System

Abstract

To provide a system.SOLUTION: Provided is a system comprising: means for accumulating, in a knowledge database, highly accurate results generated by a user via a knowledge generation platform; means for providing the user with the knowledge accumulated by the accumulating means in an ecosystem manner; means for improving convenience and business efficiency; means in which the user uses search engines and interactive agents to acquire information through the providing means; means for transmitting information acquired by a terminal to the knowledge generation platform; means in which a server analyzes information on the knowledge generation platform; means for accumulating, in the knowledge database, the information analyzed by the server with the analyzing means; and means in which the user reuses the information in the knowledge database.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There is an issue that the high-precision results generated by conventional search and chatbots are stored in a distributed manner, making it difficult to utilize the results efficiently. [Means for solving the problem]

[0005] To solve this problem, we provide a system that accumulates highly accurate results generated by users in a knowledge database via a knowledge generation platform and provides the accumulated knowledge to users in the form of an ecosystem, thereby improving convenience and operational efficiency. [Brief explanation of the drawings]

[0006] [Figure 1]1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The present invention is a system that stores highly accurate results generated by users using conventional search and chatbots in a knowledge database via a knowledge generation platform. Specifically, users send information obtained through search and chatbots to the knowledge generation platform, which stores the information in the knowledge database. This knowledge database stores knowledge in various fields and provides that knowledge to users in the form of an ecosystem.

[0029] "Example 2"

[0030] As a specific example, when a user searches for medical information, the search results are sent to the knowledge generation platform, and that information is stored in the knowledge database. If another user subsequently needs similar information, the relevant information is retrieved from the knowledge database and provided to the user. This allows users to efficiently obtain the information they need.

[0031] "Example 3"

[0032] Furthermore, the present invention is a system that aims to improve convenience and business efficiency. Specifically, by providing information stored in a knowledge database at the time the user needs it, the system reduces the time the user spends searching for information and improves business efficiency.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The user generates information using traditional search or chatbots.

[0036] Step 2: Send the generated information to the knowledge generation platform.

[0037] Step 3: The knowledge generation platform stores the information in the knowledge database.

[0038] "Example 2"

[0039] Step 1: The user conducts a medical information search.

[0040] Step 2: Submit the search results to the knowledge generation platform.

[0041] Step 3: The knowledge generation platform stores the information in the knowledge database.

[0042] Step 4: When another user needs similar information, the relevant information is retrieved from the knowledge database and provided to the user.

[0043] "Example 3"

[0044] Step 1: The user searches for the information they need.

[0045] Step 2: Obtain the relevant information from the knowledge database.

[0046] Step 3: Provide the acquired information to the user.

[0047] Example 1

[0048] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0049] The information obtained using conventional search engines and interactive agents is temporary and difficult to reuse. Furthermore, there is a lack of means to guarantee the accuracy and reliability of the information obtained, making it difficult for users to efficiently obtain highly accurate information. Furthermore, there is a need for a method to improve user convenience and enhance work efficiency by providing accumulated knowledge as an ecosystem.

[0050] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0051] In this invention, the server includes a means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, a means for providing the stored knowledge to users in the form of an ecosystem, and a means for improving convenience and business efficiency, thereby enabling the efficient storage and reuse of high-precision information obtained by users using search engines and interactive agents.

[0052] "User" means an individual or entity that uses the system to obtain information and submit the generated results to the knowledge generation platform.

[0053] "High-precision results" are accurate and reliable information obtained by users using search engines or interactive agents.

[0054] The "knowledge generation platform" is a system that analyzes information sent by users, converts it into an appropriate format, and stores it in a knowledge database.

[0055] A "knowledge database" is a database system that accumulates analyzed information and stores it in a form that can be reused later.

[0056] An "ecosystem" is an entire system that provides accumulated knowledge to users and enables them to efficiently obtain information.

[0057] A "search engine" is software that searches the Internet for relevant information based on keywords entered by a user and provides the results.

[0058] An "interactive agent" is software that responds to users' questions in natural language, and is also called a chatbot.

[0059] "Terminal" refers to a device that a user uses to access a search engine or interactive agent, including a PC or smartphone.

[0060] A "server" is a computer system that operates a knowledge generation platform and knowledge database, and analyzes and stores information.

[0061] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is used to understand the meaning of information and convert it into an appropriate format.

[0062] The "HTTPS protocol" is a communication protocol for securely sending and receiving data over the Internet.

[0063] This invention is a system that accumulates high-precision results generated by users in a knowledge database via a knowledge generation platform and provides the accumulated knowledge to users in the form of an ecosystem. Specific embodiments of this system are described below.

[0064] System configuration

[0065] Hardware

[0066] Devices: Devices such as PCs, smartphones, and tablets that allow users to access search engines and interactive agents.

[0067] Server: A computer system for operating the knowledge generation platform and knowledge database, equipped with a high-performance processor and large-capacity storage.

[0068] software

[0069] Search engine: Software that searches the Internet for relevant information based on keywords entered by the user and provides results.

[0070] Conversational Agent: Software that responds to user questions in natural language, using generative AI models to generate appropriate answers to the questions.

[0071] Knowledge generation platform: A system that analyzes information sent by users, converts it into an appropriate format, and stores it in a knowledge database. It uses natural language processing technologies such as Google® Cloud Natural Language API and IBM Watson® Natural Language Understanding.

[0072] Knowledge database: A database system that accumulates analyzed information and stores it in a form that can be reused later. It uses a database management system such as MySQL (registered trademark) or PostgreSQL.

[0073] Data processing and calculation

[0074] Acquisition and transmission of information

[0075] A user uses a device to access a search engine or conversational agent to obtain information. For example, the user asks the conversational agent, "Tell me about the latest AI technology." The conversational agent uses a generative AI model to generate an appropriate answer to the question. If this answer is determined to be highly accurate, the device sends this information to the knowledge generation platform. Transmission is via the HTTPS protocol.

[0076] Analysis and accumulation of information

[0077] The server analyzes the information received by the knowledge generation platform. This analysis uses natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding. The server understands the meaning of the information and converts it into an appropriate format. The analyzed information is then stored in a knowledge database.

[0078] Reuse of information

[0079] When a user retrieves information using a search engine or a conversational agent again, the accumulated information from the knowledge database is reused. For example, if another user asks, "Tell me about the latest AI technology," the conversational agent will retrieve information from the knowledge database, such as, "This includes generative AI models and deep learning," and respond.

[0080] Examples of concrete examples and prompts

[0081] As a concrete example, consider the case where a user asks a conversational agent, "Tell me about the latest AI technologies." The conversational agent responds, "The latest AI technologies include generative AI models and deep learning." If this answer is deemed highly accurate, the device sends this information to the knowledge generation platform. The server analyzes the information using the Google Cloud Natural Language API and stores it in a MySQL database. This information can be reused when other users search for "the latest AI technologies."

[0082] Examples of prompts to input to a generative AI model might include:

[0083] "Tell me about the latest AI technology"

[0084] By inputting this prompt sentence into the interactive agent, the user can obtain highly accurate information, which is then stored in a knowledge database.

[0085] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0086] Step 1:

[0087] Users retrieve information using search engines or interactive agents.

[0088] Specifically, the user uses a terminal to ask the conversational agent, "Tell me about the latest AI technology."

