system

Abstract

We provide the system. [Solution] A generative model means that receives information entered by a user and searches for data on the internet based on that information, A data processing means that formats the data collected by the generation model means and displays it in a format that can be visually presented to the user, A file generation means that converts the formatted data into a file format that can be downloaded externally, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional manual data search and input waste a large amount of time and labor, and there is a problem that the burden on the user increases due to repetitive simple operations. In addition, manual handling of information induces the occurrence of human errors, and there is a possibility that the reliability and accuracy of data are impaired. Furthermore, since the conversion work when expressing information in different formats is also complicated, there is also a problem that business efficiency decreases.

Means for Solving the Problems

[0005] This invention solves these problems by providing a system that automatically searches for data on the internet based on information input by the user and quickly and accurately obtains the necessary information using a generation model. Furthermore, a data processing means that formats the data and displays it to the user in a visually presentable format allows for easy confirmation and evaluation of the information. In addition, a file generation means enables the download of the formatted data in an external file format specified by the user, thereby improving work efficiency. It also includes a function that allows for highly reliable data selection, optimizing information selection and management.

[0006] A "user" is an entity that uses a system to input information and receives the results of that information processing.

[0007] "Information" refers to the labels and search queries for data entered by the user, and serves as the basis for the system to retrieve data.

[0008] A "generative model means" is an artificial intelligence-based process for automatically acquiring relevant data from the internet based on input information.

[0009] "Data processing means" refers to a function that formats acquired data into a visually understandable format for the user and displays it.

[0010] "File generation method" refers to the process of converting formatted data into an external file format specified by the user (e.g., Excel or CSV format) and making it available for saving or downloading.

[0011] "Reliability" refers to the criteria used to evaluate the accuracy and safety of the data being acquired, and to select more reliable information. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] To implement this invention, it is necessary to have a terminal equipped with a user interface and a server that performs data retrieval and processing. The user starts the system by entering an information label on the terminal and sends an information request to the server. The server automatically searches for relevant data on the internet using a generative model. Because this generative model utilizes natural language processing and machine learning, it can collect accurate data from diverse information sources.

[0034] Next, the server formats the collected data using data processing tools and reconstructs it in a visually recognizable format (e.g., tabular format) for the user. This formatted data is then sent to the terminal, allowing the user to review and evaluate the results. If the displayed data is found to contain errors or inappropriate information, the user may have the option to choose a different source for further information, taking reliability into consideration.

[0035] Furthermore, users are given the option to save the processed data as an external file. The device sends a download request to the server, which generates an external file in formats such as Excel or CSV using a file generation method, and sends it back to the device. The user can then download this external file and use it with any data management tool.

[0036] As a concrete example, if a user wants to create a table of "average temperatures by region," they input label information on their terminal and request a search from the server. The server uses a generative model to collect the latest average temperature data for each region from the internet, formats it, and sends the results to the terminal. The user can then download the formatted results and use them as business data. This method significantly reduces the time and effort required for users to acquire and format information.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The user uses their device to enter the necessary labels and keywords into the input forms displayed on the interface. This defines the type and structure of the information to be searched.

[0040] Step 2:

[0041] The terminal receives user input, converts it to an appropriate format (e.g., JSON), and then sends a request to the server. The request includes the label information entered by the user.

[0042] Step 3:

[0043] The server analyzes the request received from the terminal and activates the generative model. The generative model analyzes the input labels and keywords and searches for relevant data from databases and web APIs based on that analysis.

[0044] Step 4:

[0045] The generative model collects necessary information from reliable data sources on the internet and then formats that data on a server. Data formatting includes filtering out unnecessary information and converting it to the format requested by the user.

[0046] Step 5:

[0047] The server sends the formatted data to the terminal. The data may include links to the original source and metadata regarding the data's reliability. The terminal displays the received data on an interface that shows the information in a visually easy-to-understand format.

[0048] Step 6:

[0049] Users can view the results on screen and evaluate the data as needed. Furthermore, by selecting the download option, they can save the formatted data as an external file.

[0050] Step 7:

[0051] The terminal sends a download request to the server, and the server generates a file in the specified format (e.g., Excel or CSV) using a file generation mechanism. The generated file is sent to the terminal, and the user can download it and use it in their local environment.

[0052] (Example 1)

[0053] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0054] In today's world, where vast amounts of data exist on the internet, there is a need to efficiently and accurately acquire the information users desire and organize and present it in the required format. However, conventional systems require considerable effort and time for information collection, formatting, and transformation, and lack sufficient means to verify the accuracy and reliability of the information. To solve this problem, a system equipped with advanced data collection and processing capabilities is essential.

[0055] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0056] In this invention, the server includes a generation model means that receives information entered by a user via an information input terminal and searches for data based on that information; an information processing means that formats the information collected by the generation model means and presents it in a format that the user can visually confirm; and an information generation means that converts the formatted information into an information format that can be stored externally. This makes it possible for users to efficiently collect reliable information and use it in a flexible format.

[0057] A "terminal" is a device used by users to input information and to receive and verify that information.

[0058] "Information" refers to the data that a user specifies as the search target, or the labels associated with that data.

[0059] A "server" is a central management device that retrieves, formats, generates, and provides information.

[0060] "Generative model means" refers to technologies and algorithms for collecting data based on input information.

[0061] "Information processing means" refers to technology that has the function of formatting collected data and converting it into a visually easy-to-understand format.

[0062] "Information generation means" refers to technology for converting formatted information into a format that can be stored externally.

[0063] "Information sources" refer to various sources of data available on the internet.

[0064] "Evaluation" is the process of determining the reliability of a designated information source and using that judgment to select that information.

[0065] This invention relates to a system for the efficient collection, processing, and presentation of information. This system operates based on information input by a user and includes multiple means for performing information processing.

[0066] The user inputs information using a terminal. This terminal can consist of various computing devices such as personal computers, smartphones, and tablets. The terminal is responsible for collecting information through the user interface and transmitting that information to the server.

[0067] The server is the primary device for retrieving and formatting information. It utilizes a generative AI model to collect data from the internet based on the input information. This generative AI model implements natural language processing and machine learning algorithms, such as ChatGPT® and BERT. This allows the server to obtain highly accurate data from diverse information sources.

[0068] Furthermore, the server is equipped with information processing capabilities that format the acquired data and convert it into a user-friendly format. This conversion is intended to make data visualization more effective, and various formats such as tables and graphs can be selected.

[0069] Furthermore, the server is equipped with information generation capabilities, converting formatted information into a format that can be saved as an external file. This conversion allows for the generation of files in formats such as Excel and CSV, which can then be sent back to the terminal. Users can download these files and utilize them with their preferred data management tools.

[0070] For example, if a user wants to collect information such as "average temperature for each prefecture in Japan," they would input this information into their device and request a search from the server. The server would then use a generative AI model to collect the latest average temperatures for each prefecture from the internet, format the data, and send the results to the device. The user could then download the formatted results as a file and use them for business documents, analysis, and other purposes.

[0071] An example of a prompt to input into the generating AI model is, "Search for the average temperature for May 2023 in each prefecture of Japan and display it in a table format." This prompt allows the user to efficiently obtain the necessary information.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The user accesses the terminal's user interface and enters the necessary information. For example, they might enter label information such as "average temperature by prefecture in Japan." This input is then prepared as a data retrieval request to the server.

[0075] Step 2:

[0076] The terminal receives user input and sends a request to the server. The data sent includes labels and conditions specified by the user. When this request reaches the server, the information retrieval process begins.

[0077] Step 3:

[0078] The server uses a generative AI model based on the received request to search for relevant data from the internet. Following the input prompt, it extracts data from diverse sources using natural language processing algorithms. The output at this stage is the collected relevant data.

[0079] Step 4:

[0080] The server formats the data retrieved through the search using information processing tools. Specifically, it converts the retrieved data into a format that is easy for the user to understand. For example, it organizes numerical data and reconstructs it into a tabular format. The output of this step is formatted data.

[0081] Step 5:

[0082] The server sends the formatted data to the terminal. The terminal displays the received data directly in its user interface, allowing the user to verify the results. The output of this step is data in a format that the user can view on the terminal.

[0083] Step 6:

[0084] Users evaluate the displayed data and request additional information as needed. They can also request a change in the data source if the displayed data is inaccurate.

[0085] Step 7:

[0086] The user can choose to save the data as an external file as needed. The terminal sends a download request to the server, asking it to convert the data into Excel or CSV format using a file generation method.

[0087] Step 8:

[0088] The server uses information generation tools to convert the formatted data into the specified file format and sends it back to the terminal. The user can download this file and use it with a data management tool. The final output is an external file in the format specified by the user.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] Logistics centers require the rapid and accurate provision of information on inventory status and delivery schedules. This is necessary to enable efficient inventory management and flexible delivery planning. However, the time and effort required to acquire and format this information leads to a decrease in operational efficiency.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes: information generation means that receive information entered by a user and search for data on an information source based on that information; data processing means that format the data collected by the information generation means and display it to the user in a visually presentable format; file generation means that convert the formatted data into a file format that can be output externally; and logistics information management means that collect and dynamically display inventory and delivery information based on the information entered by the user. This enables rapid and accurate information acquisition and presentation, thereby improving the efficiency of logistics operations.

[0094] An "information generation means" is a device that has the function of searching for and obtaining relevant data from information sources based on information entered by the user.

[0095] "Data processing means" refers to methods that aim to format collected data and present it to the user in a visually clear and easy-to-understand format.

[0096] A "file generation method" is a method that converts formatted data into a file format that can be output externally, making the data easily usable by the user.

[0097] A "logistics information management system" is a system that collects, manages, and dynamically displays information such as inventory status and delivery schedules based on information entered by the user.

[0098] In implementing this invention, the server configures the system by making full use of information generation means, data processing means, file generation means, and logistics information management means. Based on the information input by the user from the terminal, the server quickly searches for relevant data from information sources on the internet and collects the data while considering the reliability of the information using a generative AI model. As the information generation means, a generative AI model using natural language processing technology is utilized.

[0099] The collected data is formatted through data processing and displayed on the device in a visually recognizable format. Here, the data formatting is performed using a user interface based on React Native, making the results easily viewable. If necessary, the formatted data is converted to common file formats such as Excel or CSV using a file generation tool and provided to the user. A Python programming environment is used for file generation.

[0100] The logistics information management system helps users instantly check inventory status and delivery information on devices such as smartphones. When a user enters the necessary information, the server dynamically collects the latest inventory data and delivery schedules and sends the formatted information to the device.

[0101] As a concrete example, when a user enters a label such as "Tokyo inventory status" into their terminal, the server collects warehouse information for the corresponding region and displays it on the terminal in a visually easy-to-understand format. Based on this information, the user can perform appropriate inventory management and create an efficient delivery plan.

[0102] An example of a prompt message could be entered as follows:

[0103] "Please collect the latest inventory information for products in warehouses in the Tokyo area, and highlight and list products with low stock levels."

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The user operates the terminal and enters information. Specifically, the entered information relates to inventory status and delivery schedules. Based on this input, a prompt message is generated and prepared to be sent to the server.

[0107] Step 2:

[0108] The server analyzes the prompt message received from the user. Based on the analyzed prompt message, it uses a generative AI model to search for relevant data from information sources on the internet. Specifically, it utilizes natural language processing algorithms to extract appropriate keywords and collect data related to them.

[0109] Step 3:

[0110] The server formats the collected data using data processing tools. Specifically, it converts the collected raw data into a visually recognizable format such as a tabular format. At this stage, Python is used to perform appropriate data filtering and styling.

[0111] Step 4:

[0112] The server sends the formatted data to the terminal. The sent data is displayed on the terminal's user interface. Here, React Native is used to display the data in a way that makes it easy for the user to check.

[0113] Step 5:

[0114] The user reviews the data displayed on their device and, if necessary, chooses to save it as an external file. If saving is selected, the server uses a file generation mechanism to convert the formatted data into Excel or CSV format and returns it to the device as a downloadable file.

