analyzed as having a positive emotion.

[0799] Based on this organized and processed data and the results of emotion analysis, the generative AI generates images and charts. If the user's emotional state is positive, the generative AI will generate charts with bright colors and positive messages.

[0800] The server then assembles the final document based on the generated images, charts, and organized and processed data. For example, it compiles the data into a PDF annual report using Python's ReportLab library. The document's design and content are adapted to the user's emotional state.

[0801] Finally, the server sends the completed document to the terminal, which displays it to the user, who can then view the document and download it as needed.

[0802] Prompt Sentence Examples

[0803] An example of a prompt sentence to input to the generative AI model is as follows:

[0804] "Create an annual report based on your 2022 sales data, with positive messaging and brightly designed graphs."

[0805] In this way, the system is designed to enable users to quickly and easily create high-quality materials, while also providing optimal design and content according to the user's emotional state.

[0806] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0807] Step 1:

[0808] A user accesses the system using a terminal and inputs the outline of the document they want to create. For example, they might input, "Create an annual report based on sales data for fiscal year 2022." This input is sent to the server through the terminal's user interface.

[0809] Step 2:

[0810] The terminal sends the summary information entered by the user to the server, which receives the input data in text format.

[0811] Step 3:

[0812] The server analyzes the received summary information using Python's natural language processing library (nltk or spaCy) to extract keywords such as "2022," "sales," and "annual report," and identify the type and scope of the data. The input to this analysis process is the text data of the summary information, and the output is the extracted keywords and data range.

[0813] Step 4:

[0814] The server extracts the necessary data from a database (MySQL or PostgreSQL) or API based on the specified keywords and data range. For example, it extracts "sales data" for "fiscal year 2022." The input for this process is the specified keywords and data range, and the output is the corresponding sales data.

[0815] Step 5:

[0816] The server organizes and processes the extracted data using the Pandas library. Specifically, it filters, aggregates, and sorts the data by month. The input to this process is the extracted sales data, and the output is the organized and processed data.

[0817] Step 6:

[0818] The server's emotion engine analyzes the user's input text. It uses NLTK's VADER Sentiment Analysis tool to identify the user's emotional state. For example, if the input text has a positive tone, it outputs a positive emotion. The input of this process is the user's text data, and the output is the emotional state.

[0819] Step 7:

[0820] The server's generative AI (GPT-3 or DALL-E) generates images and charts based on the organized and processed data and the results of sentiment analysis. Based on the emotional state, the generative AI selects designs such as brightly colored charts and positive messages. The inputs to this process are the organized and processed data and the emotional state, and the output is images and charts.

[0821] Step 8:

[0822] The server assembles documents using the generated images and charts, along with the organized and processed data. For example, it creates a PDF report using the Python ReportLab library. The inputs to this process are the images, charts, and organized and processed data, and the output is the final document (PDF report).

[0823] Step 9:

[0824] The server sends the completed document to the terminal. The input of this sending process is the completed document, and the output is the successful transfer of the document to the terminal.

[0825] Step 10:

[0826] The terminal displays the received material to the user, who can then view and download it as needed. The input to this process is the material sent from the server, and the output is the material displayed to the user.

[0827] Through these specific processing steps, the present invention provides a process for users to create efficient and high-quality materials, and realizes optimal document generation according to the needs and feelings of the user.

[0828] (Application example 2)

[0829] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0830] Conventional document creation systems require users to manually create the content and design of documents, which is time-consuming and labor-intensive and inefficient. Furthermore, dynamic document generation that takes into account the user's emotional state is difficult, making it impossible to provide optimal documents for individual users. The present invention aims to solve these problems by providing a system that allows users to easily create high-quality documents and provides optimal documents that correspond to the user's emotional state.

[0831] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input summary information; means for transmitting the summary information to the server; means for the server to analyze the summary information and extract necessary data; means for the server to organize and process the extracted data; means for a generation AI to generate images and charts based on the data; means for the server to assemble materials based on the organized and processed data and the generated images and charts; means for transmitting the final materials to the terminal; means for the terminal to display the received materials to the user; means for analyzing the user's emotional state using emotion analysis means; and means for the generation AI to adapt the content and design of the materials based on the user's emotional state. This makes it possible to easily and automatically generate dynamic, high-quality materials that take the user's emotional state into consideration.

[0832] "Users" are people or organizations that use this system and create materials.

[0833] "Summary information" refers to basic data and instructions regarding the content and theme of the material the user wants to create.

[0834] The "server" is a computer system that performs centralized calculations and management to analyze the input summary information, extract, organize, and process the necessary data, and generate the final materials.

[0835] "Data Extraction Method" refers to the technology or process that retrieves the data required from the Summary Information from the API or database.

[0836] "Means of organizing and processing" refers to the techniques or processes that filter, aggregate, sort, or otherwise format the extracted data for a specific purpose.

[0837] "Generative AI" is an artificial intelligence technology that generates images and charts based on organized and processed data, and determines the content and design of materials.

[0838] "Emotion analysis means" is a technology that analyzes the user's emotional state from voice or text input, and reflects the analysis results in the system.

[0839] "Means for assembling materials" refers to the technology or process for compiling the final materials into a single document format based on the images and diagrams created by the generative AI.

[0840] "Terminal" refers to a computer, smartphone, or other digital device used by a user.

[0841] "Transmission means" refers to the technology or process for transmitting the final material from the server to the terminal.

[0842] "Display means" refers to the technique or process by which the final material is displayed to the user on the terminal.

[0843] This invention is a system that generates high-quality driving analysis reports so that users can easily understand their daily driving conditions in autonomous vehicles. This system collects sensor data and driving situation data from autonomous vehicles in real time, analyzes them on a server, and with the cooperation of a generation AI model, provides users with an optimized driving report. Furthermore, by using emotion analysis means, the system adjusts the design and content according to the user's emotional state.

[0844] The server first receives and analyzes the summary information entered by the user. It then extracts the necessary data from an API or database, and organizes and processes the extracted data by filtering, aggregating, and sorting it chronologically. Based on this organized and processed data, the generative AI generates images and charts. Based on the generated images, charts, and text data, the server assembles the final document in PDF or HTML format.