[0089] Input: User question (prompt)

[0090] Output: Answer from the conversational agent (answer from the generative AI model)

[0091] Step 2:

[0092] The terminal transmits the acquired information to the knowledge generation platform.

[0093] Specifically, the terminal sends the answer it receives from the conversational agent, "Modern AI technologies include generative AI models and deep learning," to the knowledge generation platform using the HTTPS protocol.

[0094] Input: Answer from the conversational agent

[0095] Output: Data sent to the knowledge generation platform

[0096] Step 3:

[0097] The server analyzes the information on the knowledge generation platform.

[0098] Specifically, the server uses the Google Cloud Natural Language API to analyze the received information, understand the meaning of the information, and convert it into an appropriate format.

[0099] Input: Data sent to the knowledge generation platform

[0100] Output: Parsed information (data converted into the appropriate format)

[0101] Step 4:

[0102] The server stores the analyzed information in a knowledge database.

[0103] Specifically, the server inserts the parsed information into a MySQL database for later reuse.

[0104] Input: Parsed information

[0105] Output: Data stored in the knowledge database

[0106] Step 5:

[0107] Users reuse information from the knowledge database.

[0108] Specifically, when another user asks the conversational agent, "Tell me about the latest AI technology," the conversational agent retrieves information from the knowledge database that says, "This includes generative AI models and deep learning," and responds.

[0109] Input: User question (prompt)

[0110] Output: Information retrieved from the knowledge database (answer by the conversational agent)

[0111] (Application example 1)

[0112] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0113] In conventional factory work, it was difficult for workers to quickly obtain the information they needed, resulting in a decline in work efficiency. Also, a lot of time was often spent troubleshooting and learning optimal work procedures, resulting in a decline in overall productivity. Furthermore, while automation using robots is progressing, there was also the problem of insufficient cooperation with workers, making efficient operation difficult.

[0114] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0115] In this invention, the server includes a means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, a means for providing the stored knowledge to users in the form of an ecosystem, a means for improving convenience and work efficiency, a means for providing a knowledge-sharing robot assistant to improve work efficiency within the factory, a means for displaying information to workers in real time via smart devices, and a means for the robot to transport necessary parts according to the worker's instructions and automate simple tasks. This allows workers to quickly obtain the information they need and efficiently troubleshoot and optimize work procedures. It also strengthens collaboration with the robot, improving overall productivity.

[0116] "User" refers to a person who uses the system to search for information or acquire knowledge.

[0117] "High-precision results" refers to accurate and reliable information obtained through search and chatbots.

[0118] A "knowledge generation platform" refers to a system that collects information obtained by users and stores it in a knowledge database.

[0119] "Knowledge DB" refers to a database that accumulates knowledge from various fields and provides it to users in the form of an ecosystem.

[0120] An "ecosystem" refers to a system in which information accumulated in a knowledge database is provided to users and mutually utilized.

[0121] "Improving convenience" means enabling users to quickly and easily obtain the information they need.

[0122] "Improving business efficiency" refers to increasing work efficiency and improving productivity.

[0123] A "knowledge-sharing robot assistant" is a robot that retrieves information from a knowledge database and provides it to workers in order to improve work efficiency within the factory.

[0124] "Smart device" refers to devices for displaying information, such as smartphones, smart glasses, and head-mounted displays.

[0125] "Displaying information in real time" means displaying the necessary information instantly so that workers can use it on the spot.

[0126] A "robot" refers to a machine that transports necessary parts according to the instructions of a worker or automates simple tasks.

[0127] To implement the present invention, the following system configuration and program are required.

[0128] System Configuration

[0129] 1. Hardware

[0130] Smart devices: smartphones, smart glasses, head-mounted displays (e.g., Google Glass (registered trademark), Microsoft (registered trademark) HoloLens (registered trademark))

[0131] Factory robots: Robots that transport parts and automate simple tasks (e.g., Universal Robots UR series)

[0132] Server: Server for hosting the knowledge generation platform and knowledge DB

[0133] 2. Software

[0134] API server: Server software (e.g., Flask) for providing the functionality of the knowledge generation platform

[0135] Database: Database software that functions as a knowledge database (e.g., MongoDB)

[0136] Program processing

[0137] The server executes a program that includes the following means:

[0138] 1. Knowledge Generation Platform

[0139] Users use searches and chatbots via their smart devices, and the highly accurate results obtained are sent to a knowledge generation platform.

[0140] The knowledge generation platform stores the received information in a knowledge database.

[0141] 2. Knowledge DB

[0142] The knowledge database accumulates knowledge in various fields and provides it to users in the form of an ecosystem.

[0143] When a user requests the information they need, the most appropriate information is retrieved from the knowledge database and displayed in real time on the smart device.

[0144] 3. Knowledge-Sharing Robot Assistant

[0145] To improve work efficiency within the factory, information is retrieved from the knowledge database and provided to workers.

[0146] The robot follows the worker's instructions to transport the necessary parts and automate simple tasks.

[0147] Specific examples

[0148] For example, when a worker searches for "the best welding technique," the knowledge generation platform stores that information in the knowledge database. Next, when another worker needs the same information, the information about the best welding technique is retrieved from the knowledge database and displayed in real time on the smart glasses.

[0149] If troubleshooting is required, the worker can search for "how to solve error code 123," and the solution will be retrieved from the knowledge database and displayed on the head-mounted display.The robot will then transport the necessary parts according to the worker's instructions, automating the work.

[0150] Prompt Sentence Examples

[0151] "What is the best welding technique?"

[0152] How do I resolve error code 123?

[0153] This allows workers to quickly obtain the information they need, allowing them to efficiently troubleshoot and optimize work procedures. It also strengthens collaboration with robots, improving overall productivity.

[0154] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0155] Step 1:

[0156] A user uses a smart device to input information through a search query or a chatbot. The input query is sent to a knowledge generation platform. The input is text data about the information the user wants to know or the problem they want to solve. The output is the query data sent to the knowledge generation platform.

[0157] Step 2:

[0158] The server processes the query data received through the knowledge generation platform and generates relevant high-precision results. This process involves using a generative AI model to generate optimal answers to the queries. The input is the query data submitted by the user, and the output is the generated high-precision results.

[0159] Step 3:

[0160] The server stores the generated high-precision results in a knowledge database. The input here is the generated high-precision results, and the output is the data stored in the knowledge database. Data processing involves converting the result data into an appropriate format and saving it in the database.

[0161] Step 4:

[0162] When the user needs the information again, he / she sends a request to the knowledge DB via his / her smart device. The input is the request data related to the information the user wants to know, and the output is the request data sent to the knowledge DB.

[0163] Step 5:

[0164] The server retrieves information corresponding to the request from the knowledge database. The input is the request data from the user, and the output is the related information retrieved from the knowledge database. Data calculation involves searching the database based on the request and extracting the most appropriate information.

[0165] Step 6:

[0166] The server sends the acquired information to the smart device and displays it to the user in real time. The input is information acquired from the knowledge database, and the output is information displayed on the smart device. Specifically, the server converts the information into an appropriate format and displays it on the smart device's display.

[0167] Step 7:

[0168] When a user issues an instruction to a robot, the instruction is sent to the robot through a smart device. The input is instruction data from the user, and the output is instruction data sent to the robot.

[0169] Step 8:

[0170] The robot transports necessary parts and automates simple tasks based on received instructions. The input is instruction data from the user, and the output is the results of the performed work. Specifically, the robot operates according to the instructions and performs the specified task.