[0115] Step 6:

[0116] Users download the generated files and use them as tools for logistics management and delivery planning. These files can be opened with appropriate data management tools, improving the user's work efficiency.

[0117] The above processing steps improve the efficiency of logistics information management, allowing users to obtain accurate information in real time.

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

[0119] This invention comprises a system including a generation model means for performing data retrieval on the internet based on user input information, a data processing means for formatting the data and presenting it visually to the user, and a file generation means for converting the formatted data into an external file format. Furthermore, it aims to improve the flexibility and responsiveness of the user interface by incorporating an emotion engine that recognizes the user's emotions.

[0120] The terminal sends the search label entered by the user to the server. The emotion engine installed on the server analyzes and infers the emotional state from the user's input actions and interactions. Based on the collected emotional information, the server provides preconditions and feedback, and then smoothly and efficiently initiates data retrieval using a generative model. The generative model constantly scans various reliable data sources on the internet and collects data that matches the request.

[0121] Next, the server formats the collected data using data processing tools. Based on the analysis results of the emotion engine, it adjusts the presentation method and style of the collected data to present information in a way that is optimal for the user's state. As a result, the user receives a data view that is appropriate to their state, enabling efficient decision-making.

[0122] Users select appropriate data by reviewing the data displayed on their device and evaluating its reliability and relevance. The emotion engine provides feedback that continuously optimizes data processing methods and generative models in real time, leading to a continuous improvement in the user experience. Furthermore, if needed, users are provided with the option to download formatted data, which they can save as an external file in their specified format.

[0123] As a concrete example, when a user is seeking information on "a company's annual revenue," they enter a label into the terminal. At this point, the emotion engine detects that the user is experiencing stress and reduces anxiety by presenting the information in a simple and easy-to-understand format. The collected data is quickly formatted and presented to the user in a calm manner. The user can comfortably review the data and, if necessary, perform detailed analysis using the download option. This format enables flexible information delivery that takes user emotions into consideration, improving work efficiency and reducing stress.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The user uses the on-device interface to enter the labels and keywords needed for data retrieval. Once the input is complete, clicking the "Search" button prepares the request to be sent to the server.

[0127] Step 2:

[0128] The terminal receives user input, formats it into JSON format, and sends it to the server. At this time, user interaction data is also sent, enabling analysis by the emotion engine.

[0129] Step 3:

[0130] The server analyzes the data received from the terminal and uses an emotion engine to estimate the user's emotional state. For example, it can determine stress or anxiety from input speed and touch patterns.

[0131] Step 4:

[0132] The server utilizes generative modeling to search for appropriate and reliable information from internet data sources based on user-specified labels. If necessary, it retrieves data from multiple sources with varying levels of reliability.

[0133] Step 5:

[0134] The data collected by the generative model is formatted by the data processing means. Based on the user's emotions analyzed by the emotion engine, the visual representation of the data is adjusted and transmitted to the terminal in the display format most suitable for the user.

[0135] Step 6:

[0136] The formatted data is displayed to the user on the device. The user reviews the visualized information and evaluates whether it meets their purpose. The user's reactions and further interactions are also sent back to the server as emotional feedback.

[0137] Step 7:

[0138] If a user wants to save data as an external file, they select the download option. The device sends this request to the server, which then uses a file generation tool to create a file in the specified format (e.g., Excel or CSV).

[0139] Step 8:

[0140] The server sends the generated files to the terminal, which the user downloads and uses for review and analysis in their local environment as needed. This entire process enables flexible and efficient information delivery that responds to emotional responses.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] Conventional information retrieval systems often present information without considering the user's emotional state, leading to situations where users find it difficult to understand. Furthermore, the reliability of the information provided is often a challenge, requiring users to verify it themselves, hindering efficient decision-making. Additionally, the limited functionality for adjusting format and style along with information provision makes providing flexible information to meet diverse user needs a significant challenge.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0145] In this invention, the server includes a generation model means that receives information input by the user and searches a database based on that information; a data processing means that organizes the data collected by the generation model means and displays it in a format that can be visually provided to the user; a file generation means that converts the organized data into a file format that can be transmitted externally; and an emotion analysis means that analyzes the user's emotional state and optimizes the operation of the generation model means and the data processing means based on the analysis results. This enables the presentation of optimal information that matches the user's emotional state, the provision of highly reliable data, and flexible adjustment of the information format.

[0146] The "generative model means" is a function that performs a process of searching for data on the internet based on user input information and collecting the necessary information.

[0147] "Data processing means" refers to a function that formats the data collected by the generative model means and displays it in a format that can be visually provided to the user.

[0148] A "file generation method" is a function that converts formatted data into a file format that can be transmitted externally.

[0149] The "emotion analysis means" is a function that analyzes the user's emotional state and optimizes the operation of the generative model means and data processing means based on the analysis results.

[0150] This invention is a system for searching data on the internet based on user input information and presenting the information in the most optimal format. The specific configuration and operation of this system are described below.

[0151] The server has a generative model that receives information entered by the user using a terminal and uses that information to search a database. This model is designed to quickly and accurately collect necessary information from reliable sources worldwide. Specifically, it uses a data processing engine and AI-based algorithms to evaluate the reliability of the collected information and select the most relevant data.

[0152] Next, the server utilizes data processing tools to format the collected data. These tools adjust the data's arrangement and visual display to provide information in a user-friendly and easy-to-understand format. Examples include graphical representations of data and highlighting of key metrics.

[0153] Furthermore, the server incorporates an emotion analysis system that analyzes the user's emotional state. This function allows the system to infer the user's psychological state based on their input and interactions, and optimize the operation of the generative model and data processing systems in real time. This enables flexible information presentation tailored to the user's emotions, improving the user experience.

[0154] For example, if a user searches for "annual revenue of a company" using their device, sentiment analysis can determine that the user is experiencing stress. Based on this information, the server simplifies the presentation of the information through data processing, making it easier to understand. The information is then provided in a format that is easy for the user to comprehend, and if necessary, it is also available in a file format that can be downloaded externally.

[0155] An example of a prompt would be, "Please tell me the latest global trends in corporate annual revenues." Such prompts allow the system to search for and present appropriate information.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The user enters a search label into the terminal. The entered label is a keyword that points to specific information based on the user's request. This information is sent as input to the server.

[0159] Step 2:

[0160] The terminal sends the search label entered by the user to the server. Once this label reaches the server, the server prepares to begin the information retrieval process.

[0161] Step 3:

[0162] The server uses sentiment analysis tools to analyze the user's input behavior and infer the user's emotional state. This analysis considers input patterns and interaction speed, and the results are used as preprocessing for data retrieval.

[0163] Step 4:

[0164] The server operates a generative model and searches databases on the internet to collect the necessary information. This process ensures the accuracy of the information by collecting data from reliable sources and verifying data reliability. The collected information becomes the server's output.

[0165] Step 5:

[0166] The server formats the collected data using data processing tools. This formatting process ensures the data is presented visually in an easily understandable way, based on the results of sentiment analysis. For example, graphs and highlighting may be used. The formatted data is then output from the server and provided to the user.

[0167] Step 6:

[0168] Users review the formatted data provided from their devices and evaluate its reliability and relevance. This evaluation is performed to support data selection and decision-making.

[0169] Step 7:

[0170] The server incorporates user feedback into a feedback loop between sentiment analysis and data processing tools to optimize information presentation. This process continuously improves the user experience.

[0171] Step 8:

[0172] Users can download the necessary data in an external file format. This feature allows them to retrieve formatted and saved data for later review and analysis.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0175] In today's information society, users are required to efficiently obtain the data they need from vast amounts of information. However, simply presenting information is not enough; flexible information presentation tailored to the user's emotions and state of mind is essential for improving the user experience. Current systems struggle to provide information while considering user emotions, leading to stress and dissatisfaction.

[0176] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0177] In this invention, the server includes a generation model means that receives information input by the user and searches for data on the internet based on that information; a data processing means that formats the collected data and displays it in a presentable format; a file generation means that converts the formatted data into a file format that can be downloaded externally; an emotion analysis means that analyzes the user's emotions; and a means that dynamically adjusts the display style of the information based on the emotion analysis results. This makes it possible to provide information in a form that is appropriate to the user's emotional state, thereby reducing stress and promoting efficient decision-making.

[0178] "User input" refers to the input that a user sends to a system via a terminal in order to obtain specific data or services.

[0179] A "generative model means" is a mechanism for searching for and collecting relevant data from the internet based on user input information.

[0180] "Data processing means" refers to methods for formatting collected data into a user-friendly format and presenting it visually.

[0181] A "file generation method" is a mechanism that converts formatted data into a format that can be stored externally, allowing users to download it.

[0182] "Emotional analysis methods" refer to processes for inferring and analyzing a user's emotional state based on their input and actions.

[0183] "Dynamic adjustment methods" refer to methods of changing the style and format of information presented in response to the results of user sentiment analysis.

[0184] To realize this invention, a system centered on server, terminal, and user interaction will be constructed. The server will play a central role in performing data retrieval, data formatting, sentiment analysis, and dynamic adjustment of display styles.

[0185] The server receives information entered by the user via the terminal and collects relevant data from various reliable sources on the internet. This process utilizes generative modeling technology. Generative models leverage AI technology to efficiently extract necessary information from large amounts of data. They also evaluate the reliability of specified sources and select the most appropriate data based on their reliability.

[0186] Next, the server formats the collected data and converts it into a user-friendly format. This formatting process utilizes data processing tools. Furthermore, sentiment analysis tools are used to analyze the user's emotional state and dynamically adjust the information display style to match the user's psychological state. This allows information to be presented to the user in the most optimal way, reducing stress and confusion.

[0187] For example, if a user requests detailed information about a specific product, the server quickly collects reviews and specifications for that product. If the server determines from the user's facial expressions and voice that they are experiencing stress, it displays the information simply and intuitively. For sentiment analysis, the Affectiva SDK and Google Cloud AI's Sentiment Analysis API can be used. Furthermore, Python's Beautiful Soup and Pandas libraries are useful for specific data collection and formatting.

[0188] This system not only supports efficient decision-making but also improves the user experience, solving the challenges of conventional technologies. An example of a prompt message is as follows: "Collect the latest product reviews related to the '{product name}' entered by the user, and if the user is feeling anxious, extract and present reassuring points from the reviews." Using this prompt enables information delivery tailored to the user's emotions, creating a user-friendly environment.

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The terminal receives user input information and sends it to the server. This input information includes data about specific product names or services. This allows the server to initiate the search process.

[0192] Step 2:

[0193] The server uses a generative AI model based on the received input information to search for data on the internet. The server uses AI algorithms to extract and collect relevant data from reliable sources. The collected data is in its raw, unprocessed state.

[0194] Step 3:

[0195] The server performs reliability assessments and selects information deemed highly reliable from the collected data. During this selection process, it utilizes the reliability score of the data source to filter the necessary data.

[0196] Step 4:

[0197] The server formats the selected data using data processing tools. Specifically, it uses the Python Pandas library to convert the data into a visually understandable format, such as tables or graphs. This formatted data then becomes the input data for the next process.

[0198] Step 5:

[0199] The emotion analysis system analyzes the user's emotional state from their input actions and interactions via the device. Using the Affectiva SDK, it collects and analyzes the user's emotional data and sends it to the server. This analysis result is used for adjustments in the next step.

[0200] Step 6:

[0201] The server adjusts the presentation style of formatted data based on sentiment analysis results. For example, if the user is experiencing stress, the information is simplified and visually reduced. This adjustment includes summarizing and highlighting data.

[0202] Step 7:

[0203] The server sends the processed data to the terminal, allowing the user to visually review it. The user can then make decisions based on this data and, if necessary, view more detailed information or download it in an external file format.

[0204] Through this process, the server utilizes generative AI models and the latest sentiment analysis technologies to deliver user-centric information.