[0845] In particular, for owners of autonomous vehicles, data is collected from various sensors inside the vehicle (speed sensor, GPS sensor, lidar sensor), and by analyzing the driver's emotional state in real time using a camera, the content and design of the generated driving report are adapted to the user's emotional state.

[0846] As a specific use case, consider the case where a user launches the application and inputs, "I would like to create a driving safety report today." Data such as speed data, location data, driving patterns, and the driver's emotional state are collected from the autonomous vehicle. Based on this data, the server creates a driving safety report by sending the following prompt sentence to the generation AI.

[0847] Example prompt sentence:

[0848] I want to write a driving safety report today.

[0849] Data collected:

[0850] Speed ​​data: average speed 60 km / h, maximum speed 100 km / h

[0851] Location data: urban, suburban

[0852] Driving pattern: sudden acceleration 3 times, sudden braking 2 times

[0853] Driver's emotional state: Slightly stressed facial expression

[0854] Based on this, prepare a driving safety report and summarize it in language that is easy for the driver to understand.

[0855] The server analyzes this data and generates a driving report based on a generative AI model (e.g., OpenAI's GPT-4). The report is then sent to the user's smartphone or car's instrument panel display and displayed to the user, who can then receive specific advice on how to improve their driving safety.

[0856] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0857] Step 1:

[0858] The user inputs summary information. Using a terminal in the autonomous vehicle or a smartphone, the user inputs instructions such as, "I would like to create a driving safety report today." This input becomes the initial data for the system.

[0859] Step 2:

[0860] The terminal sends the input summary information to the server. When the user inputs the summary information, the terminal sends this information to the server. At this time, the input summary information is "I would like to create a driving safety report."

[0861] Step 3:

[0862] The server analyzes the summary information and extracts the necessary data. The server then analyzes the received summary information using natural language processing technology and extracts the necessary data categories (speed data, location data, driving patterns, etc.). The output is a categorization of the necessary data for the summary information input.

[0863] Step 4:

[0864] The server collects various sensor data from the vehicle. Based on the extracted data type, the server collects data from various sensors in the vehicle (speed sensor, GPS sensor, lidar sensor, etc.). The input is a categorized data request, and the output is specific sensor data.

[0865] Step 5:

[0866] The server analyzes the user's emotional state using emotion analysis means. The driver's facial expressions are captured using a camera in the vehicle and analyzed using the Emotion API. The input is the captured image data, and the output is the driver's emotional state.

[0867] Step 6:

[0868] The server organizes and processes the collected sensor data and emotion data. It uses a data analysis library (e.g., Python's pandas or numpy) to filter, aggregate, and sort the data. The input is the collected sensor data and emotion data, and the output is the organized and processed data.

[0869] Step 7:

[0870] The generation AI generates prompt sentences based on the organized and processed data and generates the contents of the driving report.Using the generation AI (e.g., OpenAI GPT-4), the following prompt sentences are prepared and the contents of the driving report are generated.

[0871] Example prompt:

[0872] I want to write a driving safety report today.

[0873] Data collected:

[0874] Speed ​​data: average speed 60 km / h, maximum speed 100 km / h

[0875] Location data: urban, suburban

[0876] Driving pattern: sudden acceleration 3 times, sudden braking 2 times

[0877] Driver's emotional state: Slightly stressed facial expression

[0878] Based on this, prepare a driving safety report and summarize it in language that is easy for the driver to understand.

[0879] The input is the organized and processed data and prompt statements, and the output is the content of the generated driving report.

[0880] Step 8:

[0881] The server assembles a driving report based on the generated images, charts, and text data. It integrates the images, charts, and text information obtained from the generation AI into PDF or HTML format. The input is the content of the generated driving report, and the output is the final driving report.

[0882] Step 9:

[0883] The server sends the final driving report to the terminal. The completed driving report (PDF or HTML format) is sent to the user's terminal. The input is the driving report, and the output is the completion of sending it to the user's terminal.

[0884] Step 10:

[0885] The terminal displays the received driving report to the user. The terminal displays the received driving report on the display, which the user can view. The input is the received driving report, and the output is the user's visual confirmation.

[0886] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0887] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0888] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0889] [Fourth embodiment]

[0890] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0891] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0892] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0893] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0894] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0895] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0896] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0897] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0898] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0899] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0900] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0901] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0902] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0903] This invention is a generative AI integrated system that allows users to easily create high-quality documents. In this system, the server automatically extracts, organizes, and processes data based on the summary information entered by the user, and the generative AI generates images and charts, which are then output as documents.

[0904] The user accesses the system from a terminal and inputs the outline of the document they want to create. For example, they might input an instruction such as "Create a report based on sales data for fiscal year 2022." This outline is then sent to the server.

[0905] The server analyzes the received summary information and identifies the data the system needs. Based on this analysis, the server accesses an API or database to extract the required data. For example, if sales data is in a database, the server retrieves the relevant data using a query.

[0906] The acquired data is organized and processed on the server. In this process, the data is filtered and converted into an appropriate format. For example, monthly sales data is aggregated and sorted chronologically. In this way, the data is organized into a form that is easy to understand visually.

[0907] Next, generative AI works to generate images and charts based on the organized and processed data. For example, it can generate line graphs showing sales trends or infographics showing market trends. The generated images and charts are then incorporated as an integral part of the document.

[0908] The server then combines the final organized and processed data with the generated images and charts to create a document, such as a PDF report, so that the necessary information can be understood at a glance. This document is then sent to the device.

[0909] The terminal displays the materials sent from the server to the user. The user can view the materials on the terminal and download them as needed. This reduces the time and effort required for users to create materials, and allows users to obtain high-quality materials efficiently.

[0910] As a concrete example, consider the case where a company's sales department is creating an annual report. The salesperson (user) inputs summary information into a terminal to instruct the creation of the report. Based on the received information, the server extracts the necessary data from the sales database and generates a graph showing monthly sales trends. Finally, this information is integrated to create an annual report in PDF format, which is provided to the salesperson via the terminal.

[0911] In this way, the system of the present invention automates consistent data processing and visual document generation, thereby providing a highly efficient document creation environment for users.