[0171] This allows users to quickly obtain the information they need, allowing them to efficiently troubleshoot and find the optimal work procedure. It also strengthens collaboration with the robot, improving overall productivity.

[0172] Example 2

[0173] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0174] Conventional medical information search systems make it difficult for users to efficiently obtain the information they need. Furthermore, when multiple users search for the same information, they have to repeat the same search process each time, which is time-consuming and labor-intensive. Furthermore, there is a lack of a way to effectively utilize accumulated knowledge, and improvements in convenience and operational efficiency are needed.

[0175] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0176] In this invention, the server includes: means for accumulating high-precision results generated by users in a knowledge database via a knowledge generation platform; means for providing the accumulated knowledge to users in the form of an ecosystem; means for improving convenience and work efficiency; means for users to search for medical information and send the search results to the knowledge generation platform; and means for retrieving the relevant information from the knowledge database and providing it to another user when another user needs similar information. This allows users to efficiently obtain the medical information they need, reducing the time and effort required when multiple users search for the same information. Furthermore, by effectively utilizing the accumulated knowledge, convenience and work efficiency can be improved.

[0177] "User" means an individual or organization that uses the System to retrieve medical information.

[0178] "High-precision results" are accurate and reliable information that users receive when they search.

[0179] A "knowledge generation platform" is a system for processing search results and storing them in a knowledge database.

[0180] A "knowledge database" is a database system for storing and managing accumulated medical information and other knowledge.

[0181] An "ecosystem" is an overall system environment that provides accumulated knowledge to users and allows them to mutually utilize it.

[0182] "Improving convenience" means enabling users to quickly and easily obtain the information they need.

[0183] "Improved business efficiency" refers to the ability to increase business efficiency and reduce time and effort by using the system.

[0184] "Medical information" refers to all information related to medical care, such as disease treatment methods, symptoms, and drug information.

[0185] "Search results" are a list of information obtained when a user performs a search.

[0186] "Send" refers to the act of sending data from a terminal to a server.

[0187] "Retrieval" refers to the act of the server retrieving the necessary information from the knowledge database.

[0188] "Providing" refers to the act of displaying the information acquired by the server to the user.

[0189] The present invention is a system for enabling a user to efficiently obtain medical information. A specific embodiment of this system will be described below.

[0190] System configuration

[0191] The system mainly consists of the following components:

[0192] User's device (PC, smartphone, etc.)

[0193] server

[0194] Knowledge Generation Platform

[0195] Knowledge Database

[0196] Hardware and software used

[0197] Device: A device such as a PC or smartphone. Searches are performed using a web browser (e.g., GOOGLE CHROME (registered trademark), Mozilla Firefox).

[0198] Server: Receives and processes search queries. Uses a web server (e.g., Apache HTTP Server, Nginx) and a database management system (e.g., MySQL, PostgreSQL).

[0199] Knowledge generation platform: Processes search results and stores them in a knowledge database. Uses a data streaming platform (e.g., Apache Kafka).

[0200] Knowledge database: Stores and manages accumulated medical information. Uses a database management system (e.g., MySQL, PostgreSQL).

[0201] System Operation

[0202] 1. A user searches for medical information

[0203] Users open a web browser on their PC or smartphone and access a search engine or dedicated medical information search site.

[0204] Enter a keyword such as "diabetes treatment" in the search box and click the search button.

[0205] 2. The device sends the search results to the server

[0206] The terminal transmits the generated search query to the server as an HTTP request.

[0207] Example: A terminal sends the request "GET / search?q=diabetes treatment HTTP / 1.1" to a server.

[0208] 3. The server processes the data in the knowledge generation platform

[0209] The server analyzes the received search query and collects relevant medical information.

[0210] The server transmits the collected information to the knowledge generation platform.

[0211] Example: A server collects information on "diabetes treatment" and sends it to a knowledge generation platform.

[0212] 4. The server stores the data in a knowledge database

[0213] The knowledge generation platform stores the received information in a knowledge database.

[0214] Example: A knowledge database stores information about "treatments for diabetes."

[0215] 5. Another user searches for similar information

[0216] Another user similarly searches for medical information using a PC or smartphone.

[0217] Enter "diabetes cure" in the search box and click the search button.

[0218] 6. The server retrieves the relevant information from the knowledge database and provides it to the user.

[0219] The server searches and acquires the relevant information from the knowledge database.

[0220] The server sends the acquired information to the user's terminal as an HTTP response.

[0221] Example: The server retrieves information about "diabetes treatment" from a knowledge database and displays it to the user.

[0222] Examples of concrete examples and prompts

[0223] Specific examples

[0224] User A searches for "treatment of high blood pressure" and the results are stored in the knowledge database.

[0225] When user B searches for "treatment of high blood pressure" at a later date, information is retrieved from the knowledge database and provided to user B.

[0226] Prompt Sentence Examples

[0227] "What's the latest information on treatments for high blood pressure?"

[0228] "Please provide detailed information on diabetes treatment."

[0229] This system allows users to efficiently obtain the medical information they need. It also reduces the time and effort required when multiple users search for the same information. By effectively utilizing the accumulated knowledge, convenience and work efficiency can be improved.

[0230] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0231] Step 1:

[0232] Users search for medical information

[0233] Input: A user uses a PC or smartphone, opens a web browser, and accesses a search engine or dedicated medical information search site. They enter a keyword such as "diabetes treatment" in the search box and click the search button.

[0234] Data processing: A search query is generated.

[0235] Output: The generated search query is saved on the device.

[0236] Specific operation: A user types "diabetes cure" and clicks the search button, generating a search query.

[0237] Step 2:

[0238] The device sends the search results to the server

[0239] Input: The generated search query.

[0240] Data processing: The device sends the search query to the server as an HTTP request.

[0241] Output: The server receives the search query.

[0242] Specific operation: The terminal sends the request "GET / search?q=diabetes treatment HTTP / 1.1" to the server.

[0243] Step 3:

[0244] The server processes the data in the knowledge generation platform

[0245] Input: The search query received by the server.

[0246] Data processing: The server analyzes the search query and collects relevant medical information, which is then sent to the knowledge generation platform.

[0247] Output: The knowledge generation platform receives the information.

[0248] Specific operation: The server collects information on "diabetes treatment methods" and sends it to the knowledge generation platform.

[0249] Step 4:

[0250] The server stores the data in a knowledge database

[0251] Input: Information received by the knowledge generation platform.

[0252] Data processing: The knowledge generation platform stores the received information in a knowledge database.

[0253] Output: Information is stored in a knowledge database.

[0254] Specific operation: Information about "treatments for diabetes" is stored in the knowledge database.

[0255] Step 5:

[0256] Another user searches for similar information

[0257] Input: Another user uses a PC or smartphone, opens a web browser, accesses a search engine or dedicated medical information search site, types "diabetes treatment" in the search box, and clicks the search button.

[0258] Data processing: A search query is generated.

[0259] Output: The generated search query is saved on the device.

[0260] Specific behavior: Another user types "diabetes cure" and clicks the search button, generating a search query.

[0261] Step 6:

[0262] The server retrieves the relevant information from the knowledge database and provides it to the user.

[0263] Input: The search query received by the server.

[0264] Data processing: The server searches for and retrieves the relevant information from the knowledge database, and sends the retrieved information to the user's device as an HTTP response.

[0265] Output: Information is displayed on the user's terminal.