[0205] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0206] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0207] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0211] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0212] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0213] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0214] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0215] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0216] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0217] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0218] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0219] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0220] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0221] To implement this invention, it is necessary to have a terminal equipped with a user interface and a server that performs data retrieval and processing. The user starts the system by entering an information label on the terminal and sends an information request to the server. The server automatically searches for relevant data on the internet using a generative model. Because this generative model utilizes natural language processing and machine learning, it can collect accurate data from diverse information sources.

[0222] Next, the server formats the collected data using data processing tools and reconstructs it in a visually recognizable format (e.g., tabular format) for the user. This formatted data is then sent to the terminal, allowing the user to review and evaluate the results. If the displayed data is found to contain errors or inappropriate information, the user may have the option to choose a different source for further information, taking reliability into consideration.

[0223] Furthermore, users are given the option to save the processed data as an external file. The device sends a download request to the server, which generates an external file in formats such as Excel or CSV using a file generation method, and sends it back to the device. The user can then download this external file and use it with any data management tool.

[0224] As a concrete example, if a user wants to create a table of "average temperatures by region," they input label information on their terminal and request a search from the server. The server uses a generative model to collect the latest average temperature data for each region from the internet, formats it, and sends the results to the terminal. The user can then download the formatted results and use them as business data. This method significantly reduces the time and effort required for users to acquire and format information.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The user uses their device to enter the necessary labels and keywords into the input forms displayed on the interface. This defines the type and structure of the information to be searched.

[0228] Step 2:

[0229] The terminal receives user input, converts it to an appropriate format (e.g., JSON), and then sends a request to the server. The request includes the label information entered by the user.

[0230] Step 3:

[0231] The server analyzes the request received from the terminal and activates the generative model. The generative model analyzes the input labels and keywords and searches for relevant data from databases and web APIs based on that analysis.

[0232] Step 4:

[0233] The generative model collects necessary information from reliable data sources on the internet and then formats that data on a server. Data formatting includes filtering out unnecessary information and converting it to the format requested by the user.

[0234] Step 5:

[0235] The server sends the formatted data to the terminal. The data may include links to the original source and metadata regarding the data's reliability. The terminal displays the received data on an interface that shows the information in a visually easy-to-understand format.

[0236] Step 6:

[0237] Users can view the results on screen and evaluate the data as needed. Furthermore, by selecting the download option, they can save the formatted data as an external file.

[0238] Step 7:

[0239] The terminal sends a download request to the server, and the server generates a file in the specified format (e.g., Excel or CSV) using a file generation mechanism. The generated file is sent to the terminal, and the user can download it and use it in their local environment.

[0240] (Example 1)

[0241] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0242] In today's world, where vast amounts of data exist on the internet, there is a need to efficiently and accurately acquire the information users desire and organize and present it in the required format. However, conventional systems require considerable effort and time for information collection, formatting, and transformation, and lack sufficient means to verify the accuracy and reliability of the information. To solve this problem, a system equipped with advanced data collection and processing capabilities is essential.

[0243] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0244] In this invention, the server includes a generation model means that receives information entered by a user via an information input terminal and searches for data based on that information; an information processing means that formats the information collected by the generation model means and presents it in a format that the user can visually confirm; and an information generation means that converts the formatted information into an information format that can be stored externally. This makes it possible for users to efficiently collect reliable information and use it in a flexible format.

[0245] A "terminal" is a device used by users to input information and to receive and verify that information.

[0246] "Information" refers to the data that a user specifies as the search target, or the labels associated with that data.

[0247] A "server" is a central management device that retrieves, formats, generates, and provides information.

[0248] "Generative model means" refers to technologies and algorithms for collecting data based on input information.

[0249] "Information processing means" refers to technology that has the function of formatting collected data and converting it into a visually easy-to-understand format.

[0250] "Information generation means" refers to technology for converting formatted information into a format that can be stored externally.

[0251] "Information sources" refer to various sources of data available on the internet.

[0252] "Evaluation" is the process of determining the reliability of a designated information source and using that judgment to select that information.

[0253] This invention relates to a system for the efficient collection, processing, and presentation of information. This system operates based on information input by a user and includes multiple means for performing information processing.

[0254] The user inputs information using a terminal. This terminal can consist of various computing devices such as personal computers, smartphones, and tablets. The terminal is responsible for collecting information through the user interface and transmitting that information to the server.

[0255] The server is the primary device for retrieving and formatting information. It utilizes a generative AI model to collect data from the internet based on the input information. This generative AI model implements natural language processing and machine learning algorithms, such as ChatGPT and BERT. This allows the server to obtain highly accurate data from diverse sources.

[0256] Furthermore, the server is equipped with information processing capabilities that format the acquired data and convert it into a user-friendly format. This conversion is intended to make data visualization more effective, and various formats such as tables and graphs can be selected.

[0257] Furthermore, the server is equipped with information generation capabilities, converting formatted information into a format that can be saved as an external file. This conversion allows for the generation of files in formats such as Excel and CSV, which can then be sent back to the terminal. Users can download these files and utilize them with their preferred data management tools.

[0258] For example, if a user wants to collect information such as "average temperature for each prefecture in Japan," they would input this information into their device and request a search from the server. The server would then use a generative AI model to collect the latest average temperatures for each prefecture from the internet, format the data, and send the results to the device. The user could then download the formatted results as a file and use them for business documents, analysis, and other purposes.

[0259] An example of a prompt to input into the generating AI model is, "Search for the average temperature for May 2023 in each prefecture of Japan and display it in a table format." This prompt allows the user to efficiently obtain the necessary information.

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] The user accesses the terminal's user interface and enters the necessary information. For example, they might enter label information such as "average temperature by prefecture in Japan." This input is then prepared as a data retrieval request to the server.

[0263] Step 2:

[0264] The terminal receives user input and sends a request to the server. The data sent includes labels and conditions specified by the user. When this request reaches the server, the information retrieval process begins.

[0265] Step 3:

[0266] The server uses a generative AI model based on the received request to search for relevant data from the internet. Following the input prompt, it extracts data from diverse sources using natural language processing algorithms. The output at this stage is the collected relevant data.

[0267] Step 4:

[0268] The server formats the data retrieved through the search using information processing tools. Specifically, it converts the retrieved data into a format that is easy for the user to understand. For example, it organizes numerical data and reconstructs it into a tabular format. The output of this step is formatted data.

[0269] Step 5:

[0270] The server sends the formatted data to the terminal. The terminal displays the received data directly in its user interface, allowing the user to verify the results. The output of this step is data in a format that the user can view on the terminal.

[0271] Step 6:

[0272] Users evaluate the displayed data and request additional information as needed. They can also request a change in the data source if the displayed data is inaccurate.

[0273] Step 7:

[0274] The user can choose to save the data as an external file as needed. The terminal sends a download request to the server, asking it to convert the data into Excel or CSV format using a file generation method.

[0275] Step 8:

[0276] The server uses information generation tools to convert the formatted data into the specified file format and sends it back to the terminal. The user can download this file and use it with a data management tool. The final output is an external file in the format specified by the user.

[0277] (Application Example 1)

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

[0279] Logistics centers require the rapid and accurate provision of information on inventory status and delivery schedules. This is necessary to enable efficient inventory management and flexible delivery planning. However, the time and effort required to acquire and format this information leads to a decrease in operational efficiency.

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

[0281] In this invention, the server includes: information generation means that receive information entered by a user and search for data on an information source based on that information; data processing means that format the data collected by the information generation means and display it to the user in a visually presentable format; file generation means that convert the formatted data into a file format that can be output externally; and logistics information management means that collect and dynamically display inventory and delivery information based on the information entered by the user. This enables rapid and accurate information acquisition and presentation, thereby improving the efficiency of logistics operations.

[0282] The "information generation means" has the function of searching for and obtaining relevant data from an information source based on the information input by the user.

[0283] The "data processing means" aims to format the collected data and present it to the user in a visual and easy-to-understand form.

[0284] The "file generation means" converts the formatted data into an externally outputable file format so that the user can easily use the data.

[0285] The "logistics information management means" provides a function of collecting and managing information on inventory status and delivery schedules based on the information input by the user and dynamically displaying it.

[0286] When implementing this invention, the server utilizes the information generation means, data processing means, file generation means, and logistics information management means to constitute a system. The server quickly searches for relevant data from information sources on the Internet based on the information input by the user from the terminal, and collects the data while considering the reliability of the information using the generated AI model. As the information generation means, a generated AI model using natural language processing technology is utilized.

[0287] The collected data is formatted through the data processing means and displayed on the terminal in a form that is easy for the user to visually recognize. Here, for the formatting of the data, a user interface using React Native is used so that the results can be easily viewed. If necessary, the formatted data is converted into a general file format such as Excel or CSV by the file generation means and provided to the user. For file generation, a programming environment using Python is used.

[0288] The logistics information management system helps users instantly check inventory status and delivery information on devices such as smartphones. When a user enters the necessary information, the server dynamically collects the latest inventory data and delivery schedules and sends the formatted information to the device.

[0289] As a concrete example, when a user enters a label such as "Tokyo inventory status" into their terminal, the server collects warehouse information for the corresponding region and displays it on the terminal in a visually easy-to-understand format. Based on this information, the user can perform appropriate inventory management and create an efficient delivery plan.

[0290] An example of a prompt message could be entered as follows:

[0291] "Please collect the latest inventory information for products in warehouses in the Tokyo area, and highlight and list products with low stock levels."

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The user operates the terminal and enters information. Specifically, the entered information relates to inventory status and delivery schedules. Based on this input, a prompt message is generated and prepared to be sent to the server.

[0295] Step 2:

[0296] The server analyzes the prompt message received from the user. Based on the analyzed prompt message, it uses a generative AI model to search for relevant data from information sources on the internet. Specifically, it utilizes natural language processing algorithms to extract appropriate keywords and collect data related to them.

[0297] Step 3:

[0298] The server formats the collected data using data processing tools. Specifically, it converts the collected raw data into a visually recognizable format such as a tabular format. At this stage, Python is used to perform appropriate data filtering and styling.

[0299] Step 4:

[0300] The server sends the formatted data to the terminal. The sent data is displayed on the terminal's user interface. Here, React Native is used to display the data in a way that makes it easy for the user to check.

[0301] Step 5:

[0302] The user reviews the data displayed on their device and, if necessary, chooses to save it as an external file. If saving is selected, the server uses a file generation mechanism to convert the formatted data into Excel or CSV format and returns it to the device as a downloadable file.

[0303] Step 6:

[0304] Users download the generated files and use them as tools for logistics management and delivery planning. These files can be opened with appropriate data management tools, improving the user's work efficiency.

[0305] The above processing steps improve the efficiency of logistics information management, allowing users to obtain accurate information in real time.

[0306] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0307] This invention is composed of a system including generation model means for performing data search on the Internet based on user input information, data processing means for formatting data and visually presenting it to the user, and file generation means for converting the formatted data into an external file format. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it aims to improve the flexibility and responsiveness of the user interface.

[0308] The terminal sends the search label input by the user to the server. The emotion engine installed on the server analyzes and infers the emotional state from the user's input actions and interactions. As a result, the server starts smooth and efficient data search by the generation model means while giving preconditions and feedback based on the collected emotion information. The generation model means constantly scans various reliable data sources on the Internet and collects data that matches the request.

[0309] Subsequently, the server formats the collected data by the data processing means. According to the analysis result of the emotion engine, it adjusts the presentation method and style of the collected data and presents the information in the form most suitable for the user's state. As a result, the user receives a data view suitable for their state, enabling efficient decision-making.

[0310] The user selects appropriate data by checking the data displayed on the terminal and evaluating its reliability and relevance. The feedback from the emotion engine continuously optimizes the operations of the data processing means and the generation model in real time, aiming to continuously improve the user experience. Furthermore, when the user needs it, a download option for the formatted data is provided, and the user can save it as an external file in the specified format.

[0311] As a concrete example, when a user is seeking information on "a company's annual revenue," they enter a label into the terminal. At this point, the emotion engine detects that the user is experiencing stress and reduces anxiety by presenting the information in a simple and easy-to-understand format. The collected data is quickly formatted and presented to the user in a calm manner. The user can comfortably review the data and, if necessary, perform detailed analysis using the download option. This format enables flexible information delivery that takes user emotions into consideration, improving work efficiency and reducing stress.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The user uses the on-device interface to enter the labels and keywords needed for data retrieval. Once the input is complete, clicking the "Search" button prepares the request to be sent to the server.