[0912] The processing flow will be explained below.

[0913] Step 1:

[0914] The user inputs the outline information of the document from the terminal. For example, the user inputs an instruction such as "Create a report based on sales data for fiscal year 2022."

[0915] Step 2:

[0916] The terminal transmits the input summary information to the server.

[0917] Step 3:

[0918] The server analyzes the received summary information, which includes using natural language processing techniques to extract keywords and identify the type and scope of required data.

[0919] Step 4:

[0920] The server extracts the necessary data from an API or database based on the analysis results. For example, it references a database that stores sales data and executes the query "SELECT FROM sales WHERE year = 2022".

[0921] Step 5:

[0922] The server organizes and processes the extracted data. During this process, the data is filtered to extract data for specific months. It also calculates the total sales for each month and sorts them chronologically.

[0923] Step 6:

[0924] Generative AI generates images and charts based on the data organized and processed on the server. For example, it uses a graph creation tool to generate a line graph showing the increase or decrease in sales.

[0925] Step 7:

[0926] The server assembles the final document based on the organized and processed data and the generated images and charts, for example, by formatting it as a PDF report so that important information can be understood at a glance.

[0927] Step 8:

[0928] The server transmits the final generated material to the terminal.

[0929] Step 9:

[0930] The terminal displays the received materials to the user, who can then view the materials through the terminal and download them as needed.

[0931] In this way, the system automatically generates high-quality materials based on the summary information entered by the user through each step.

[0932] Example 1

[0933] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0934] Traditional document creation processes are often performed manually, resulting in the time-consuming and labor-intensive process of collecting, formatting, analyzing, and creating the final document. This manual process also increases the risk of human error, potentially resulting in low-quality documents. Furthermore, creating visually easy-to-understand documents requires advanced expertise. There is a need for a system that can solve these problems and automatically create high-quality documents.

[0935] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0936] In this invention, the server includes means for analyzing summary information and identifying necessary data, means for extracting the identified data from the database, and means for organizing and processing the extracted data, which makes it possible to automatically obtain and format data based on the summary information entered by the user, and generate visually easy-to-understand materials.

[0937] A "user" is a person or entity that utilizes the system to input instructions and summary information for creating materials.

[0938] "Terminal" means an electronic device used by a user to access the system, enter summary information and display received materials.

[0939] A "server" is a central computer that processes data for the entire system, extracts, organizes, processes, and generates documents based on instructions from users.

[0940] "Summary information" is data that indicates the basic requirements and instructions for creating a document entered by the user.

[0941] A "natural language processing library" is a software tool or library that the server uses to parse the summary information entered by the user.

[0942] A "database" is a digital storage system in which tangible data is stored.

[0943] A "generative AI model" is an artificial intelligence algorithm that runs on a server and generates images and charts based on organized and processed data.

[0944] "Material" is a collection of data created by the server using a generative AI model, and is the final document provided to the user.

[0945] "Images and charts" are visual content created by generative AI models based on organized and processed data.

[0946] This invention is a generative AI integrated system that allows users to easily create high-quality documents. Based on the summary information entered by the user, the server automatically extracts, organizes, and processes data, and the generative AI generates images and charts, which are then output as documents.

[0947] The user accesses the system from a terminal and inputs summary information for the document they want to create. For example, they might input an instruction such as "Create a report based on sales data for fiscal year 2022" into the terminal. This summary information is sent from the terminal to the server. In this case, the terminal can be thought of as inputting a form using a web browser.

[0948] The server analyzes the received summary information to identify the data the system requires. This analysis is performed using a natural language processing (NLP) library (e.g., spaCy or NLTK). Based on the results of this analysis, the server accesses an API or database to extract the required data. For example, if sales data is stored in a database, the server retrieves the data using an SQL query such as "SELECT FROM sales WHERE year=2022."

[0949] The acquired data is organized and processed on the server. In this process, the data is filtered using a data analysis library (e.g., pandas) and converted into an appropriate format. For example, monthly sales data is aggregated and sorted in chronological order.

[0950] Next, a generative AI model is put into action to generate images and charts based on the organized and processed data. Examples of generative AI models that are used include OpenAI's GPT-3 and DALLE-2. This generates line graphs showing sales trends and infographics showing market trends.

[0951] The server then combines the final organized and processed data with the generated images and charts to create a document. This document is often generated in PDF format and is formatted using a PDF generation library (e.g., ReportLab). This visually organizes the necessary information so that it can be understood at a glance.

[0952] The completed materials are sent from the server to the terminal, which displays them to the user, who can then view them on the terminal and download them as needed.

[0953] As a concrete example, consider the case where a company's sales department is creating an annual report. In this case, the sales department (user) inputs summary information into a terminal to instruct the creation of the report. For example, the user inputs a prompt such as, "Please create a sales data analysis report for fiscal year 2022." The server receives this, extracts the necessary data from the sales database, generates a graph showing monthly sales trends, and finally creates an annual report in PDF format and provides it to the sales department via the terminal.

[0954] In this way, the system of the present invention automates consistent data processing and visual document generation, thereby providing a highly efficient document creation environment for users.

[0955] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0956] Step 1:

[0957] The user accesses the system from a terminal and inputs summary information for the document they want to create. For example, they input instructions such as "Create a report based on sales data for fiscal year 2022." The input here is done by the user entering summary information into a form using a web browser. The input summary information is then used as input for the next step.

[0958] Step 2:

[0959] The terminal sends the summary information entered by the user to the server. This process is performed through an HTTP request (POST method). The input of the terminal is the summary information, and the output is the HTTP request received by the server.

[0960] Step 3:

[0961] The server analyzes the received summary information. This analysis is performed using a natural language processing library (e.g., spaCy or NLTK). Specifically, it extracts keywords and phrases from the summary information to identify the required data and clarify the type and range of data. The input is the summary information received via the HTTP request, and the output is the type and range of the identified data.

[0962] Step 4:

[0963] The server extracts the specified data from the database. It accesses the database (e.g. MySQL or PostgreSQL) using an SQL query (e.g. "SELECT FROM sales WHERE year=2022") to retrieve the relevant data. The input is the type and range of the specified data, and the output is the retrieved data.