[0266] Specific operation: The server retrieves information about "diabetes treatment" from the knowledge database and displays it to the user.

[0267] (Application example 2)

[0268] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0269] In modern brick-and-mortar stores, it is difficult for users to efficiently obtain product information. In particular, when users want to know detailed information or stock status about a specific product, they must inquire with a store clerk, which takes time and effort. Furthermore, when multiple users request the same information, duplicate information acquisition occurs, which is inefficient. Furthermore, conventional search systems and chatbots have difficulty providing information in real time, which reduces user convenience.

[0270] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the accumulated knowledge to users in the form of an ecosystem, means for improving convenience and business efficiency, and means for providing product information in real time using smart devices in physical stores. This allows users to improve their shopping experience in physical stores and obtain the product information they need quickly and efficiently.

[0271] "User" means any person or entity that uses the System to search for and retrieve information.

[0272] "Highly Accurate Results" are search results that provide accurate and relevant information in response to a user's search query.

[0273] A "knowledge generation platform" is a system for collecting information generated by users and storing it in a knowledge database.

[0274] A "knowledge database" is a database that stores collected information and provides it to users as needed.

[0275] An "ecosystem" is an integrated system environment for providing users with information stored in a knowledge database.

[0276] "Improving convenience" means enabling users to obtain the information they need quickly and efficiently.

[0277] "Improving business efficiency" refers to preventing duplicate information acquisition and increasing the efficiency of the entire system.

[0278] A "brick and mortar store" is a store that sells products in a physical location.

[0279] A "smart device" is an electronic device that is connected to the Internet and can display and operate information.

[0280] "Real-time" refers to information being obtained and provided immediately.

[0281] The present invention relates to a system for providing real-time product information using smart devices in a physical store. Specific embodiments of this system are described below.

[0282] System Configuration

[0283] The system consists of the following main components:

[0284] 1. Knowledge generation platform: Collects high-precision results generated by users and stores them in a knowledge database.

[0285] 2. Knowledge database: Stores accumulated information and makes it available when needed.

[0286] 3. Smart Devices: Devices used by customers in physical stores (e.g., smart glasses).

[0287] 4. Ecosystem: An integrated system that provides users with information stored in a knowledge database.

[0288] Program processing

[0289] The server accumulates the highly accurate results generated by users in a knowledge database via a knowledge generation platform. Specifically, when a user searches for product information using a smart device, the search results are sent to the knowledge generation platform. The knowledge generation platform analyzes the search results and stores them in the knowledge database.

[0290] Next, when another user needs information about the same product, they access the knowledge database through their smart device to retrieve the relevant information, which is then displayed in real time on the smart device's display.

[0291] Hardware and software used

[0292] Hardware: Smart glasses, server

[0293] Software: Knowledge generation platform, knowledge database, API

[0294] Specific examples

[0295] For example, if a user is searching for "medical masks" in a physical store, the user wearing smart glasses can search for "medical masks" by voice, and the smart glasses' display will show detailed information about the masks and their stock availability. This information is retrieved in real time from the knowledge database.

[0296] Prompt Sentence Examples

[0297] "Search for information on medical masks."

[0298] "Get medical mask information from our knowledge database."

[0299] "Display medical mask information on smart glasses."

[0300] In this way, users can improve their shopping experience in physical stores and obtain the product information they need quickly and efficiently.

[0301] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0302] Step 1:

[0303] A user searches for product information using a smart device (e.g., smart glasses). The user inputs a search query through voice input or touch operation. The input query is sent from the smart device to a server. The input data is the search query, and the output data is the search request sent to the server.

[0304] Step 2:

[0305] The server sends the received search query to the knowledge generation platform. The knowledge generation platform analyzes the search query and generates relevant high-precision results. The input data is the search query, and the output data is the high-precision results. In particular, the knowledge generation platform analyzes the search query and retrieves relevant information from the database.

[0306] Step 3:

[0307] The knowledge generation platform stores the generated high-precision results in a knowledge database. The input data is the high-precision results, and the output data is the information stored in the knowledge database. In particular, the knowledge generation platform writes the high-precision results into the database.

[0308] Step 4:

[0309] If another user wants to search for information about the same product, they will use their smart device to enter a search query again. The entered query will be sent from the smart device to the server. The input data is the search query, and the output data is the search request sent to the server.

[0310] Step 5:

[0311] The server accesses the knowledge database to retrieve relevant information. The input data is a search query, and the output data is the information retrieved from the knowledge database. Specifically, the server sends a query to the knowledge database to retrieve relevant information.

[0312] Step 6:

[0313] The server sends the acquired information to the smart device. The input data is the information acquired from the knowledge database, and the output data is the information sent to the smart device. Specifically, the server transfers the acquired information to the smart device.

[0314] Step 7:

[0315] The smart device displays the received information to the user. The input data is the information sent from the server, and the output data is the information displayed on the smart device's display. Specifically, the smart device displays the received information on the display.

[0316] In this way, users can improve their shopping experience in physical stores and obtain the product information they need quickly and efficiently.

[0317] Example 3

[0318] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0319] With conventional information search systems, it was difficult for users to quickly obtain the information they needed, and information searches could take a long time. In addition, search results often did not necessarily meet the user's needs, which led to problems with the inability to improve work efficiency. Furthermore, conventional systems lacked a means to effectively utilize accumulated knowledge, making it difficult to reuse knowledge.

[0320] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0321] In this invention, the server includes means for accumulating high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the accumulated knowledge to users in the form of an ecosystem, means for receiving and analyzing queries sent by users from their terminals, means for searching for related information from the knowledge database based on the analyzed queries, means for inputting the search results into a generative AI model as prompt sentences to generate appropriate answers, and means for providing the generated answers to users. This allows users to quickly obtain the information they need, shortening information search time and improving work efficiency.

[0322] "User" means an individual or organization that uses the system to search for information and obtain the data they need.

[0323] "High-accuracy results" are search results that provide accurate and relevant information in response to a user's query.

[0324] A "knowledge generation platform" is a system for collecting information generated by users and storing it in a knowledge database.

[0325] A "knowledge database" is a database that stores accumulated information and knowledge and can be searched and retrieved as needed.

[0326] An "ecosystem" is the overall structure of a system that provides accumulated knowledge to users and promotes the reuse and sharing of information.

[0327] A "terminal" is a device (e.g., a PC or smartphone) through which a user inputs a query and communicates with the server.

[0328] A "query" is a question or request that a user enters into a system to retrieve information.

[0329] A "server" is a computer system that receives and analyzes queries, retrieves information from a knowledge database, and generates answers using generative AI models.

[0330] A "generative AI model" is an artificial intelligence model that generates appropriate answers in natural language based on an input prompt sentence.

[0331] A "prompt sentence" is an instruction sentence input to a generative AI model, and is the sentence that serves as the basis for the model to generate an appropriate answer.

[0332] MODE FOR CARRYING OUT THE INVENTION

[0333] The present invention is a system for enabling users to quickly obtain the information they need and improving work efficiency. A specific embodiment of this system will be described below.

[0334] System configuration

[0335] Hardware

[0336] Server: High-performance server (e.g. general-purpose server)

[0337] Device: The device used by the user (e.g., computer, smartphone)

[0338] software

[0339] Database Management System: Software for managing knowledge databases (e.g., MySQL)

[0340] Generative AI model: An artificial intelligence model that performs natural language processing (e.g., a general-purpose generative AI model)

[0341] Program processing

[0342] The server receives and analyzes queries sent from the device. Based on the analyzed queries, the server searches for relevant information from a knowledge database. The search results are input as prompt sentences into the generative AI model, which generates an appropriate answer. The generated answer is sent from the server to the device and provided to the user.