[0315] Step 2:

[0316] The terminal receives user input, formats it into JSON format, and sends it to the server. At this time, user interaction data is also sent, enabling analysis by the emotion engine.

[0317] Step 3:

[0318] The server analyzes the data received from the terminal and uses an emotion engine to estimate the user's emotional state. For example, it can determine stress or anxiety from input speed and touch patterns.

[0319] Step 4:

[0320] The server utilizes generative modeling to search for appropriate and reliable information from internet data sources based on user-specified labels. If necessary, it retrieves data from multiple sources with varying levels of reliability.

[0321] Step 5:

[0322] The data collected by the generative model is formatted by the data processing means. Based on the user's emotions analyzed by the emotion engine, the visual representation of the data is adjusted and transmitted to the terminal in the display format most suitable for the user.

[0323] Step 6:

[0324] The formatted data is displayed to the user on the device. The user reviews the visualized information and evaluates whether it meets their purpose. The user's reactions and further interactions are also sent back to the server as emotional feedback.

[0325] Step 7:

[0326] If a user wants to save data as an external file, they select the download option. The device sends this request to the server, which then uses a file generation tool to create a file in the specified format (e.g., Excel or CSV).

[0327] Step 8:

[0328] The server sends the generated files to the terminal, which the user downloads and uses for review and analysis in their local environment as needed. This entire process enables flexible and efficient information delivery that responds to emotional responses.

[0329] (Example 2)

[0330] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0331] Conventional information retrieval systems often present information without considering the user's emotional state, leading to situations where users find it difficult to understand. Furthermore, the reliability of the information provided is often a challenge, requiring users to verify it themselves, hindering efficient decision-making. Additionally, the limited functionality for adjusting format and style along with information provision makes providing flexible information to meet diverse user needs a significant challenge.

[0332] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0333] In this invention, the server includes a generation model means that receives information input by the user and searches a database based on that information; a data processing means that organizes the data collected by the generation model means and displays it in a format that can be visually provided to the user; a file generation means that converts the organized data into a file format that can be transmitted externally; and an emotion analysis means that analyzes the user's emotional state and optimizes the operation of the generation model means and the data processing means based on the analysis results. This enables the presentation of optimal information that matches the user's emotional state, the provision of highly reliable data, and flexible adjustment of the information format.

[0334] The "generative model means" is a function that performs a process of searching for data on the internet based on user input information and collecting the necessary information.

[0335] "Data processing means" refers to a function that formats the data collected by the generative model means and displays it in a format that can be visually provided to the user.

[0336] A "file generation method" is a function that converts formatted data into a file format that can be transmitted externally.

[0337] The "emotion analysis means" is a function that analyzes the user's emotional state and optimizes the operation of the generative model means and data processing means based on the analysis results.

[0338] This invention is a system for searching data on the internet based on user input information and presenting the information in the most optimal format. The specific configuration and operation of this system are described below.

[0339] The server has a generative model that receives information entered by the user using a terminal and uses that information to search a database. This model is designed to quickly and accurately collect necessary information from reliable sources worldwide. Specifically, it uses a data processing engine and AI-based algorithms to evaluate the reliability of the collected information and select the most relevant data.

[0340] Next, the server utilizes data processing tools to format the collected data. These tools adjust the data's arrangement and visual display to provide information in a user-friendly and easy-to-understand format. Examples include graphical representations of data and highlighting of key metrics.

[0341] Furthermore, the server incorporates an emotion analysis system that analyzes the user's emotional state. This function allows the system to infer the user's psychological state based on their input and interactions, and optimize the operation of the generative model and data processing systems in real time. This enables flexible information presentation tailored to the user's emotions, improving the user experience.

[0342] For example, if a user searches for "annual revenue of a company" using their device, sentiment analysis can determine that the user is experiencing stress. Based on this information, the server simplifies the presentation of the information through data processing, making it easier to understand. The information is then provided in a format that is easy for the user to comprehend, and if necessary, it is also available in a file format that can be downloaded externally.

[0343] An example of a prompt would be, "Please tell me the latest global trends in corporate annual revenues." Such prompts allow the system to search for and present appropriate information.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] The user enters a search label into the terminal. The entered label is a keyword that points to specific information based on the user's request. This information is sent as input to the server.

[0347] Step 2:

[0348] The terminal sends the search label entered by the user to the server. Once this label reaches the server, the server prepares to begin the information retrieval process.

[0349] Step 3:

[0350] The server uses sentiment analysis tools to analyze the user's input behavior and infer the user's emotional state. This analysis considers input patterns and interaction speed, and the results are used as preprocessing for data retrieval.

[0351] Step 4:

[0352] The server operates a generative model and searches databases on the internet to collect the necessary information. This process ensures the accuracy of the information by collecting data from reliable sources and verifying data reliability. The collected information becomes the server's output.

[0353] Step 5:

[0354] The server formats the collected data using data processing tools. This formatting process ensures the data is presented visually in an easily understandable way, based on the results of sentiment analysis. For example, graphs and highlighting may be used. The formatted data is then output from the server and provided to the user.

[0355] Step 6:

[0356] Users review the formatted data provided from their devices and evaluate its reliability and relevance. This evaluation is performed to support data selection and decision-making.

[0357] Step 7:

[0358] The server incorporates user feedback into a feedback loop between sentiment analysis and data processing tools to optimize information presentation. This process continuously improves the user experience.

[0359] Step 8:

[0360] Users can download the necessary data in an external file format. This feature allows them to retrieve formatted and saved data for later review and analysis.

[0361] (Application Example 2)

[0362] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0363] In today's information society, users are required to efficiently obtain the data they need from vast amounts of information. However, simply presenting information is not enough; flexible information presentation tailored to the user's emotions and state of mind is essential for improving the user experience. Current systems struggle to provide information while considering user emotions, leading to stress and dissatisfaction.

[0364] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0365] In this invention, the server includes a generation model means that receives information input by the user and searches for data on the internet based on that information; a data processing means that formats the collected data and displays it in a presentable format; a file generation means that converts the formatted data into a file format that can be downloaded externally; an emotion analysis means that analyzes the user's emotions; and a means that dynamically adjusts the display style of the information based on the emotion analysis results. This makes it possible to provide information in a form that is appropriate to the user's emotional state, thereby reducing stress and promoting efficient decision-making.

[0366] "User input" refers to the input that a user sends to a system via a terminal in order to obtain specific data or services.

[0367] A "generative model means" is a mechanism for searching for and collecting relevant data from the internet based on user input information.

[0368] "Data processing means" refers to methods for formatting collected data into a user-friendly format and presenting it visually.

[0369] A "file generation method" is a mechanism that converts formatted data into a format that can be stored externally, allowing users to download it.

[0370] "Emotional analysis methods" refer to processes for inferring and analyzing a user's emotional state based on their input and actions.

[0371] "Dynamic adjustment methods" refer to methods of changing the style and format of information presented in response to the results of user sentiment analysis.

[0372] To realize this invention, a system centered on server, terminal, and user interaction will be constructed. The server will play a central role in performing data retrieval, data formatting, sentiment analysis, and dynamic adjustment of display styles.

[0373] The server receives information entered by the user via the terminal and collects relevant data from various reliable sources on the internet. This process utilizes generative modeling technology. Generative models leverage AI technology to efficiently extract necessary information from large amounts of data. They also evaluate the reliability of specified sources and select the most appropriate data based on their reliability.

[0374] Next, the server formats the collected data and converts it into a user-friendly format. This formatting process utilizes data processing tools. Furthermore, sentiment analysis tools are used to analyze the user's emotional state and dynamically adjust the information display style to match the user's psychological state. This allows information to be presented to the user in the most optimal way, reducing stress and confusion.

[0375] For example, if a user requests detailed information about a specific product, the server quickly collects reviews and specifications for that product. If the server determines from the user's facial expressions and voice that they are experiencing stress, it displays the information simply and intuitively. For sentiment analysis, the Affectiva SDK or Google Cloud AI's Sentiment Analysis API can be used. Furthermore, Python's Beautiful Soup and Pandas libraries are useful for specific data collection and formatting.

[0376] This system not only supports efficient decision-making but also improves the user experience, solving the challenges of conventional technologies. An example of a prompt message is as follows: "Collect the latest product reviews related to the '{product name}' entered by the user, and if the user is feeling anxious, extract and present reassuring points from the reviews." Using this prompt enables information delivery tailored to the user's emotions, creating a user-friendly environment.

[0377] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0378] Step 1:

[0379] The terminal receives user input information and sends it to the server. This input information includes data about specific product names or services. This allows the server to initiate the search process.

[0380] Step 2:

[0381] The server uses a generative AI model based on the received input information to search for data on the internet. The server uses AI algorithms to extract and collect relevant data from reliable sources. The collected data is in its raw, unprocessed state.

[0382] Step 3:

[0383] The server performs reliability assessments and selects information deemed highly reliable from the collected data. During this selection process, it utilizes the reliability score of the data source to filter the necessary data.

[0384] Step 4:

[0385] The server formats the selected data using data processing tools. Specifically, it uses the Python Pandas library to convert the data into a visually understandable format, such as tables or graphs. This formatted data then becomes the input data for the next process.

[0386] Step 5:

[0387] The emotion analysis system analyzes the user's emotional state from their input actions and interactions via the device. Using the Affectiva SDK, it collects and analyzes the user's emotional data and sends it to the server. This analysis result is used for adjustments in the next step.

[0388] Step 6:

[0389] The server adjusts the presentation style of formatted data based on sentiment analysis results. For example, if the user is experiencing stress, the information is simplified and visually reduced. This adjustment includes summarizing and highlighting data.

[0390] Step 7:

[0391] The server sends the processed data to the terminal, allowing the user to visually review it. The user can then make decisions based on this data and, if necessary, view more detailed information or download it in an external file format.

[0392] Through this process, the server utilizes generative AI models and the latest sentiment analysis technologies to deliver user-centric information.

[0393] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0394] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0395] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0399] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0400] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0401] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0402] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0403] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0404] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0405] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0406] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0407] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0408] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0409] To implement this invention, it is necessary to have a terminal equipped with a user interface and a server that performs data retrieval and processing. The user starts the system by entering an information label on the terminal and sends an information request to the server. The server automatically searches for relevant data on the internet using a generative model. Because this generative model utilizes natural language processing and machine learning, it can collect accurate data from diverse information sources.

[0410] Next, the server formats the collected data using data processing tools and reconstructs it in a visually recognizable format (e.g., tabular format) for the user. This formatted data is then sent to the terminal, allowing the user to review and evaluate the results. If the displayed data is found to contain errors or inappropriate information, the user may have the option to choose a different source for further information, taking reliability into consideration.

[0411] Furthermore, users are given the option to save the processed data as an external file. The device sends a download request to the server, which generates an external file in formats such as Excel or CSV using a file generation method, and sends it back to the device. The user can then download this external file and use it with any data management tool.

[0412] As a concrete example, if a user wants to create a table of "average temperatures by region," they input label information on their terminal and request a search from the server. The server uses a generative model to collect the latest average temperature data for each region from the internet, formats it, and sends the results to the terminal. The user can then download the formatted results and use them as business data. This method significantly reduces the time and effort required for users to acquire and format information.

[0413] The following describes the processing flow.

[0414] Step 1:

[0415] The user uses their device to enter the necessary labels and keywords into the input forms displayed on the interface. This defines the type and structure of the information to be searched.

[0416] Step 2:

[0417] The terminal receives user input, converts it to an appropriate format (e.g., JSON), and then sends a request to the server. The request includes the label information entered by the user.

[0418] Step 3:

[0419] The server analyzes the request received from the terminal and activates the generative model. The generative model analyzes the input labels and keywords and searches for relevant data from databases and web APIs based on that analysis.