[0964] Step 5:

[0965] The server organizes and processes the extracted data. This process uses a data analysis library (e.g., pandas) to filter the data and convert it into an appropriate format. For example, this may involve aggregating monthly sales data and sorting it in chronological order. The input is the data extracted from the database, and the output is the organized and processed data.

[0966] Step 6:

[0967] A generative AI model generates images and charts based on the organized and processed data. In this step, a generative AI model (e.g., OpenAI's GPT-3 or DALLE-2) is used to generate graphs and infographics. For example, a line graph showing sales increases or decreases or an infographic showing market trends may be generated. The input is the organized and processed data, and the output is the generated images and charts.

[0968] Step 7:

[0969] The server combines the organized and processed data with the generated images and charts to create documents. In this example, a PDF generation library (e.g., ReportLab) is used to generate documents in PDF format. For example, an annual report containing sales trend graphs and text is created. The input is the organized and processed data and the generated images and charts, and the output is a document in PDF format.

[0970] Step 8:

[0971] The server sends the completed document to the terminal. This process is performed using an HTTP response. The server's input is the completed PDF document, and the output is the HTTP response to the terminal.

[0972] Step 9:

[0973] The terminal displays the received PDF document to the user. The user can view the document on the terminal and download it as needed. The input is the PDF document sent from the server, and the output is the display and download to the user.

[0974] (Application example 1)

[0975] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0976] Data processing and report creation in conventional logistics centers relied heavily on manual work, which was time-consuming and labor-intensive, and also had accuracy issues. Furthermore, data visualization was insufficient, preventing managers from providing the information they needed to make quick decisions. For this reason, there is a demand for a system that automates efficient, high-quality data processing and document creation.

[0977] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0978] In this invention, the server includes: means for a user to input summary information; means for transmitting the summary information to the server; means for the server to analyze the summary information and extract necessary data; means for the server to organize and process the extracted data; means for a generation AI to generate images and charts based on the data; means for the server to assemble materials based on the organized and processed data and the generated images and charts; means for transmitting the final materials to a terminal; means for the terminal to display the materials received to the user; means for inputting prompts to acquire necessary data and generate visual charts; means for outputting business-related information such as shipment quantities and inventory levels as graphs based on the acquired data; and means for integrating the acquired data and the generated graphs to generate materials in report format. This automates data processing and document creation in logistics centers, enabling efficient and high-quality report creation.

[0979] "User" refers to a person who uses this system to input summary information and give instructions for creating materials.

[0980] "Summary information" refers to information that includes basic information and instructions for creating materials.

[0981] "Server" refers to the computer that analyzes the summary information sent by the user, extracts, organizes, and processes the necessary data, and generates the final document.

[0982] "Generative AI" refers to artificial intelligence technology that automatically generates images and charts based on extracted, organized, and processed data.

[0983] "Prompt" refers to text that describes instructions the user gives the system to obtain the required data and generate a visual chart.

[0984] "Materials" refers to report-style documents that integrate organized and processed data and generated images and charts.

[0985] "Graph" refers to graphs and charts generated to visually display numerical data or statistical information.

[0986] "Graph" refers to a visual representation, such as a line graph or bar graph, that is generated to visually show numerical data.

[0987] "Terminal" refers to a computer or smart device used by a user to input instructions for creating materials and to view the created materials.

[0988] A "logistics center" refers to a facility that stores, manages, and ships goods.

[0989] This invention is a system for streamlining data processing and document creation in a logistics center. The system is composed of the following main components:

[0990] 1. User operations

[0991] The administrator (user) of the logistics center accesses the system using a smartphone or computer terminal. The user enters summary information for document creation into the input field on the terminal. For example, the user enters a prompt statement such as "Create a shipping data and inventory report for March."

[0992] 2. Server Operation

[0993] The server receives the summary information sent by the user. The server analyzes the summary information and accesses an API or database to extract the necessary data. This includes data on shipment numbers and inventory levels from the logistics management system database. The server then organizes and processes the retrieved data. Specifically, it calculates monthly totals for shipment numbers and average inventory levels. The Python library "Pandas" is used to filter and aggregate the data.

[0994] 3. Image and chart generation

[0995] Based on the organized and processed data, the generative AI generates images and charts. Specifically, it uses the Python "Matplotlib" library to create line graphs and bar graphs. This generative AI operates according to the prompts entered by the user, providing visual information.

[0996] 4. Creating and outputting the final materials

[0997] The server integrates the organized data with the generated images and charts to generate the final report format. This document is output in PDF format or other formats. The Python "FPDF" library is used to generate the report. The server then sends the generated report to the user's terminal. The terminal displays the received report to the user, who can then download or print it as needed.

[0998] To give a concrete example, a user might enter the following prompt sentence:

[0999] "Please create a shipping data and inventory report for March. Specifically, please include graphs that visually show the progress of shipments and average inventory."

[1000] This system automates data processing and document creation at logistics centers, enabling efficient, high-quality reports to be created quickly.

[1001] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1002] Step 1:

[1003] A user accesses the system using a terminal and inputs the outline information for creating documents as a prompt statement. For example, the user inputs the instruction "Create shipping data and inventory report for March." This input information is sent to the server through the terminal interface.

[1004] Step 2:

[1005] The server receives the summary information sent by the user. It analyzes the received information and performs analysis processing to identify the required data. Specifically, it identifies the type and range of data, including shipping data and inventory information, based on the prompt text, and prepares to access the database or API.

[1006] Step 3:

[1007] The server accesses the API or database to extract the required data identified by querying the logistics management system database to retrieve data related to shipments and inventory levels for March. The inputs are the parsed prompt results and the database query, and the output is the retrieved raw data.

[1008] Step 4:

[1009] The server cleans and processes the extracted data. Specifically, it uses the Python library Pandas to filter the data and format it as needed. This step aggregates shipments by month and averages inventory levels. The input is the extracted raw data, and the output is the formatted data.

[1010] Step 5:

[1011] The generative AI generates images and charts based on the organized data. It uses Python's Matplotlib library to create line and bar graphs. Specifically, it plots the aggregated shipment data on a line graph and the average inventory level data on a bar graph. The input is the formatted data, and the output is the generated image file.