[0343] Specific examples

[0344] For example, consider the case where a user enters the query "Tell me about the latest project management tools."

[0345] 1. Query reception: A user sends a query from their device saying, "Please tell me about the latest project management tools."

[0346] 2. Query analysis: The server analyzes the query and searches the knowledge database for information about "project management tools."

[0347] 3. Information retrieval: The server uses MySQL to retrieve relevant information from the knowledge database.

[0348] 4. Use of generative AI model: The server inputs the acquired information into the generative AI model and formats the information in a form that is easy for the user to understand.

[0349] 5. Information provision: The server sends the formatted information to the user's terminal.

[0350] An example of a prompt to be input to the generative AI model would be, "Please provide information about the latest project management tools. Please use the following data as a basis," followed by information retrieved from the knowledge database.

[0351] In this way, the user can quickly obtain the necessary information, shortening the time required for searching information and improving work efficiency. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0352] Step 1:

[0353] The user enters a query.

[0354] The user inputs the information they want to obtain from their device (e.g., a PC or smartphone). For example, they might input, "Tell me about the latest project management tools." The input query is saved in an input field on the device.

[0355] Step 2:

[0356] The device sends a query to the server.

[0357] The terminal sends the query entered by the user to the server as an HTTP request, and data is sent over the Internet. The input is the user's query, and the output is an HTTP request to the server.

[0358] Step 3:

[0359] The server receives and parses the query.

[0360] The server receives the HTTP request sent from the terminal. It analyzes the received query and identifies the type of information the user is seeking. The input is the HTTP request, and the output is the analyzed query. Specifically, it recognizes the query content as information about the "project management tool."

[0361] Step 4:

[0362] The server retrieves relevant information from a knowledge database.

[0363] The server searches for relevant information from a knowledge database based on the analysis results. For example, it uses a database management system (e.g., MySQL) to retrieve the latest information on "project management tools." The input is the analyzed query, and the output is the search results. Specifically, it executes the query "SELECT FROM knowledge_db WHERE topic='project management tools' ORDER BY date DESC LIMIT 1."

[0364] Step 5:

[0365] The server inputs a prompt sentence into the generative AI model.

[0366] The server inputs a prompt to the generative AI model based on information obtained from the knowledge database. An example of a prompt is, "Please provide information about the latest project management tools. Please use the following data as a basis." The input is the search results, and the output is a prompt to the generative AI model.

[0367] Step 6:

[0368] A generative AI model generates the appropriate answer.

[0369] The generative AI model generates an appropriate answer based on the input prompt. The generated answer is expressed in natural language that is easy for users to understand. The input is the prompt, and the output is the generated answer. Specifically, it generates an answer such as, "Modern project management tools help with task management, project progress visualization, and team collaboration."

[0370] Step 7:

[0371] The server sends the generated answer to the user.

[0372] The server sends the answer obtained from the generative AI model to the user's device. At this time, data is sent over the Internet. The input is the generated answer, and the output is an HTTP response to the user's device. Specifically, the answer is sent in JSON format.

[0373] Step 8:

[0374] The user receives the answer.

[0375] The user receives the response from the server on their device and checks the displayed answer. This allows the user to quickly obtain the information they need. The input is the HTTP response from the server, and the output is the answer displayed on the user's device.

[0376] (Application example 3)

[0377] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0378] With conventional systems, it was difficult for users to quickly obtain the information they needed, and there were issues with time-consuming information searches, particularly in the maintenance and troubleshooting of factory robots, which reduced work efficiency.In addition, there was a lack of means to provide appropriate information for error codes and maintenance requests, making it difficult to respond quickly.

[0379] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0380] In this invention, the server includes means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the stored knowledge to users in the form of an ecosystem, means for improving convenience and work efficiency, and means for the robot to detect error codes and maintenance requests and search for and provide related information from the knowledge database. This enables users to quickly obtain the information they need, enabling quick and appropriate responses to factory robot maintenance and troubleshooting.

[0381] "User" refers to an individual or organization that uses the system to obtain information and perform business.

[0382] "High accuracy results" refers to user-generated information and data that is accurate and reliable.

[0383] A "knowledge generation platform" refers to a system or tool that collects information generated by users and stores it in a knowledge database.

[0384] A "knowledge database" refers to a database that systematically stores accumulated information and knowledge and allows it to be searched and provided as needed.

[0385] An "ecosystem" refers to an integrated system or environment for providing users with information stored in a knowledge database.

[0386] "Improving convenience" refers to increasing the ease and comfort of use for users when using the system.

[0387] "Improving business efficiency" refers to reducing the time and effort required to carry out business operations and increasing productivity.

[0388] A "robot" refers to an automated mechanical device used in factories and other places.

[0389] An "error code" refers to an identification number or string of characters that is displayed when a robot or system detects an abnormality.

[0390] "Maintenance Request" refers to a notification or instruction issued when a robot or system requires routine maintenance or repair.

[0391] "Related information" refers to specific measures and procedures that address error codes and maintenance requests.

[0392] As an embodiment of the present invention, a "smart maintenance assistant" system installed in a factory robot will be described as an example.

[0393] System Program

[0394] The system consists of the following main components:

[0395] 1. Knowledge Generation Platform

[0396] 2. Knowledge Database

[0397] 3. Ecosystem

[0398] 4. Robot error detection and maintenance request detection function

[0399] Program processing explanation

[0400] Hardware used

[0401] Factory robots (e.g., general industrial robots)

[0402] Robot-mounted computer

[0403] Software used

[0404] Python Program

[0405] Knowledge database (database management system)

[0406] Data processing and calculation

[0407] 1. Knowledge Generation Platform

[0408] The server accumulates the high-precision results generated by users in a knowledge database via a knowledge generation platform, which allows the information generated by users to be systematically stored.

[0409] 2. Knowledge Database

[0410] The server provides users with the accumulated knowledge in the form of an ecosystem. The knowledge database includes maintenance information and troubleshooting guides.

[0411] 3. Ecosystem

[0412] Through the ecosystem, the server quickly provides the information users need, thereby improving convenience and operational efficiency.

[0413] 4. Robot error detection and maintenance request detection function

[0414] The robot detects error codes and maintenance requests and provides relevant information from a knowledge database, enabling fast and accurate robot maintenance and troubleshooting.

[0415] Specific examples

[0416] For example, if the robot detects "error_101," it retrieves the information "Please check the belt tension" from the knowledge database and displays it on the robot's display.

[0417] Prompt Sentence Examples

[0418] "When the robot detects an error code, retrieve the maintenance information corresponding to that error code from the knowledge database and display it."

[0419] The above is an embodiment of the present invention. This system allows users to quickly obtain the necessary information, enabling quick and appropriate responses in maintenance and troubleshooting of factory robots.

[0420] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0421] Step 1:

[0422] The server accumulates the high-precision results generated by users in a knowledge database via a knowledge generation platform.

[0423] Input: User-generated high-precision results

[0424] Data processing: High-precision results are formalized in the knowledge generation platform and stored in the knowledge database.

[0425] Output: High-precision results stored in a knowledge database

[0426] Specific operation: The server receives the data entered by the user and stores it in the database in the appropriate format.