[0420] Step 4:

[0421] The generative model collects necessary information from reliable data sources on the internet and then formats that data on a server. Data formatting includes filtering out unnecessary information and converting it to the format requested by the user.

[0422] Step 5:

[0423] The server sends the formatted data to the terminal. The data may include links to the original source and metadata regarding the data's reliability. The terminal displays the received data on an interface that shows the information in a visually easy-to-understand format.

[0424] Step 6:

[0425] Users can view the results on screen and evaluate the data as needed. Furthermore, by selecting the download option, they can save the formatted data as an external file.

[0426] Step 7:

[0427] The terminal sends a download request to the server, and the server generates a file in the specified format (e.g., Excel or CSV) using a file generation mechanism. The generated file is sent to the terminal, and the user can download it and use it in their local environment.

[0428] (Example 1)

[0429] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0430] In today's world, where vast amounts of data exist on the internet, there is a need to efficiently and accurately acquire the information users desire and organize and present it in the required format. However, conventional systems require considerable effort and time for information collection, formatting, and transformation, and lack sufficient means to verify the accuracy and reliability of the information. To solve this problem, a system equipped with advanced data collection and processing capabilities is essential.

[0431] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0432] In this invention, the server includes a generation model means that receives information entered by a user via an information input terminal and searches for data based on that information; an information processing means that formats the information collected by the generation model means and presents it in a format that the user can visually confirm; and an information generation means that converts the formatted information into an information format that can be stored externally. This makes it possible for users to efficiently collect reliable information and use it in a flexible format.

[0433] A "terminal" is a device used by users to input information and to receive and verify that information.

[0434] "Information" refers to the data that a user specifies as the search target, or the labels associated with that data.

[0435] A "server" is a central management device that retrieves, formats, generates, and provides information.

[0436] "Generative model means" refers to technologies and algorithms for collecting data based on input information.

[0437] "Information processing means" refers to technology that has the function of formatting collected data and converting it into a visually easy-to-understand format.

[0438] "Information generation means" refers to technology for converting formatted information into a format that can be stored externally.

[0439] "Information sources" refer to various sources of data available on the internet.

[0440] "Evaluation" is the process of determining the reliability of a designated information source and using that judgment to select that information.

[0441] This invention relates to a system for the efficient collection, processing, and presentation of information. This system operates based on information input by a user and includes multiple means for performing information processing.

[0442] The user inputs information using a terminal. This terminal can consist of various computing devices such as personal computers, smartphones, and tablets. The terminal is responsible for collecting information through the user interface and transmitting that information to the server.

[0443] The server is the primary device for retrieving and formatting information. It utilizes a generative AI model to collect data from the internet based on the input information. This generative AI model implements natural language processing and machine learning algorithms, such as ChatGPT and BERT. This allows the server to obtain highly accurate data from diverse sources.

[0444] Furthermore, the server is equipped with information processing capabilities that format the acquired data and convert it into a user-friendly format. This conversion is intended to make data visualization more effective, and various formats such as tables and graphs can be selected.

[0445] Furthermore, the server is equipped with information generation capabilities, converting formatted information into a format that can be saved as an external file. This conversion allows for the generation of files in formats such as Excel and CSV, which can then be sent back to the terminal. Users can download these files and utilize them with their preferred data management tools.

[0446] For example, if a user wants to collect information such as "average temperature for each prefecture in Japan," they would input this information into their device and request a search from the server. The server would then use a generative AI model to collect the latest average temperatures for each prefecture from the internet, format the data, and send the results to the device. The user could then download the formatted results as a file and use them for business documents, analysis, and other purposes.

[0447] An example of a prompt to input into the generating AI model is, "Search for the average temperature for May 2023 in each prefecture of Japan and display it in a table format." This prompt allows the user to efficiently obtain the necessary information.

[0448] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0449] Step 1:

[0450] The user accesses the terminal's user interface and enters the necessary information. For example, they might enter label information such as "average temperature by prefecture in Japan." This input is then prepared as a data retrieval request to the server.

[0451] Step 2:

[0452] The terminal receives user input and sends a request to the server. The data sent includes labels and conditions specified by the user. When this request reaches the server, the information retrieval process begins.

[0453] Step 3:

[0454] The server uses a generative AI model based on the received request to search for relevant data from the internet. Following the input prompt, it extracts data from diverse sources using natural language processing algorithms. The output at this stage is the collected relevant data.

[0455] Step 4:

[0456] The server formats the data retrieved through the search using information processing tools. Specifically, it converts the retrieved data into a format that is easy for the user to understand. For example, it organizes numerical data and reconstructs it into a tabular format. The output of this step is formatted data.

[0457] Step 5:

[0458] The server sends the formatted data to the terminal. The terminal displays the received data directly in its user interface, allowing the user to verify the results. The output of this step is data in a format that the user can view on the terminal.

[0459] Step 6:

[0460] Users evaluate the displayed data and request additional information as needed. They can also request a change in the data source if the displayed data is inaccurate.

[0461] Step 7:

[0462] The user can choose to save the data as an external file as needed. The terminal sends a download request to the server, asking it to convert the data into Excel or CSV format using a file generation method.

[0463] Step 8:

[0464] The server uses information generation tools to convert the formatted data into the specified file format and sends it back to the terminal. The user can download this file and use it with a data management tool. The final output is an external file in the format specified by the user.

[0465] (Application Example 1)

[0466] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0467] Logistics centers require the rapid and accurate provision of information on inventory status and delivery schedules. This is necessary to enable efficient inventory management and flexible delivery planning. However, the time and effort required to acquire and format this information leads to a decrease in operational efficiency.

[0468] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0469] In this invention, the server includes: information generation means that receive information entered by a user and search for data on an information source based on that information; data processing means that format the data collected by the information generation means and display it to the user in a visually presentable format; file generation means that convert the formatted data into a file format that can be output externally; and logistics information management means that collect and dynamically display inventory and delivery information based on the information entered by the user. This enables rapid and accurate information acquisition and presentation, thereby improving the efficiency of logistics operations.

[0470] An "information generation means" is a device that has the function of searching for and obtaining relevant data from information sources based on information entered by the user.

[0471] "Data processing means" refers to methods that aim to format collected data and present it to the user in a visually clear and easy-to-understand format.

[0472] A "file generation method" is a method that converts formatted data into a file format that can be output externally, making the data easily usable by the user.

[0473] A "logistics information management system" is a system that collects, manages, and dynamically displays information such as inventory status and delivery schedules based on information entered by the user.

[0474] In implementing this invention, the server configures the system by making full use of information generation means, data processing means, file generation means, and logistics information management means. Based on the information input by the user from the terminal, the server quickly searches for relevant data from information sources on the internet and collects the data while considering the reliability of the information using a generative AI model. As the information generation means, a generative AI model using natural language processing technology is utilized.

[0475] The collected data is formatted through data processing and displayed on the device in a visually recognizable format. Here, the data formatting is performed using a user interface based on React Native, making the results easily viewable. If necessary, the formatted data is converted to common file formats such as Excel or CSV using a file generation tool and provided to the user. A Python programming environment is used for file generation.

[0476] The logistics information management system helps users instantly check inventory status and delivery information on devices such as smartphones. When a user enters the necessary information, the server dynamically collects the latest inventory data and delivery schedules and sends the formatted information to the device.

[0477] As a concrete example, when a user enters a label such as "Tokyo inventory status" into their terminal, the server collects warehouse information for the corresponding region and displays it on the terminal in a visually easy-to-understand format. Based on this information, the user can perform appropriate inventory management and create an efficient delivery plan.

[0478] An example of a prompt message could be entered as follows:

[0479] "Please collect the latest inventory information for products in warehouses in the Tokyo area, and highlight and list products with low stock levels."

[0480] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0481] Step 1:

[0482] The user operates the terminal and enters information. Specifically, the entered information relates to inventory status and delivery schedules. Based on this input, a prompt message is generated and prepared to be sent to the server.

[0483] Step 2:

[0484] The server analyzes the prompt message received from the user. Based on the analyzed prompt message, it uses a generative AI model to search for relevant data from information sources on the internet. Specifically, it utilizes natural language processing algorithms to extract appropriate keywords and collect data related to them.

[0485] Step 3:

[0486] The server formats the collected data using data processing tools. Specifically, it converts the collected raw data into a visually recognizable format such as a tabular format. At this stage, Python is used to perform appropriate data filtering and styling.

[0487] Step 4:

[0488] The server sends the formatted data to the terminal. The sent data is displayed on the terminal's user interface. Here, React Native is used to display the data in a way that makes it easy for the user to check.

[0489] Step 5:

[0490] The user reviews the data displayed on their device and, if necessary, chooses to save it as an external file. If saving is selected, the server uses a file generation mechanism to convert the formatted data into Excel or CSV format and returns it to the device as a downloadable file.

[0491] Step 6:

[0492] Users download the generated files and use them as tools for logistics management and delivery planning. These files can be opened with appropriate data management tools, improving the user's work efficiency.

[0493] The above processing steps improve the efficiency of logistics information management, allowing users to obtain accurate information in real time.

[0494] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0495] This invention comprises a system including a generation model means for performing data retrieval on the internet based on user input information, a data processing means for formatting the data and presenting it visually to the user, and a file generation means for converting the formatted data into an external file format. Furthermore, it aims to improve the flexibility and responsiveness of the user interface by incorporating an emotion engine that recognizes the user's emotions.

[0496] The terminal sends the search label entered by the user to the server. The emotion engine installed on the server analyzes and infers the emotional state from the user's input actions and interactions. Based on the collected emotional information, the server provides preconditions and feedback, and then smoothly and efficiently initiates data retrieval using a generative model. The generative model constantly scans various reliable data sources on the internet and collects data that matches the request.

[0497] Next, the server formats the collected data using data processing tools. Based on the analysis results of the emotion engine, it adjusts the presentation method and style of the collected data to present information in a way that is optimal for the user's state. As a result, the user receives a data view that is appropriate to their state, enabling efficient decision-making.

[0498] Users select appropriate data by reviewing the data displayed on their device and evaluating its reliability and relevance. The emotion engine provides feedback that continuously optimizes data processing methods and generative models in real time, leading to a continuous improvement in the user experience. Furthermore, if needed, users are provided with the option to download formatted data, which they can save as an external file in their specified format.

[0499] As a concrete example, when a user is seeking information on "a company's annual revenue," they enter a label into the terminal. At this point, the emotion engine detects that the user is experiencing stress and reduces anxiety by presenting the information in a simple and easy-to-understand format. The collected data is quickly formatted and presented to the user in a calm manner. The user can comfortably review the data and, if necessary, perform detailed analysis using the download option. This format enables flexible information delivery that takes user emotions into consideration, improving work efficiency and reducing stress.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] The user uses the on-device interface to enter the labels and keywords needed for data retrieval. Once the input is complete, clicking the "Search" button prepares the request to be sent to the server.

[0503] Step 2:

[0504] The terminal receives user input, formats it into JSON format, and sends it to the server. At this time, user interaction data is also sent, enabling analysis by the emotion engine.

[0505] Step 3:

[0506] The server analyzes the data received from the terminal and uses an emotion engine to estimate the user's emotional state. For example, it can determine stress or anxiety from input speed and touch patterns.

[0507] Step 4:

[0508] The server utilizes generative modeling to search for appropriate and reliable information from internet data sources based on user-specified labels. If necessary, it retrieves data from multiple sources with varying levels of reliability.

[0509] Step 5:

[0510] The data collected by the generative model is formatted by the data processing means. Based on the user's emotions analyzed by the emotion engine, the visual representation of the data is adjusted and transmitted to the terminal in the display format most suitable for the user.

[0511] Step 6:

[0512] The formatted data is displayed to the user on the device. The user reviews the visualized information and evaluates whether it meets their purpose. The user's reactions and further interactions are also sent back to the server as emotional feedback.

[0513] Step 7:

[0514] If a user wants to save data as an external file, they select the download option. The device sends this request to the server, which then uses a file generation tool to create a file in the specified format (e.g., Excel or CSV).