[1012] Step 6:

[1013] The server integrates the organized data with the generated images and charts to generate the final report format document, which is output in PDF format using the Python "FPDF" library. The input is the formatted data and generated image files, and the output is a report format PDF file.

[1014] Step 7:

[1015] The server sends the generated PDF report to the user's terminal. The user can use the terminal to view the received report and download or print it as needed. The input is the generated PDF file and the output is the report displayed on the terminal.

[1016] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1017] This invention is a generative AI integrated system that allows users to easily create high-quality documents, and also combines it with an emotion engine that recognizes the user's emotions. In this system, the server automatically extracts, organizes, and processes data based on the summary information entered by the user, and the generative AI generates images and charts. The emotion engine also recognizes the user's emotional state and adapts the content and design of the document accordingly.

[1018] Users access the system from their terminals and input outline information for the materials they wish to create. This outline information is then sent from the terminal to the server.

[1019] The server analyzes the received summary information, which includes using natural language processing techniques to extract keywords and identify the type and scope of required data.

[1020] The server then accesses an API or database to extract the necessary data, which is then organized and processed within the server. This process involves filtering, aggregating, and sorting the data chronologically.

[1021] The emotion engine works here and analyzes the user's voice or text input to recognize their emotional state. For example, if the user's input has a positive tone, the emotion engine will analyze it as a positive emotion.

[1022] The generative AI generates images and charts based on the organized and processed data, taking into account the analysis results of the emotion engine. For example, if the user's emotion is positive, it generates a chart with a bright color scheme and a positive message.

[1023] The server then assembles the document based on the processed data and the generated images and charts. For example, it formats it as a PDF report, applying content and design based on the analysis results of the emotion engine. This document is then sent to the device.

[1024] The terminal displays the materials sent from the server to the user, who can then view the materials through the terminal and download them as needed.

[1025] As a concrete example, consider the case where a company's sales department is creating an annual report. The salesperson (user) inputs the instruction "Create a report based on sales data for fiscal year 2022" into their device. The server analyzes this information, extracts the necessary data from the sales database, and organizes and processes it. Furthermore, if the emotion engine analyzes the tone of the text entered by the salesperson as positive, the generative AI generates a positive message and a brightly designed graph. Finally, the annual report is completed in PDF format and provided to the salesperson via their device.

[1026] In this way, the system of the present invention not only automates consistent data processing and visual document generation, but also provides optimal content and design according to the user's emotional state, thereby providing a more advanced and efficient document creation environment for users.

[1027] The processing flow will be explained below.

[1028] Step 1:

[1029] The user inputs the outline of the document from the terminal. For example, the user inputs instructions such as "Create a report with a positive tone based on sales data for fiscal year 2022."

[1030] Step 2:

[1031] The terminal transmits the input summary information to the server.

[1032] Step 3:

[1033] The server analyzes the summary information it receives. This analysis involves using natural language processing technology to extract keywords and identify the type and scope of data required. For example, it extracts keywords such as "sales data" and "2022."

[1034] Step 4:

[1035] The server extracts the necessary data from the API or database based on the analysis results. For example, it executes the query "SELECT FROM sales WHERE year = 2022" against the sales database to retrieve sales data for 2022.

[1036] Step 5:

[1037] The server organizes and processes the extracted data. This process involves filtering the data, aggregating monthly sales, and sorting them by time. For example, 12 months of sales data can be totaled by month and sorted in chronological order.

[1038] Step 6:

[1039] The device acquires the user's emotions and sends the data to the server. For example, if the user voice-types "Please write the report in a positive tone," this voice data is sent to the server.

[1040] Step 7:

[1041] The server's emotion engine analyzes the user's emotion data. For example, it recognizes the emotion as positive from the voice data and records the analysis result.

[1042] Step 8:

[1043] The generative AI generates images and charts based on the analysis results of the emotion engine, using data organized and processed on the server. For example, it generates line graphs with a positive design and infographics with a bright tone.

[1044] Step 9:

[1045] The server assembles the final document based on the organized and processed data and the generated images and charts, for example, creating a PDF report with a positive message and a bright design.

[1046] Step 10:

[1047] The server transmits the final generated material to the terminal.

[1048] Step 11:

[1049] The terminal displays the received documents to the user. The user can view the documents through the terminal, check their contents, and download them as needed. For example, a sales department employee displays the generated annual report on the terminal and checks its contents.

[1050] In this way, the system of the present invention automatically generates optimal materials based on the summary information and emotion data entered by the user through a series of steps, allowing users to not only efficiently create high-quality materials, but also receive materials with content and design appropriate to their emotions.

[1051] Example 2

[1052] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1053] In today's world, it is important for users to create high-quality documents quickly. However, with conventional systems, it has been difficult to consistently automate the process of extracting, organizing, and processing data, as well as generating visual graphics. Furthermore, there has been no system that can provide optimal design and content based on the user's emotional state. As a result, users have had to spend a lot of time and effort creating documents.

[1054] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input summary information; a means for transmitting the summary information to the server; a means for the server to analyze the summary information and extract necessary data; a means for the server to organize and process the extracted data; a means for an emotion engine to analyze the user's emotional state; a means for a generation AI to generate images and charts based on the data and the emotion analysis results; a means for the server to assemble materials based on the organized and processed data and the generated images and charts; a means for transmitting the final materials to a terminal; and a means for the terminal to display the received materials to the user. This enables users to quickly create high-quality materials through a consistent, automated process. In addition, user satisfaction can be improved by providing materials with optimal design and content based on the user's emotional state.

[1055] A "user" is an entity that accesses the system and inputs summary information for the creation of materials.

[1056] A "terminal" is a hardware device or software environment that is operated by a user and that communicates with a server.

[1057] The "server" is a central processing unit that analyzes the summary information received from the user, extracts the necessary data, organizes and processes it, generates images and charts using AI, and assembles materials.

[1058] "Summary information" refers to the original information that the user inputs to create a document, and includes specific data and instructions.

[1059] "Analysis" is the process of identifying keywords and the type and range of required data from the summary information.