[0427] Step 2:

[0428] The server provides the accumulated knowledge to users in the form of an ecosystem.

[0429] Input: Information stored in the knowledge database

[0430] Data processing: Search for necessary information from the knowledge database and provide it through the ecosystem

[0431] Output: Knowledge information provided to the user

[0432] Specific operation: When a user requests information, the server searches for the relevant information from the database and provides it to the user through the ecosystem.

[0433] Step 3:

[0434] The robot detects error codes and maintenance requests.

[0435] Input: Data from the robot's sensors and internal systems

[0436] Data processing: Analyze and identify error codes and maintenance requests

[0437] Output: Detected error code and maintenance request

[0438] Specific operation: When the robot detects an abnormality during operation, it analyzes data from sensors and internal systems to identify error codes and maintenance requests.

[0439] Step 4:

[0440] The server searches for relevant information from a knowledge database based on the detected error code or maintenance request.

[0441] Input: Detected error code or maintenance request

[0442] Data processing: Search for relevant information from the knowledge database

[0443] Output: Related information

[0444] Specific operation: The server receives the error code or maintenance request and searches the knowledge database for corresponding information.

[0445] Step 5:

[0446] The server provides the retrieved relevant information to the robot.

[0447] Input: Relevant information retrieved from a knowledge database

[0448] Data processing: converting information into a format that a robot can understand

[0449] Output: Relevant information provided to the robot

[0450] Specific operation: The server sends the searched information to the robot, which displays it on the robot's display and outputs audio.

[0451] Step 6:

[0452] The robot performs maintenance and troubleshooting based on the information provided.

[0453] Input: Relevant information provided by the server

[0454] Data processing: Use the information to perform specific maintenance and troubleshooting procedures

[0455] Output: Maintenance and troubleshooting completed

[0456] Specific behavior: The robot performs necessary maintenance tasks or troubleshooting according to the information provided.

[0457] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0458] "Example 1"

[0459] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system includes a means for storing highly accurate results generated by users in a knowledge database via a knowledge generation platform, a means for providing the stored knowledge to users in the form of an ecosystem, and a means for improving convenience and business efficiency. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, the emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc., and adjusts the operation of the knowledge generation platform according to the emotions. For example, if the user feels angry, the emotion engine sends an instruction to the knowledge generation platform to prioritize providing knowledge that will quickly resolve the user's problem.

[0460] "Example 2"

[0461] Another embodiment of the present invention provides a system in which an emotion engine selects knowledge to provide based on a user's emotion. This system includes a means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, a means for providing the stored knowledge to users in the form of an ecosystem, and a means for improving convenience and business efficiency. Furthermore, this system includes an emotion engine that recognizes the user's emotion. Specifically, the emotion engine recognizes the user's emotion and selects knowledge to provide based on that emotion. For example, if the user feels happy, the emotion engine sends an instruction to the knowledge generation platform to provide knowledge that matches the user's interests and preferences.

[0462] "Example 3"

[0463] As a further embodiment of the present invention, a system is provided in which an emotion engine adjusts the operation of a knowledge generation platform in response to a user's emotion. The system includes a means for storing high-precision results generated by users in a knowledge DB via the knowledge generation platform, a means for providing the stored knowledge to users in the form of an ecosystem, and a means for improving convenience and work efficiency. The system further includes an emotion engine that recognizes the user's emotion. Specifically, the emotion engine recognizes the user's emotion and adjusts the operation of the knowledge generation platform in response to the emotion. For example, if the user feels confused, the emotion engine sends an instruction to the knowledge generation platform to quickly provide the information the user needs.

[0464] The processing flow of each embodiment will be described below.

[0465] "Example 1"

[0466] Step 1: A user uses the system to search or query for information.

[0467] Step 2: The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc.

[0468] Step 3: Adjust the behavior of the knowledge generation platform based on the emotions recognized by the emotion engine.

[0469] Step 4: The knowledge generation platform prioritizes and provides knowledge to quickly resolve users' problems.

[0470] "Example 2"

[0471] Step 1: A user uses the system to search or query for information.

[0472] Step 2: The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc.

[0473] Step 3: Select the knowledge to provide based on the emotion recognized by the emotion engine.

[0474] Step 4: The knowledge generation platform provides knowledge that matches the user's interests and preferences.

[0475] "Example 3"

[0476] Step 1: A user uses the system to search or query for information.

[0477] Step 2: The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc.

[0478] Step 3: Adjust the behavior of the knowledge generation platform based on the emotions recognized by the emotion engine.

[0479] Step 4: The knowledge generation platform quickly provides users with the information they need.

[0480] Example 1

[0481] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0482] Information obtained using conventional search engines and chatbots is only used temporarily for individual users and is rarely accumulated as useful knowledge for other or future users. Furthermore, because information is provided without considering the user's feelings, user satisfaction and work efficiency can decline. To solve these issues, a system is needed that efficiently accumulates highly accurate information obtained by users and provides appropriate information according to their feelings.

[0483] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0484] In this invention, the server includes means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the stored knowledge to users in the form of an ecosystem, means for improving convenience and business efficiency, means including an emotion engine for recognizing user emotions, and means for adjusting the operation of the knowledge generation platform in accordance with the emotions recognized by the emotion engine. This makes it possible to efficiently store high-precision information obtained by users and provide appropriate information in accordance with their emotions.

[0485] "User" means an individual or organization that uses the System to search for information or obtain information using a chatbot.

[0486] "High-precision results" are accurate and reliable information that users obtain through search engines and chatbots.

[0487] A "knowledge generation platform" is a system that receives information obtained by users, analyzes it, and stores it in a knowledge database.

[0488] A "knowledge database" is a database that accumulates knowledge in various fields and provides it as needed.

[0489] An "ecosystem" is an entire system that provides accumulated knowledge to users and enables them to utilize that knowledge.

[0490] "Improving convenience" refers to increasing the ease and efficiency of using the system.

[0491] "Improving business efficiency" refers to increasing the efficiency in carrying out business.

[0492] The "emotion engine" is an engine that recognizes emotions from the user's tone of voice, facial expressions, text input, etc., and adjusts the system's behavior according to those emotions.

[0493] "Adjusting behavior" refers to changing the behavior of the knowledge generation platform based on the emotions recognized by the emotion engine.

[0494] This invention is a system that accumulates highly accurate results obtained by users using conventional search engines and chatbots in a knowledge database via a knowledge generation platform, and provides the accumulated knowledge to users in the form of an ecosystem. Furthermore, by combining it with an emotion engine, knowledge is provided according to the user's emotions.

[0495] System configuration

[0496] 1. User operations

[0497] Users use the devices to input information into search engines and chatbots.

[0498] Example: User types, "Tell me about the latest AI technology."

[0499] 2. Device Operation

[0500] The terminal transmits the information input by the user to the knowledge generation platform.

[0501] Software used: A library for sending HTTP requests (e.g., Axios)

[0502] 3. Server Operation

[0503] The server analyzes the received information and stores it in a knowledge database.

[0504] Hardware used: Server (e.g. cloud service)

[0505] Software used: Database management system (e.g. MySQL)

[0506] 4. Operation of the Emotion Engine

[0507] The emotion engine recognizes emotions from the user's tone of voice, facial expressions, and text input.