[0515] Step 8:

[0516] The server sends the generated files to the terminal, which the user downloads and uses for review and analysis in their local environment as needed. This entire process enables flexible and efficient information delivery that responds to emotional responses.

[0517] (Example 2)

[0518] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0519] Conventional information retrieval systems often present information without considering the user's emotional state, leading to situations where users find it difficult to understand. Furthermore, the reliability of the information provided is often a challenge, requiring users to verify it themselves, hindering efficient decision-making. Additionally, the limited functionality for adjusting format and style along with information provision makes providing flexible information to meet diverse user needs a significant challenge.

[0520] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0521] In this invention, the server includes a generation model means that receives information input by the user and searches a database based on that information; a data processing means that organizes the data collected by the generation model means and displays it in a format that can be visually provided to the user; a file generation means that converts the organized data into a file format that can be transmitted externally; and an emotion analysis means that analyzes the user's emotional state and optimizes the operation of the generation model means and the data processing means based on the analysis results. This enables the presentation of optimal information that matches the user's emotional state, the provision of highly reliable data, and flexible adjustment of the information format.

[0522] The "generative model means" is a function that performs a process of searching for data on the internet based on user input information and collecting the necessary information.

[0523] "Data processing means" refers to a function that formats the data collected by the generative model means and displays it in a format that can be visually provided to the user.

[0524] A "file generation method" is a function that converts formatted data into a file format that can be transmitted externally.

[0525] The "emotion analysis means" is a function that analyzes the user's emotional state and optimizes the operation of the generative model means and data processing means based on the analysis results.

[0526] This invention is a system for searching data on the internet based on user input information and presenting the information in the most optimal format. The specific configuration and operation of this system are described below.

[0527] The server has a generative model that receives information entered by the user using a terminal and uses that information to search a database. This model is designed to quickly and accurately collect necessary information from reliable sources worldwide. Specifically, it uses a data processing engine and AI-based algorithms to evaluate the reliability of the collected information and select the most relevant data.

[0528] Next, the server utilizes data processing tools to format the collected data. These tools adjust the data's arrangement and visual display to provide information in a user-friendly and easy-to-understand format. Examples include graphical representations of data and highlighting of key metrics.

[0529] Furthermore, the server incorporates an emotion analysis system that analyzes the user's emotional state. This function allows the system to infer the user's psychological state based on their input and interactions, and optimize the operation of the generative model and data processing systems in real time. This enables flexible information presentation tailored to the user's emotions, improving the user experience.

[0530] For example, if a user searches for "annual revenue of a company" using their device, sentiment analysis can determine that the user is experiencing stress. Based on this information, the server simplifies the presentation of the information through data processing, making it easier to understand. The information is then provided in a format that is easy for the user to comprehend, and if necessary, it is also available in a file format that can be downloaded externally.

[0531] An example of a prompt would be, "Please tell me the latest global trends in corporate annual revenues." Such prompts allow the system to search for and present appropriate information.

[0532] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0533] Step 1:

[0534] The user enters a search label into the terminal. The entered label is a keyword that points to specific information based on the user's request. This information is sent as input to the server.

[0535] Step 2:

[0536] The terminal sends the search label entered by the user to the server. Once this label reaches the server, the server prepares to begin the information retrieval process.

[0537] Step 3:

[0538] The server uses sentiment analysis tools to analyze the user's input behavior and infer the user's emotional state. This analysis considers input patterns and interaction speed, and the results are used as preprocessing for data retrieval.

[0539] Step 4:

[0540] The server operates a generative model and searches databases on the internet to collect the necessary information. This process ensures the accuracy of the information by collecting data from reliable sources and verifying data reliability. The collected information becomes the server's output.

[0541] Step 5:

[0542] The server formats the collected data using data processing tools. This formatting process ensures the data is presented visually in an easily understandable way, based on the results of sentiment analysis. For example, graphs and highlighting may be used. The formatted data is then output from the server and provided to the user.

[0543] Step 6:

[0544] Users review the formatted data provided from their devices and evaluate its reliability and relevance. This evaluation is performed to support data selection and decision-making.

[0545] Step 7:

[0546] The server incorporates user feedback into a feedback loop between sentiment analysis and data processing tools to optimize information presentation. This process continuously improves the user experience.

[0547] Step 8:

[0548] Users can download the necessary data in an external file format. This feature allows them to retrieve formatted and saved data for later review and analysis.

[0549] (Application Example 2)

[0550] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0551] In today's information society, users are required to efficiently obtain the data they need from vast amounts of information. However, simply presenting information is not enough; flexible information presentation tailored to the user's emotions and state of mind is essential for improving the user experience. Current systems struggle to provide information while considering user emotions, leading to stress and dissatisfaction.

[0552] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0553] In this invention, the server includes a generation model means that receives information input by the user and searches for data on the internet based on that information; a data processing means that formats the collected data and displays it in a presentable format; a file generation means that converts the formatted data into a file format that can be downloaded externally; an emotion analysis means that analyzes the user's emotions; and a means that dynamically adjusts the display style of the information based on the emotion analysis results. This makes it possible to provide information in a form that is appropriate to the user's emotional state, thereby reducing stress and promoting efficient decision-making.

[0554] "User input" refers to the input that a user sends to a system via a terminal in order to obtain specific data or services.

[0555] A "generative model means" is a mechanism for searching for and collecting relevant data from the internet based on user input information.

[0556] "Data processing means" refers to methods for formatting collected data into a user-friendly format and presenting it visually.

[0557] A "file generation method" is a mechanism that converts formatted data into a format that can be stored externally, allowing users to download it.

[0558] "Emotional analysis methods" refer to processes for inferring and analyzing a user's emotional state based on their input and actions.

[0559] "Dynamic adjustment methods" refer to methods of changing the style and format of information presented in response to the results of user sentiment analysis.

[0560] To realize this invention, a system centered on server, terminal, and user interaction will be constructed. The server will play a central role in performing data retrieval, data formatting, sentiment analysis, and dynamic adjustment of display styles.

[0561] The server receives information entered by the user via the terminal and collects relevant data from various reliable sources on the internet. This process utilizes generative modeling technology. Generative models leverage AI technology to efficiently extract necessary information from large amounts of data. They also evaluate the reliability of specified sources and select the most appropriate data based on their reliability.

[0562] Next, the server formats the collected data and converts it into a user-friendly format. This formatting process utilizes data processing tools. Furthermore, sentiment analysis tools are used to analyze the user's emotional state and dynamically adjust the information display style to match the user's psychological state. This allows information to be presented to the user in the most optimal way, reducing stress and confusion.

[0563] For example, if a user requests detailed information about a specific product, the server quickly collects reviews and specifications for that product. If the server determines from the user's facial expressions and voice that they are experiencing stress, it displays the information simply and intuitively. For sentiment analysis, the Affectiva SDK or Google Cloud AI's Sentiment Analysis API can be used. Furthermore, Python's Beautiful Soup and Pandas libraries are useful for specific data collection and formatting.

[0564] This system not only supports efficient decision-making but also improves the user experience, solving the challenges of conventional technologies. An example of a prompt message is as follows: "Collect the latest product reviews related to the '{product name}' entered by the user, and if the user is feeling anxious, extract and present reassuring points from the reviews." Using this prompt enables information delivery tailored to the user's emotions, creating a user-friendly environment.

[0565] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0566] Step 1:

[0567] The terminal receives user input information and sends it to the server. This input information includes data about specific product names or services. This allows the server to initiate the search process.

[0568] Step 2:

[0569] The server uses a generative AI model based on the received input information to search for data on the internet. The server uses AI algorithms to extract and collect relevant data from reliable sources. The collected data is in its raw, unprocessed state.

[0570] Step 3:

[0571] The server performs reliability assessments and selects information deemed highly reliable from the collected data. During this selection process, it utilizes the reliability score of the data source to filter the necessary data.

[0572] Step 4:

[0573] The server formats the selected data using data processing tools. Specifically, it uses the Python Pandas library to convert the data into a visually understandable format, such as tables or graphs. This formatted data then becomes the input data for the next process.

[0574] Step 5:

[0575] The emotion analysis system analyzes the user's emotional state from their input actions and interactions via the device. Using the Affectiva SDK, it collects and analyzes the user's emotional data and sends it to the server. This analysis result is used for adjustments in the next step.

[0576] Step 6:

[0577] The server adjusts the presentation style of formatted data based on sentiment analysis results. For example, if the user is experiencing stress, the information is simplified and visually reduced. This adjustment includes summarizing and highlighting data.

[0578] Step 7:

[0579] The server sends the processed data to the terminal, allowing the user to visually review it. The user can then make decisions based on this data and, if necessary, view more detailed information or download it in an external file format.

[0580] Through this process, the server utilizes generative AI models and the latest sentiment analysis technologies to deliver user-centric information.

[0581] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0582] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0583] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0584] [Fourth Embodiment]

[0585] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0586] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0587] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0588] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0589] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0590] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0591] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0592] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0593] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0594] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0595] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0596] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0597] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0598] To implement this invention, it is necessary to have a terminal equipped with a user interface and a server that performs data retrieval and processing. The user starts the system by entering an information label on the terminal and sends an information request to the server. The server automatically searches for relevant data on the internet using a generative model. Because this generative model utilizes natural language processing and machine learning, it can collect accurate data from diverse information sources.

[0599] Next, the server formats the collected data using data processing tools and reconstructs it in a visually recognizable format (e.g., tabular format) for the user. This formatted data is then sent to the terminal, allowing the user to review and evaluate the results. If the displayed data is found to contain errors or inappropriate information, the user may have the option to choose a different source for further information, taking reliability into consideration.

[0600] Furthermore, users are given the option to save the processed data as an external file. The device sends a download request to the server, which generates an external file in formats such as Excel or CSV using a file generation method, and sends it back to the device. The user can then download this external file and use it with any data management tool.

[0601] As a concrete example, if a user wants to create a table of "average temperatures by region," they input label information on their terminal and request a search from the server. The server uses a generative model to collect the latest average temperature data for each region from the internet, formats it, and sends the results to the terminal. The user can then download the formatted results and use them as business data. This method significantly reduces the time and effort required for users to acquire and format information.

[0602] The following describes the processing flow.

[0603] Step 1:

[0604] The user uses their device to enter the necessary labels and keywords into the input forms displayed on the interface. This defines the type and structure of the information to be searched.

[0605] Step 2:

[0606] The terminal receives user input, converts it to an appropriate format (e.g., JSON), and then sends a request to the server. The request includes the label information entered by the user.

[0607] Step 3:

[0608] The server analyzes the request received from the terminal and activates the generative model. The generative model analyzes the input labels and keywords and searches for relevant data from databases and web APIs based on that analysis.

[0609] Step 4:

[0610] The generative model collects necessary information from reliable data sources on the internet and then formats that data on a server. Data formatting includes filtering out unnecessary information and converting it to the format requested by the user.

[0611] Step 5:

[0612] The server sends the formatted data to the terminal. The data may include links to the original source and metadata regarding the data's reliability. The terminal displays the received data on an interface that shows the information in a visually easy-to-understand format.

[0613] Step 6:

[0614] Users can view the results on screen and evaluate the data as needed. Furthermore, by selecting the download option, they can save the formatted data as an external file.

[0615] Step 7:

[0616] The terminal sends a download request to the server, and the server generates a file in the specified format (e.g., Excel or CSV) using a file generation mechanism. The generated file is sent to the terminal, and the user can download it and use it in their local environment.

[0617] (Example 1)

[0618] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0619] In today's world, where vast amounts of data exist on the internet, there is a need to efficiently and accurately acquire the information users desire and organize and present it in the required format. However, conventional systems require considerable effort and time for information collection, formatting, and transformation, and lack sufficient means to verify the accuracy and reliability of the information. To solve this problem, a system equipped with advanced data collection and processing capabilities is essential.