[1060] "Data" refers to information elements such as numbers, text, and images required to create the materials desired by the user.

[1061] "Extraction" is the process of retrieving data identified through analysis from external resources such as APIs or databases.

[1062] "Organization" is the process of organizing the extracted data by filtering, aggregating, sorting, etc.

[1063] "Processing" is the process of converting organized data into a form that is easier to handle visually or logically.

[1064] An "emotion engine" is a combination of software or hardware that analyzes a user's input and behavior to determine their current emotional state.

[1065] "Generative AI" is an artificial intelligence model that automatically generates images and charts contained in documents based on organized and processed data and the results of sentiment analysis.

[1066] "Images and diagrams" are graphic elements created by generative AI to represent visual information.

[1067] A "material" is a formatted collection of information such as a report, presentation, or document that is ultimately provided to a user.

[1068] "Transmission" is the communication act in which the server transfers the final material to the terminal.

[1069] "Display" refers to the process in which the terminal visually presents the received materials to the user.

[1070] MODE FOR CARRYING OUT THE INVENTION

[1071] This invention is a generative AI integrated system that allows users to easily create high-quality materials, and also combines it with an emotion engine that recognizes the user's emotions. This system has the following hardware and software configuration.

[1072] Hardware Configuration

[1073] 1. Terminal: The device operated by the user (e.g., PC, tablet, smartphone).

[1074] 2. Server: A central processing unit that analyzes, extracts, and processes data for AI generation.

[1075] Software Configuration

[1076] 1. Natural language processing libraries: Python-based nltk and spaCy are used to analyze summary information.

[1077] 2. Database management system: Use MySQL or PostgreSQL to manage the necessary data.

[1078] 3. Pandas library: Used to filter, aggregate, and time-order the extracted data.

[1079] 4. Sentiment Analysis Tool: Analyze the user's emotional state using NLTK's VADER Sentiment Analysis.

[1080] 5. Generative AI model: Using OpenAI's GPT-3 and DALL-E, images and charts are generated based on user input data and sentiment analysis results.

[1081] 6. Report generation library: Use the Python ReportLab library to compile reports in PDF format.

[1082] Implementation method

[1083] The user accesses the system from a terminal and inputs the outline of the document they want to create. Specifically, they input the instruction to "create a report based on sales data for fiscal year 2022." The terminal then sends this outline to the server.

[1084] The server then analyzes the received summary information using a Python-based natural language processing library. During this analysis, keywords such as "2022 fiscal year," "sales," and "report" are extracted to identify the type and scope of data required.

[1085] The server then accesses an API or database to extract sales data, which it then uses the Pandas library to filter, aggregate, and sort (for example, to sum sales data by month for January through December of fiscal year 2022).

[1086] In parallel, the emotion engine analyzes the user's input text and voice data. It uses NLTK's VADER Sentiment Analysis tool to identify the user's emotional state. For example, if the input text has a positive tone, it will be analyzed as having a positive emotion.

[1087] Based on this organized and processed data and the results of emotion analysis, the generative AI generates images and charts. If the user's emotional state is positive, the generative AI will generate charts with bright colors and positive messages.

[1088] The server then assembles the final document based on the generated images, charts, and organized and processed data. For example, it compiles the data into a PDF annual report using Python's ReportLab library. The document's design and content are adapted to the user's emotional state.

[1089] Finally, the server sends the completed document to the terminal, which displays it to the user, who can then view the document and download it as needed.

[1090] Prompt Sentence Examples

[1091] An example of a prompt sentence to input to the generative AI model is as follows:

[1092] "Create an annual report based on your 2022 sales data, with positive messaging and brightly designed graphs."

[1093] In this way, the system is designed to enable users to quickly and easily create high-quality materials, while also providing optimal design and content according to the user's emotional state.

[1094] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1095] Step 1:

[1096] A user accesses the system using a terminal and inputs the outline of the document they want to create. For example, they might input, "Create an annual report based on sales data for fiscal year 2022." This input is sent to the server through the terminal's user interface.

[1097] Step 2:

[1098] The terminal sends the summary information entered by the user to the server, which receives the input data in text format.

[1099] Step 3:

[1100] The server analyzes the received summary information using Python's natural language processing library (nltk or spaCy) to extract keywords such as "2022," "sales," and "annual report," and identify the type and scope of the data. The input to this analysis process is the text data of the summary information, and the output is the extracted keywords and data range.

[1101] Step 4:

[1102] The server extracts the necessary data from a database (MySQL or PostgreSQL) or API based on the specified keywords and data range. For example, it extracts "sales data" for "fiscal year 2022." The input for this process is the specified keywords and data range, and the output is the corresponding sales data.

[1103] Step 5:

[1104] The server organizes and processes the extracted data using the Pandas library. Specifically, it filters, aggregates, and sorts the data by month. The input to this process is the extracted sales data, and the output is the organized and processed data.

[1105] Step 6:

[1106] The server's emotion engine analyzes the user's input text. It uses NLTK's VADER Sentiment Analysis tool to identify the user's emotional state. For example, if the input text has a positive tone, it outputs a positive emotion. The input of this process is the user's text data, and the output is the emotional state.

[1107] Step 7:

[1108] The server's generative AI (GPT-3 or DALL-E) generates images and charts based on the organized and processed data and the results of sentiment analysis. Based on the emotional state, the generative AI selects designs such as brightly colored charts and positive messages. The inputs to this process are the organized and processed data and the emotional state, and the output is images and charts.

[1109] Step 8:

[1110] The server assembles documents using the generated images and charts, along with the organized and processed data. For example, it creates a PDF report using the Python ReportLab library. The inputs to this process are the images, charts, and organized and processed data, and the output is the final document (PDF report).

[1111] Step 9:

[1112] The server sends the completed document to the terminal. The input of this sending process is the completed document, and the output is the successful transfer of the document to the terminal.

[1113] Step 10:

[1114] The terminal displays the received material to the user, who can then view and download it as needed. The input to this process is the material sent from the server, and the output is the material displayed to the user.

[1115] Through these specific processing steps, the present invention provides a process for users to create efficient and high-quality materials, and realizes optimal document generation according to the needs and feelings of the user.