[0508] Software used: Emotion recognition library (e.g. OpenCV, NLP library)

[0509] Example: If a user types "This problem won't go away!", the emotion engine will recognize that the user is angry.

[0510] 5. Coordinating knowledge delivery

[0511] The server receives instructions from the emotion engine and adjusts the knowledge provided according to the user's emotions.

[0512] Example: If a user is angry, the server can quickly provide knowledge to help resolve the issue.

[0513] 6. Providing knowledge

[0514] The server provides the accumulated knowledge to users in the form of an ecosystem.

[0515] Software used: Web application framework (e.g., Django)

[0516] Specific examples

[0517] User searches or enters information into the chatbot

[0518] A user types, "Tell me about the latest AI technology."

[0519] The user types, "This problem persists!"

[0520] The terminal sends information to the knowledge generation platform

[0521] The device sends an HTTP request containing the information "Tell me about the latest AI technology."

[0522] The device sends an HTTP request containing the information "This problem persists!"

[0523] The server analyzes the information and stores it in a knowledge database

[0524] The server analyzes the information "Tell me about the latest AI technology" and stores it in a knowledge database.

[0525] The server analyzes the information "This problem cannot be solved!" and stores it in a knowledge database.

[0526] Emotion engine recognizes user emotions

[0527] The emotion engine recognizes that the user is angry from the text "This problem won't go away!"

[0528] The emotion engine recognizes that the user is sad from their facial expression.

[0529] The server adjusts knowledge provision according to emotions.

[0530] The server receives information that "the user is angry" and provides high-priority knowledge.

[0531] The server receives information that "the user is sad" and provides comforting knowledge.

[0532] The server provides knowledge to the user

[0533] The server displays the knowledge "Tell me about the latest AI technology" on a web page.

[0534] The server displays the knowledge "This problem is not solved!" on a web page.

[0535] This system allows users to efficiently obtain highly accurate information and receive appropriate support according to their emotions.

[0536] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0537] Step 1:

[0538] A user searches for or enters information into a chatbot.

[0539] Input: A user uses a device to enter information into a search engine or chatbot.

[0540] What happens: A user opens a browser and types "Tell me about the latest AI technology" into the search bar.

[0541] Output: Information entered by the user is sent to the terminal.

[0542] Step 2:

[0543] The terminal transmits the information to the knowledge generation platform.

[0544] Input: Information entered by the user.

[0545] Specific operation: The terminal generates an HTTP request and sends data including the input information to the server. The library used is Axios.

[0546] Output: The information entered by the user is sent to the knowledge generation platform.

[0547] Step 3:

[0548] The server analyzes the information and stores it in a knowledge database.

[0549] Input: Information sent from the device.

[0550] Specific operation: The server runs a Python script to analyze the received data and saves the analysis results in a MySQL database.

[0551] Output: The analyzed information is stored in a knowledge database.

[0552] Step 4:

[0553] The emotion engine recognizes the user's emotions.

[0554] Input: User tone of voice, facial expressions, and text input.

[0555] What it does: The emotion engine uses OpenCV to analyze the user's facial expressions and NLP libraries to extract emotions from text input.

[0556] Output: The user's emotion is recognized.

[0557] Step 5:

[0558] The server adjusts the knowledge provision according to emotions.

[0559] Input: Emotion recognition results from the emotion engine.

[0560] Specific operation: The server receives information from the emotion engine that "the user is angry," searches for high-priority knowledge, and provides it.

[0561] Output: Knowledge provision instructions are generated according to the emotion.

[0562] Step 6:

[0563] The server provides knowledge to the user.

[0564] Input: Information stored in the knowledge database and instructions from the emotion engine.

[0565] What happens: The server uses the Django framework to generate a web page and display the knowledge to the user.

[0566] Output: The knowledge provided to the user is displayed on a web page.

[0567] (Application example 1)

[0568] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0569] Conventional knowledge management systems have had difficulty efficiently storing and appropriately providing information generated by users. They also lack the ability to respond to users' emotions, making it difficult to improve customer satisfaction. Furthermore, in brick-and-mortar stores, there was a lack of a way for staff to provide appropriate knowledge in real time when dealing with customers.

[0570] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for storing high-precision results generated by users in a knowledge DB via a knowledge generation platform, means for providing the stored knowledge to users in the form of an ecosystem, means for improving convenience and business efficiency, means for recognizing the user's emotions using an emotion engine and adjusting the operation of the knowledge generation platform according to those emotions, and means for providing knowledge in real time via a smart device. This makes it possible to provide appropriate knowledge according to the user's emotions, thereby improving the quality of customer service in physical stores.

[0571] "User" means an individual or entity that uses the system to generate information and acquire knowledge.

[0572] "High-precision results" are accurate and reliable information that users obtain through searches and chatbots.

[0573] The "knowledge generation platform" is a system for collecting information generated by users and storing it in a knowledge database.

[0574] A "knowledge DB" is a database that accumulates knowledge in various fields.

[0575] An "ecosystem" is an integrated system environment for providing accumulated knowledge to users.

[0576] "Improving convenience" refers to making it easier and more efficient for users to use the system.

[0577] "Improving business efficiency" refers to increasing business productivity and efficiency by using a system.

[0578] The "emotion engine" is a system that recognizes emotions from the user's tone of voice, facial expressions, text input, etc.

[0579] "Smart devices" are devices with internet connectivity such as smartphones, smart glasses, and head-mounted displays.

[0580] "Real-time" refers to immediate processing or response without delay.

[0581] As an embodiment of this invention, we will explain a customer service system using smart devices in a brick-and-mortar store. This system accumulates high-precision results generated by users in a knowledge database via a knowledge generation platform and provides the accumulated knowledge to users in the form of an ecosystem. Furthermore, it uses an emotion engine to recognize the user's emotions and adjusts the operation of the knowledge generation platform according to those emotions.

[0582] Hardware and software used

[0583] Hardware: Smart glasses (including camera, microphone, and display)

[0584] software:

[0585] EmotionRecognizer: A library for recognizing emotions from facial expressions

[0586] KnowledgeDB: A library for accessing the Knowledge DB and getting answers to questions.

[0587] SmartGlassesInterface: An interface for operating the camera, microphone, and display of smart glasses.

[0588] System Operation

[0589] The server captures the customer's face through the smart glasses' camera and uses EmotionRecognizer to recognize emotions from facial expressions. It then receives the customer's voice input question through the smart glasses' microphone and sends the question to the KnowledgeDB to obtain an appropriate answer. The knowledge generation platform then adjusts its behavior based on the recognized emotion, providing appropriate knowledge first. Finally, the answer is displayed on the smart glasses' display.

[0590] Specific examples

[0591] For example, if a customer asks a question about a product in a store and looks a little anxious, the emotion engine will recognize this. The server will then prioritize answers that will give the customer a sense of security from the knowledge database and display them on the smart glasses' display. This allows the customer to choose a product with peace of mind.

[0592] Prompt Sentence Examples

[0593] "If your customer looks anxious, provide a reassuring response."

[0594] In this way, the quality of customer service in physical stores can be improved.

[0595] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0596] Step 1:

[0597] The server captures the customer's face through the camera of the smart glasses. The input is the camera image and the output is the captured image data. This image data is sent to EmotionRecognizer.

[0598] Step 2:

[0599] The server recognizes the customer's emotions from the captured image data using EmotionRecognizer. The input is the image data, and the output is the recognized emotion data. EmotionRecognizer analyzes facial expressions and identifies emotions.