[0620] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0621] In this invention, the server includes a generation model means that receives information entered by a user via an information input terminal and searches for data based on that information; an information processing means that formats the information collected by the generation model means and presents it in a format that the user can visually confirm; and an information generation means that converts the formatted information into an information format that can be stored externally. This makes it possible for users to efficiently collect reliable information and use it in a flexible format.

[0622] A "terminal" is a device used by users to input information and to receive and verify that information.

[0623] "Information" refers to the data that a user specifies as the search target, or the labels associated with that data.

[0624] A "server" is a central management device that retrieves, formats, generates, and provides information.

[0625] "Generative model means" refers to technologies and algorithms for collecting data based on input information.

[0626] "Information processing means" refers to technology that has the function of formatting collected data and converting it into a visually easy-to-understand format.

[0627] "Information generation means" refers to technology for converting formatted information into a format that can be stored externally.

[0628] "Information sources" refer to various sources of data available on the internet.

[0629] "Evaluation" is the process of determining the reliability of a designated information source and using that judgment to select that information.

[0630] This invention relates to a system for the efficient collection, processing, and presentation of information. This system operates based on information input by a user and includes multiple means for performing information processing.

[0631] The user inputs information using a terminal. This terminal can consist of various computing devices such as personal computers, smartphones, and tablets. The terminal is responsible for collecting information through the user interface and transmitting that information to the server.

[0632] The server is the primary device for retrieving and formatting information. It utilizes a generative AI model to collect data from the internet based on the input information. This generative AI model implements natural language processing and machine learning algorithms, such as ChatGPT and BERT. This allows the server to obtain highly accurate data from diverse sources.

[0633] Furthermore, the server is equipped with information processing capabilities that format the acquired data and convert it into a user-friendly format. This conversion is intended to make data visualization more effective, and various formats such as tables and graphs can be selected.

[0634] Furthermore, the server is equipped with information generation capabilities, converting formatted information into a format that can be saved as an external file. This conversion allows for the generation of files in formats such as Excel and CSV, which can then be sent back to the terminal. Users can download these files and utilize them with their preferred data management tools.

[0635] For example, if a user wants to collect information such as "average temperature for each prefecture in Japan," they would input this information into their device and request a search from the server. The server would then use a generative AI model to collect the latest average temperatures for each prefecture from the internet, format the data, and send the results to the device. The user could then download the formatted results as a file and use them for business documents, analysis, and other purposes.

[0636] An example of a prompt to input into the generating AI model is, "Search for the average temperature for May 2023 in each prefecture of Japan and display it in a table format." This prompt allows the user to efficiently obtain the necessary information.

[0637] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0638] Step 1:

[0639] The user accesses the terminal's user interface and enters the necessary information. For example, they might enter label information such as "average temperature by prefecture in Japan." This input is then prepared as a data retrieval request to the server.

[0640] Step 2:

[0641] The terminal receives user input and sends a request to the server. The data sent includes labels and conditions specified by the user. When this request reaches the server, the information retrieval process begins.

[0642] Step 3:

[0643] The server uses a generative AI model based on the received request to search for relevant data from the internet. Following the input prompt, it extracts data from diverse sources using natural language processing algorithms. The output at this stage is the collected relevant data.

[0644] Step 4:

[0645] The server formats the data retrieved through the search using information processing tools. Specifically, it converts the retrieved data into a format that is easy for the user to understand. For example, it organizes numerical data and reconstructs it into a tabular format. The output of this step is formatted data.

[0646] Step 5:

[0647] The server sends the formatted data to the terminal. The terminal displays the received data directly in its user interface, allowing the user to verify the results. The output of this step is data in a format that the user can view on the terminal.

[0648] Step 6:

[0649] Users evaluate the displayed data and request additional information as needed. They can also request a change in the data source if the displayed data is inaccurate.

[0650] Step 7:

[0651] The user can choose to save the data as an external file as needed. The terminal sends a download request to the server, asking it to convert the data into Excel or CSV format using a file generation method.

[0652] Step 8:

[0653] The server uses information generation tools to convert the formatted data into the specified file format and sends it back to the terminal. The user can download this file and use it with a data management tool. The final output is an external file in the format specified by the user.

[0654] (Application Example 1)

[0655] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] Logistics centers require the rapid and accurate provision of information on inventory status and delivery schedules. This is necessary to enable efficient inventory management and flexible delivery planning. However, the time and effort required to acquire and format this information leads to a decrease in operational efficiency.

[0657] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0658] In this invention, the server includes: information generation means that receive information entered by a user and search for data on an information source based on that information; data processing means that format the data collected by the information generation means and display it to the user in a visually presentable format; file generation means that convert the formatted data into a file format that can be output externally; and logistics information management means that collect and dynamically display inventory and delivery information based on the information entered by the user. This enables rapid and accurate information acquisition and presentation, thereby improving the efficiency of logistics operations.

[0659] An "information generation means" is a device that has the function of searching for and obtaining relevant data from information sources based on information entered by the user.

[0660] "Data processing means" refers to methods that aim to format collected data and present it to the user in a visually clear and easy-to-understand format.

[0661] A "file generation method" is a method that converts formatted data into a file format that can be output externally, making the data easily usable by the user.

[0662] A "logistics information management system" is a system that collects, manages, and dynamically displays information such as inventory status and delivery schedules based on information entered by the user.

[0663] In implementing this invention, the server configures the system by making full use of information generation means, data processing means, file generation means, and logistics information management means. Based on the information input by the user from the terminal, the server quickly searches for relevant data from information sources on the internet and collects the data while considering the reliability of the information using a generative AI model. As the information generation means, a generative AI model using natural language processing technology is utilized.

[0664] The collected data is formatted through data processing and displayed on the device in a visually recognizable format. Here, the data formatting is performed using a user interface based on React Native, making the results easily viewable. If necessary, the formatted data is converted to common file formats such as Excel or CSV using a file generation tool and provided to the user. A Python programming environment is used for file generation.

[0665] The logistics information management system helps users instantly check inventory status and delivery information on devices such as smartphones. When a user enters the necessary information, the server dynamically collects the latest inventory data and delivery schedules and sends the formatted information to the device.

[0666] As a concrete example, when a user enters a label such as "Tokyo inventory status" into their terminal, the server collects warehouse information for the corresponding region and displays it on the terminal in a visually easy-to-understand format. Based on this information, the user can perform appropriate inventory management and create an efficient delivery plan.

[0667] An example of a prompt message could be entered as follows:

[0668] "Please collect the latest inventory information for products in warehouses in the Tokyo area, and highlight and list products with low stock levels."

[0669] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0670] Step 1:

[0671] The user operates the terminal and enters information. Specifically, the entered information relates to inventory status and delivery schedules. Based on this input, a prompt message is generated and prepared to be sent to the server.

[0672] Step 2:

[0673] The server analyzes the prompt message received from the user. Based on the analyzed prompt message, it uses a generative AI model to search for relevant data from information sources on the internet. Specifically, it utilizes natural language processing algorithms to extract appropriate keywords and collect data related to them.

[0674] Step 3:

[0675] The server formats the collected data using data processing tools. Specifically, it converts the collected raw data into a visually recognizable format such as a tabular format. At this stage, Python is used to perform appropriate data filtering and styling.

[0676] Step 4:

[0677] The server sends the formatted data to the terminal. The sent data is displayed on the terminal's user interface. Here, React Native is used to display the data in a way that makes it easy for the user to check.

[0678] Step 5:

[0679] The user reviews the data displayed on their device and, if necessary, chooses to save it as an external file. If saving is selected, the server uses a file generation mechanism to convert the formatted data into Excel or CSV format and returns it to the device as a downloadable file.

[0680] Step 6:

[0681] Users download the generated files and use them as tools for logistics management and delivery planning. These files can be opened with appropriate data management tools, improving the user's work efficiency.

[0682] The above processing steps improve the efficiency of logistics information management, allowing users to obtain accurate information in real time.

[0683] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0684] This invention comprises a system including a generation model means for performing data retrieval on the internet based on user input information, a data processing means for formatting the data and presenting it visually to the user, and a file generation means for converting the formatted data into an external file format. Furthermore, it aims to improve the flexibility and responsiveness of the user interface by incorporating an emotion engine that recognizes the user's emotions.

[0685] The terminal sends the search label entered by the user to the server. The emotion engine installed on the server analyzes and infers the emotional state from the user's input actions and interactions. Based on the collected emotional information, the server provides preconditions and feedback, and then smoothly and efficiently initiates data retrieval using a generative model. The generative model constantly scans various reliable data sources on the internet and collects data that matches the request.

[0686] Next, the server formats the collected data using data processing tools. Based on the analysis results of the emotion engine, it adjusts the presentation method and style of the collected data to present information in a way that is optimal for the user's state. As a result, the user receives a data view that is appropriate to their state, enabling efficient decision-making.

[0687] Users select appropriate data by reviewing the data displayed on their device and evaluating its reliability and relevance. The emotion engine provides feedback that continuously optimizes data processing methods and generative models in real time, leading to a continuous improvement in the user experience. Furthermore, if needed, users are provided with the option to download formatted data, which they can save as an external file in their specified format.

[0688] As a concrete example, when a user is seeking information on "a company's annual revenue," they enter a label into the terminal. At this point, the emotion engine detects that the user is experiencing stress and reduces anxiety by presenting the information in a simple and easy-to-understand format. The collected data is quickly formatted and presented to the user in a calm manner. The user can comfortably review the data and, if necessary, perform detailed analysis using the download option. This format enables flexible information delivery that takes user emotions into consideration, improving work efficiency and reducing stress.

[0689] The following describes the processing flow.

[0690] Step 1:

[0691] The user uses the on-device interface to enter the labels and keywords needed for data retrieval. Once the input is complete, clicking the "Search" button prepares the request to be sent to the server.

[0692] Step 2:

[0693] The terminal receives user input, formats it into JSON format, and sends it to the server. At this time, user interaction data is also sent, enabling analysis by the emotion engine.

[0694] Step 3:

[0695] The server analyzes the data received from the terminal and uses an emotion engine to estimate the user's emotional state. For example, it can determine stress or anxiety from input speed and touch patterns.

[0696] Step 4:

[0697] The server utilizes generative modeling to search for appropriate and reliable information from internet data sources based on user-specified labels. If necessary, it retrieves data from multiple sources with varying levels of reliability.

[0698] Step 5:

[0699] The data collected by the generative model is formatted by the data processing means. Based on the user's emotions analyzed by the emotion engine, the visual representation of the data is adjusted and transmitted to the terminal in the display format most suitable for the user.

[0700] Step 6:

[0701] The formatted data is displayed to the user on the device. The user reviews the visualized information and evaluates whether it meets their purpose. The user's reactions and further interactions are also sent back to the server as emotional feedback.

[0702] Step 7:

[0703] If a user wants to save data as an external file, they select the download option. The device sends this request to the server, which then uses a file generation tool to create a file in the specified format (e.g., Excel or CSV).

[0704] Step 8:

[0705] The server sends the generated files to the terminal, which the user downloads and uses for review and analysis in their local environment as needed. This entire process enables flexible and efficient information delivery that responds to emotional responses.

[0706] (Example 2)

[0707] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0708] Conventional information retrieval systems often present information without considering the user's emotional state, leading to situations where users find it difficult to understand. Furthermore, the reliability of the information provided is often a challenge, requiring users to verify it themselves, hindering efficient decision-making. Additionally, the limited functionality for adjusting format and style along with information provision makes providing flexible information to meet diverse user needs a significant challenge.

[0709] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0710] In this invention, the server includes a generation model means that receives information input by the user and searches a database based on that information; a data processing means that organizes the data collected by the generation model means and displays it in a format that can be visually provided to the user; a file generation means that converts the organized data into a file format that can be transmitted externally; and an emotion analysis means that analyzes the user's emotional state and optimizes the operation of the generation model means and the data processing means based on the analysis results. This enables the presentation of optimal information that matches the user's emotional state, the provision of highly reliable data, and flexible adjustment of the information format.

[0711] The "generative model means" is a function that performs a process of searching for data on the internet based on user input information and collecting the necessary information.