[1116] (Application example 2)

[1117] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1118] Conventional document creation systems require users to manually create the content and design of documents, which is time-consuming and labor-intensive and inefficient. Furthermore, dynamic document generation that takes into account the user's emotional state is difficult, making it impossible to provide optimal documents for individual users. The present invention aims to solve these problems by providing a system that allows users to easily create high-quality documents and provides optimal documents that correspond to the user's emotional state.

[1119] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to input summary information; means for transmitting the summary information to the server; means for the server to analyze the summary information and extract necessary data; means for the server to organize and process the extracted data; means for a generation AI to generate images and charts based on the data; means for the server to assemble materials based on the organized and processed data and the generated images and charts; means for transmitting the final materials to the terminal; means for the terminal to display the received materials to the user; means for analyzing the user's emotional state using emotion analysis means; and means for the generation AI to adapt the content and design of the materials based on the user's emotional state. This makes it possible to easily and automatically generate dynamic, high-quality materials that take the user's emotional state into consideration.

[1120] "Users" are people or organizations that use this system and create materials.

[1121] "Summary information" refers to basic data and instructions regarding the content and theme of the material the user wants to create.

[1122] The "server" is a computer system that performs centralized calculations and management to analyze the input summary information, extract, organize, and process the necessary data, and generate the final materials.

[1123] "Data Extraction Method" refers to the technology or process that retrieves the data required from the Summary Information from the API or database.

[1124] "Means of organizing and processing" refers to the techniques or processes that filter, aggregate, sort, or otherwise format the extracted data for a specific purpose.

[1125] "Generative AI" is an artificial intelligence technology that generates images and charts based on organized and processed data, and determines the content and design of materials.

[1126] "Emotion analysis means" is a technology that analyzes the user's emotional state from voice or text input, and reflects the analysis results in the system.

[1127] "Means for assembling materials" refers to the technology or process for compiling the final materials into a single document format based on the images and diagrams created by the generative AI.

[1128] "Terminal" refers to a computer, smartphone, or other digital device used by a user.

[1129] "Transmission means" refers to the technology or process for transmitting the final material from the server to the terminal.

[1130] "Display means" refers to the technique or process by which the final material is displayed to the user on the terminal.

[1131] This invention is a system that generates high-quality driving analysis reports so that users can easily understand their daily driving conditions in autonomous vehicles. This system collects sensor data and driving situation data from autonomous vehicles in real time, analyzes them on a server, and with the cooperation of a generation AI model, provides users with an optimized driving report. Furthermore, by using emotion analysis means, the system adjusts the design and content according to the user's emotional state.

[1132] The server first receives and analyzes the summary information entered by the user. It then extracts the necessary data from an API or database, and organizes and processes the extracted data by filtering, aggregating, and sorting it chronologically. Based on this organized and processed data, the generative AI generates images and charts. Based on the generated images, charts, and text data, the server assembles the final document in PDF or HTML format.

[1133] In particular, for owners of autonomous vehicles, data is collected from various sensors inside the vehicle (speed sensor, GPS sensor, lidar sensor), and by analyzing the driver's emotional state in real time using a camera, the content and design of the generated driving report are adapted to the user's emotional state.

[1134] As a specific use case, consider the case where a user launches the application and inputs, "I would like to create a driving safety report today." Data such as speed data, location data, driving patterns, and the driver's emotional state are collected from the autonomous vehicle. Based on this data, the server creates a driving safety report by sending the following prompt sentence to the generation AI.

[1135] Example prompt sentence:

[1136] I want to write a driving safety report today.

[1137] Data collected:

[1138] Speed ​​data: average speed 60 km / h, maximum speed 100 km / h

[1139] Location data: urban, suburban

[1140] Driving pattern: sudden acceleration 3 times, sudden braking 2 times

[1141] Driver's emotional state: Slightly stressed facial expression

[1142] Based on this, prepare a driving safety report and summarize it in language that is easy for the driver to understand.

[1143] The server analyzes this data and generates a driving report based on a generative AI model (e.g., OpenAI's GPT-4). The report is then sent to the user's smartphone or car's instrument panel display and displayed to the user, who can then receive specific advice on how to improve their driving safety.

[1144] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1145] Step 1:

[1146] The user inputs summary information. Using a terminal in the autonomous vehicle or a smartphone, the user inputs instructions such as, "I would like to create a driving safety report today." This input becomes the initial data for the system.

[1147] Step 2:

[1148] The terminal sends the input summary information to the server. When the user inputs the summary information, the terminal sends this information to the server. At this time, the input summary information is "I would like to create a driving safety report."

[1149] Step 3:

[1150] The server analyzes the summary information and extracts the necessary data. The server then analyzes the received summary information using natural language processing technology and extracts the necessary data categories (speed data, location data, driving patterns, etc.). The output is a categorization of the necessary data for the summary information input.

[1151] Step 4:

[1152] The server collects various sensor data from the vehicle. Based on the extracted data type, the server collects data from various sensors in the vehicle (speed sensor, GPS sensor, lidar sensor, etc.). The input is a categorized data request, and the output is specific sensor data.

[1153] Step 5:

[1154] The server analyzes the user's emotional state using emotion analysis means. The driver's facial expressions are captured using a camera in the vehicle and analyzed using the Emotion API. The input is the captured image data, and the output is the driver's emotional state.

[1155] Step 6:

[1156] The server organizes and processes the collected sensor data and emotion data. It uses a data analysis library (e.g., Python's pandas or numpy) to filter, aggregate, and sort the data. The input is the collected sensor data and emotion data, and the output is the organized and processed data.

[1157] Step 7:

[1158] The generation AI generates prompt sentences based on the organized and processed data and generates the contents of the driving report.Using the generation AI (e.g., OpenAI GPT-4), the following prompt sentences are prepared and the contents of the driving report are generated.

[1159] Example prompt:

[1160] I want to write a driving safety report today.

[1161] Data collected:

[1162] Speed ​​data: average speed 60 km / h, maximum speed 100 km / h

[1163] Location data: urban, suburban

[1164] Driving pattern: sudden acceleration 3 times, sudden braking 2 times

[1165] Driver's emotional state: Slightly stressed facial expression

[1166] Based on this, prepare a driving safety report and summarize it in language that is easy for the driver to understand.

[1167] The input is the organized and processed data and prompt statements, and the output is the content of the generated driving report.

[1168] Step 8:

[1169] The server assembles a driving report based on the generated images, charts, and text data. It integrates the images, charts, and text information obtained from the generation AI into PDF or HTML format. The input is the content of the generated driving report, and the output is the final driving report.

[1170] Step 9:

[1171] The server sends the final driving report to the terminal. The completed driving report (PDF or HTML format) is sent to the user's terminal. The input is the driving report, and the output is the completion of sending it to the user's terminal.

[1172] Step 10:

[1173] The terminal displays the received driving report to the user. The terminal displays the received driving report on the display, which the user can view. The input is the received driving report, and the output is the user's visual confirmation.

[1174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1175] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1176] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1177] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1178] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1179] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1180] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1181] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1182] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1184] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1185] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1186] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1187] 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.

[1188] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1189] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1190] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1191] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1192] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1193] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1194] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1195] The following is further disclosed regarding the above embodiment.

[1196] (Claim 1)

[1197] a means for a user to input summary information;

[1198] means for transmitting summary information to a server;

[1199] A means for the server to analyze the summary information and extract necessary data;

[1200] A means for the server to organize and process the extracted data;

[1201] A means for generative AI to generate images and charts based on data,

[1202] The server organizes and processes the data and creates images and charts to assemble the materials.

[1203] means for transmitting the final material to the terminal;

[1204] means for displaying the received material to the user;

[1205] A system including:

[1206] (Claim 2)

[1207] 10. The system of claim 1, wherein the server comprises means for extracting data from an API or a database.

[1208] (Claim 3)

[1209] 2. The system of claim 1, wherein the generating AI comprises means for visualizing graphics based on data.

[1210] "Example 1"

[1211] (Claim 1)

[1212] a means for a user to input summary information;

[1213] means for the terminal to transmit summary information to a server;

[1214] a means for the server to analyze the summary information and identify necessary data;

[1215] means for the server to extract the identified data from the database;

[1216] A means for the server to organize and process the extracted data;

[1217] A means for the generative AI model to generate images and charts based on the organized and processed data;

[1218] The server organizes and processes the data and creates images and charts to assemble the materials.

[1219] A means for the server to transmit the final material to the terminal;

[1220] means for displaying the received material to the user;

[1221] A system including:

[1222] (Claim 2)

[1223] 10. The system of claim 1, wherein the server comprises means for using a natural language processing library for the analysis and for identifying the required data.

[1224] (Claim 3)

[1225] 10. The system of claim 1, wherein the generative AI model comprises means for generating graphics based on the data.

[1226] "Application Example 1"

[1227] (Claim 1)

[1228] a means for a user to input summary information;

[1229] means for transmitting summary information to a server;

[1230] A means for the server to analyze the summary information and extract necessary data;

[1231] A means for the server to organize and process the extracted data;

[1232] A means for generative AI to generate images and charts based on data,

[1233] The server organizes and processes the data and creates images and charts to assemble the materials.

[1234] means for transmitting the final material to the terminal;

[1235] means for displaying the received material to the user;

[1236] a means for inputting prompt statements to obtain the required data and generate a visual chart;

[1237] A means of outputting business-related information such as shipment numbers and inventory levels as graphs based on the acquired data;

[1238] means for integrating the acquired data and the generated graphs to generate a report-type document;

[1239] A system including:

[1240] (Claim 2)

[1241] 10. The system of claim 1, wherein the server comprises means for extracting data from an API or a database.

[1242] (Claim 3)

[1243] 2. The system of claim 1, wherein the generating AI comprises means for visualizing graphics based on data.

[1244] "Example 2: Combining Emotion Engines"

[1245] (Claim 1)

[1246] a means for a user to input summary information;

[1247] means for transmitting summary information to a server;

[1248] A means for the server to analyze the summary information and extract necessary data;

[1249] A means for the server to organize and process the extracted data;

[1250] a means for the emotion engine to analyze the user's emotional state;

[1251] A means for generative AI to generate images and charts based on data and sentiment analysis results,

[1252] The server organizes and processes the data and creates images and charts to assemble the materials.

[1253] means for transmitting the final material to the terminal;

[1254] means for displaying the received material to the user;

[1255] A system including:

[1256] (Claim 2)

[1257] 10. The system of claim 1, wherein the server comprises means for extracting data from an API or a database.

[1258] (Claim 3)

[1259] The system of claim 1, wherein the generative AI is provided with means for visualizing graphics based on data and optimizing the design taking into account the results of sentiment analysis.

[1260] "Application example 2 when combining emotion engines"

[1261] (Claim 1)

[1262] a means for a user to input summary information;

[1263] means for transmitting summary information to a server;

[1264] A means for the server to analyze the summary information and extract necessary data;

[1265] A means for the server to organize and process the extracted data;

[1266] A means for generative AI to generate images and charts based on data,

[1267] The server organizes and processes the data and creates images and charts to assemble the materials.

[1268] means for transmitting the final material to the terminal;

[1269] means for displaying the received material to the user;

[1270] means for analyzing the emotional state of a user using an emotion analysis means;

[1271] a means for the generative AI to adapt the content and design of the material based on the user's emotional state;

[1272] A system including:

[1273] (Claim 2)

[1274] 10. The system of claim 1, wherein the server comprises means for extracting data from an API or a database.

[1275] (Claim 3)

[1276] 2. The system of claim 1, wherein the generating AI comprises means for visualizing graphics based on data. [Explanation of symbols]

[1277] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input summary information; means for transmitting summary information to a server; A means for the server to analyze the summary information and extract necessary data; A means for the server to organize and process the extracted data; A means for generative AI to generate images and charts based on data, The server organizes and processes the data and generates images and charts to assemble materials. means for transmitting the final material to the terminal; means for displaying the received material to the user; A system including:

2. 10. The system of claim 1, wherein the server comprises means for extracting data from an API or a database.

3. 2. The system of claim 1, wherein the generating AI comprises means for visualizing graphics based on the data.

Images