[0712] "Data processing means" refers to a function that formats the data collected by the generative model means and displays it in a format that can be visually provided to the user.

[0713] A "file generation method" is a function that converts formatted data into a file format that can be transmitted externally.

[0714] The "emotion analysis means" is a function that analyzes the user's emotional state and optimizes the operation of the generative model means and data processing means based on the analysis results.

[0715] This invention is a system for searching data on the internet based on user input information and presenting the information in the most optimal format. The specific configuration and operation of this system are described below.

[0716] The server has a generative model that receives information entered by the user using a terminal and uses that information to search a database. This model is designed to quickly and accurately collect necessary information from reliable sources worldwide. Specifically, it uses a data processing engine and AI-based algorithms to evaluate the reliability of the collected information and select the most relevant data.

[0717] Next, the server utilizes data processing tools to format the collected data. These tools adjust the data's arrangement and visual display to provide information in a user-friendly and easy-to-understand format. Examples include graphical representations of data and highlighting of key metrics.

[0718] Furthermore, the server incorporates an emotion analysis system that analyzes the user's emotional state. This function allows the system to infer the user's psychological state based on their input and interactions, and optimize the operation of the generative model and data processing systems in real time. This enables flexible information presentation tailored to the user's emotions, improving the user experience.

[0719] For example, if a user searches for "annual revenue of a company" using their device, sentiment analysis can determine that the user is experiencing stress. Based on this information, the server simplifies the presentation of the information through data processing, making it easier to understand. The information is then provided in a format that is easy for the user to comprehend, and if necessary, it is also available in a file format that can be downloaded externally.

[0720] An example of a prompt would be, "Please tell me the latest global trends in corporate annual revenues." Such prompts allow the system to search for and present appropriate information.

[0721] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0722] Step 1:

[0723] The user enters a search label into the terminal. The entered label is a keyword that points to specific information based on the user's request. This information is sent as input to the server.

[0724] Step 2:

[0725] The terminal sends the search label entered by the user to the server. Once this label reaches the server, the server prepares to begin the information retrieval process.

[0726] Step 3:

[0727] The server uses sentiment analysis tools to analyze the user's input behavior and infer the user's emotional state. This analysis considers input patterns and interaction speed, and the results are used as preprocessing for data retrieval.

[0728] Step 4:

[0729] The server operates a generative model and searches databases on the internet to collect the necessary information. This process ensures the accuracy of the information by collecting data from reliable sources and verifying data reliability. The collected information becomes the server's output.

[0730] Step 5:

[0731] The server formats the collected data using data processing tools. This formatting process ensures the data is presented visually in an easily understandable way, based on the results of sentiment analysis. For example, graphs and highlighting may be used. The formatted data is then output from the server and provided to the user.

[0732] Step 6:

[0733] Users review the formatted data provided from their devices and evaluate its reliability and relevance. This evaluation is performed to support data selection and decision-making.

[0734] Step 7:

[0735] The server incorporates user feedback into a feedback loop between sentiment analysis and data processing tools to optimize information presentation. This process continuously improves the user experience.

[0736] Step 8:

[0737] Users can download the necessary data in an external file format. This feature allows them to retrieve formatted and saved data for later review and analysis.

[0738] (Application Example 2)

[0739] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0740] In today's information society, users are required to efficiently obtain the data they need from vast amounts of information. However, simply presenting information is not enough; flexible information presentation tailored to the user's emotions and state of mind is essential for improving the user experience. Current systems struggle to provide information while considering user emotions, leading to stress and dissatisfaction.

[0741] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0742] In this invention, the server includes a generation model means that receives information input by the user and searches for data on the internet based on that information; a data processing means that formats the collected data and displays it in a presentable format; a file generation means that converts the formatted data into a file format that can be downloaded externally; an emotion analysis means that analyzes the user's emotions; and a means that dynamically adjusts the display style of the information based on the emotion analysis results. This makes it possible to provide information in a form that is appropriate to the user's emotional state, thereby reducing stress and promoting efficient decision-making.

[0743] "User input" refers to the input that a user sends to a system via a terminal in order to obtain specific data or services.

[0744] A "generative model means" is a mechanism for searching for and collecting relevant data from the internet based on user input information.

[0745] "Data processing means" refers to methods for formatting collected data into a user-friendly format and presenting it visually.

[0746] A "file generation method" is a mechanism that converts formatted data into a format that can be stored externally, allowing users to download it.

[0747] "Emotional analysis methods" refer to processes for inferring and analyzing a user's emotional state based on their input and actions.

[0748] "Dynamic adjustment methods" refer to methods of changing the style and format of information presented in response to the results of user sentiment analysis.

[0749] To realize this invention, a system centered on server, terminal, and user interaction will be constructed. The server will play a central role in performing data retrieval, data formatting, sentiment analysis, and dynamic adjustment of display styles.

[0750] The server receives information entered by the user via the terminal and collects relevant data from various reliable sources on the internet. This process utilizes generative modeling technology. Generative models leverage AI technology to efficiently extract necessary information from large amounts of data. They also evaluate the reliability of specified sources and select the most appropriate data based on their reliability.

[0751] Next, the server formats the collected data and converts it into a user-friendly format. This formatting process utilizes data processing tools. Furthermore, sentiment analysis tools are used to analyze the user's emotional state and dynamically adjust the information display style to match the user's psychological state. This allows information to be presented to the user in the most optimal way, reducing stress and confusion.

[0752] For example, if a user requests detailed information about a specific product, the server quickly collects reviews and specifications for that product. If the server determines from the user's facial expressions and voice that they are experiencing stress, it displays the information simply and intuitively. For sentiment analysis, the Affectiva SDK or Google Cloud AI's Sentiment Analysis API can be used. Furthermore, Python's Beautiful Soup and Pandas libraries are useful for specific data collection and formatting.

[0753] This system not only supports efficient decision-making but also improves the user experience, solving the challenges of conventional technologies. An example of a prompt message is as follows: "Collect the latest product reviews related to the '{product name}' entered by the user, and if the user is feeling anxious, extract and present reassuring points from the reviews." Using this prompt enables information delivery tailored to the user's emotions, creating a user-friendly environment.

[0754] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0755] Step 1:

[0756] The terminal receives user input information and sends it to the server. This input information includes data about specific product names or services. This allows the server to initiate the search process.

[0757] Step 2:

[0758] The server uses a generative AI model based on the received input information to search for data on the internet. The server uses AI algorithms to extract and collect relevant data from reliable sources. The collected data is in its raw, unprocessed state.

[0759] Step 3:

[0760] The server performs reliability assessments and selects information deemed highly reliable from the collected data. During this selection process, it utilizes the reliability score of the data source to filter the necessary data.

[0761] Step 4:

[0762] The server formats the selected data using data processing tools. Specifically, it uses the Python Pandas library to convert the data into a visually understandable format, such as tables or graphs. This formatted data then becomes the input data for the next process.

[0763] Step 5:

[0764] The emotion analysis system analyzes the user's emotional state from their input actions and interactions via the device. Using the Affectiva SDK, it collects and analyzes the user's emotional data and sends it to the server. This analysis result is used for adjustments in the next step.

[0765] Step 6:

[0766] The server adjusts the presentation style of formatted data based on sentiment analysis results. For example, if the user is experiencing stress, the information is simplified and visually reduced. This adjustment includes summarizing and highlighting data.

[0767] Step 7:

[0768] The server sends the processed data to the terminal, allowing the user to visually review it. The user can then make decisions based on this data and, if necessary, view more detailed information or download it in an external file format.

[0769] Through this process, the server utilizes generative AI models and the latest sentiment analysis technologies to deliver user-centric information.

[0770] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0771] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0772] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0773] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0774] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0775] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0776] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0777] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0778] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0779] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0780] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0781] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0782] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0783] 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.

[0784] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0785] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0786] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0787] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0788] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0789] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0790] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0791] The following is further disclosed regarding the embodiments described above.

[0792] (Claim 1)

[0793] A generative model means that receives information entered by a user and searches for data on the internet based on that information,

[0794] A data processing means that formats the data collected by the generation model means and displays it in a format that can be visually presented to the user,

[0795] A file generation means that converts the formatted data into a file format that can be downloaded externally,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, wherein the generation model means includes means for examining the reliability of a designated information source and selecting data based on its reliability.

[0799] (Claim 3)

[0800] The system according to claim 1, further comprising data conversion means for providing a data representation in a format specified by the user, and allowing adjustment of the arrangement and style of the data acquired by the generation model means.

[0801] "Example 1"

[0802] (Claim 1)

[0803] A generative model means that receives information entered by a user via an information input terminal and searches for data based on that information,

[0804] Information processing means that formats the information collected by the generation model means and presents it in a format that the user can visually confirm,

[0805] Information generation means for converting the formatted information into an information format that can be stored externally,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, further comprising means for evaluating a designated information source and selecting information based on that evaluation.

[0809] (Claim 3)

[0810] The system according to claim 1, further comprising information conversion means for providing information representation in a format specified by the user, and enabling adjustment of the arrangement and shape of the information acquired by the generation model means.

[0811] "Application Example 1"

[0812] (Claim 1)

[0813] Information generation means that receives information entered by the user and searches for data in the information source based on that information,

[0814] A data processing means that formats the data collected by the information generation means and displays it in a format that can be visually presented to the user,

[0815] A file generation means that converts the formatted data into a file format that can be output externally,

[0816] A logistics information management system that collects and dynamically displays inventory and delivery information based on information entered by the user,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein the information generation means includes means for examining the reliability of a designated information source and selecting data based on its reliability.

[0820] (Claim 3)

[0821] The system according to claim 1, further comprising a data conversion means that provides data representation in a format specified by the user, the arrangement and style of the data acquired by the information generation means can be adjusted, and logistics information can also be provided in a way that can be customized by the user.

[0822] "Example 2 of combining an emotion engine"

[0823] (Claim 1)

[0824] A generative model means that receives information entered by the user and searches a database based on that information,

[0825] A data processing means that organizes the data collected by the generation model means and displays it in a format that can be visually provided to the user,

[0826] A file generation means that converts the aforementioned organized data into a file format that can be transmitted externally,

[0827] An emotion analysis means that analyzes the user's emotional state and optimizes the operation of the generative model means and the data processing means based on the analysis results,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, wherein the generation model means includes means for examining the reliability of a designated information source and selecting information based on its reliability, and the sentiment analysis means provides information that is in harmony with the user.

[0831] (Claim 3)

[0832] The system according to claim 1, further comprising data conversion means for providing information representations based on a format specified by the user, allowing adjustment of the structure and appearance of the information acquired by the generative model means, and further optimizing the information visually and emotionally using feedback from the sentiment analysis means.

[0833] "Application example 2 when combining with an emotional engine"

[0834] (Claim 1)

[0835] A generative model means that receives information entered by a user and searches for data on the internet based on that information,

[0836] A data processing means that formats the data collected by the generation model means and displays it in a format that can be visually presented to the user,

[0837] A file generation means that converts the formatted data into a file format that can be downloaded externally,

[0838] A means of analyzing user emotions,

[0839] A means for dynamically adjusting the display style of information based on the analysis results of the aforementioned emotion analysis means,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, wherein the generation model means includes means for examining the reliability of a designated information source and selecting data based on its reliability.

[0843] (Claim 3)

[0844] The system according to claim 1, further comprising data conversion means for providing a data representation in a format specified by the user, and allowing adjustment of the arrangement and style of the data acquired by the generation model means. [Explanation of Symbols]

[0845] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A generative model means that receives information entered by a user and searches for data on the internet based on that information, A data processing means that formats the data collected by the generation model means and displays it in a format that can be visually presented to the user, A file generation means that converts the formatted data into a file format that can be downloaded externally, A system that includes this.

2. The system according to claim 1, wherein the generation model means includes means for examining the reliability of a designated information source and selecting data based on the reliability.

3. The system according to claim 1, further comprising data conversion means for providing a data representation in a format specified by the user, and allowing adjustment of the arrangement and style of the data acquired by the generation model means.

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