[0600] Step 3:

[0601] The server receives the customer's voice input of the question through the microphone of the smart glasses. The input is voice data and the output is text data. The server uses voice recognition software to convert the voice data into text.

[0602] Step 4:

[0603] The server sends the acquired text data to KnowledgeDB to obtain the appropriate answer. The input is text data and emotion data, and the output is the answer text. KnowledgeDB searches for answers to the question and provides appropriate answers preferentially based on the emotion data.

[0604] Step 5:

[0605] The server displays the obtained answer text on the display of the smart glasses. The input is the answer text and the output is the text displayed on the display. The answer is displayed using the display interface of the smart glasses.

[0606] Step 6:

[0607] The user checks the answer displayed on the smart glasses display and responds appropriately to the customer. The input is the text displayed on the display, and the output is the response to the customer. The user provides explanations and guidance to the customer based on the displayed information.

[0608] Example 2

[0609] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0610] Conventional information search systems have the problem that it is difficult for users to efficiently obtain the information they need, and the information provided does not respond to the user's emotions, resulting in low user satisfaction. Furthermore, there is a lack of means to effectively utilize accumulated knowledge, which makes it difficult to improve work efficiency.

[0611] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0612] In this invention, the server includes means for accumulating high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the accumulated knowledge to users in the form of an ecosystem, means for improving convenience and business efficiency, means for receiving user search queries and acquiring related information, means for transmitting the acquired information to the knowledge generation platform, and means for recognizing user emotions using an emotion engine and selecting knowledge to provide based on those emotions. This allows users to efficiently acquire the information they need, makes it possible to provide information based on emotions, and improves user satisfaction. Furthermore, by effectively utilizing the accumulated knowledge, business efficiency can be improved.

[0613] "User" means an individual or entity that uses the System to search for information and receive results.

[0614] "Highly accurate results" are search results that contain highly relevant and accurate information in response to a user's search query.

[0615] A "knowledge generation platform" is a system for collecting, analyzing, and storing search results and other information in a knowledge database.

[0616] A "knowledge database" is a database that stores accumulated knowledge and information and allows it to be searched and retrieved as needed.

[0617] An "ecosystem" is an integrated system or environment that provides accumulated knowledge to users.

[0618] "Improving convenience" refers to increasing the ease and efficiency with which users use the system.

[0619] "Improving business efficiency" refers to increasing the speed and effectiveness of business processing by using a system.

[0620] A "search query" is a keyword or phrase that a user enters when searching for information.

[0621] An "emotion engine" is a system that recognizes the user's emotions and provides appropriate information according to those emotions.

[0622] "Relevant information" refers to information that is appropriate and useful to a user's search query.

[0623] "Information provision" refers to the system presenting search results and other knowledge to users.

[0624] This invention is a system that, when a user searches for medical information, sends the search results to a knowledge generation platform and stores them in a knowledge database. Furthermore, by using an emotion engine to provide information according to the user's emotions, it improves user satisfaction.

[0625] Hardware and software used

[0626] Hardware

[0627] Device (PC, smartphone, etc.)

[0628] Server (data processing and knowledge database management)

[0629] software

[0630] Knowledge generation platform (data accumulation and provision)

[0631] Emotion engine (recognition of user emotions)

[0632] Search engines (medical information search)

[0633] Program processing

[0634] 1. User search behavior

[0635] A user uses a device to search for medical information. For example, the user opens a browser, types "diabetes cure" in the search bar, and clicks the search button.

[0636] 2. Obtaining and sending search results

[0637] The device sends a search query to the server. Specifically, the device sends the search query "diabetes treatment" to the server as an HTTP request.

[0638] The server processes the search query and retrieves relevant medical information. The server uses databases and external APIs to gather information about "diabetes treatments."

[0639] The server sends the acquired search results to the knowledge generation platform. The server sends the search results in JSON format to the knowledge generation platform.

[0640] 3. Accumulation in the knowledge database

[0641] The knowledge generation platform accumulates the received search results in a knowledge database, analyzes the search results, and stores them in the knowledge database in an appropriate format.

[0642] 4. Providing information to other users

[0643] Another user searches for similar information. Another user uses the device to search again for "diabetes cure."

[0644] The server retrieves the relevant information from the knowledge database. The server sends a query to the knowledge database to retrieve information about "diabetes treatment."

[0645] The server provides the acquired information to the user. The server sends the acquired information to the terminal, and the terminal displays the information to the user.

[0646] 5. Operation of the Emotion Engine

[0647] The emotion engine recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and input content to determine their emotions.

[0648] If the emotion engine senses that the user is happy, it sends instructions to the knowledge generation platform to provide knowledge that matches the user's interests and preferences. If the emotion engine determines that the user is "happy," it sends instructions to the knowledge generation platform to "provide information that matches the user's interests."

[0649] Examples and prompts

[0650] Specific examples

[0651] User A searches for "diabetes treatment."

[0652] The server obtains the relevant medical information and sends it to the knowledge generation platform.

[0653] The knowledge generation platform stores information in a knowledge database.

[0654] When User B searches for "diabetes treatment," the server retrieves information from the knowledge database and provides it to User B.

[0655] If the emotion engine recognizes User B's emotion as "happy," it provides additional information that matches User B's interests.

[0656] Prompt Sentence Examples

[0657] When a user searches for "diabetes treatment," the search results should be sent to a knowledge generation platform and stored in a knowledge database. If another user subsequently searches for similar information, the system should retrieve the relevant information from the knowledge database and provide it to the user. It should also recognize the user's emotions and select the knowledge to provide based on those emotions.

[0658] This invention allows users to efficiently obtain the medical information they need and also provides information tailored to their emotions, thereby improving user satisfaction and work efficiency.

[0659] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0660] Step 1:

[0661] User search behavior

[0662] Input: A user uses a device to enter a search query.

[0663] What happens: A user opens a browser, types "diabetes cure" into the search bar, and clicks the search button.

[0664] Output: The device sends the search query to the server.

[0665] Step 2:

[0666] Getting search results

[0667] Input: The search query sent from the device.

[0668] Specific operation: The device sends the search query "diabetes treatment" to the server as an HTTP request.

[0669] Data processing: The server analyzes the search query and collects relevant medical information from databases and external APIs.

[0670] Output: The server generates the medical information it has acquired as a search result.

[0671] Step 3:

[0672] Submit search results

[0673] Input: Server-generated search results.

[0674] Specific operation: The server sends the search results in JSON format to the knowledge generation platform.

[0675] Output: The knowledge generation platform receives the search results.

[0676] Step 4:

[0677] Accumulation in knowledge database

[0678] Input: Search results received by the knowledge generation platform.

[0679] Specific operation: The knowledge generation platform analyzes the search results and stores them in the knowledge database in an appropriate format.

[0680] Data processing: Search results are organized as structured data and stored in a knowledge database.

[0681] Output: Search results stored in a knowledge database.

[0682] Step 5:

[0683] Providing information to other users

[0684] Input: A query where another user searches for similar information.

[0685] Specific action: Another user uses the device to search for "diabetes cure" again.

[0686] Data processing: The server queries the knowledge database to obtain information on "diabetes treatment."

[0687] Output: The server sends the acquired information to the terminal, which displays the information to the user.

[0688] Step 6:

[0689] Emotion Engine Operation

[0690] Input: User's emotional data (facial expressions and input content).

[0691] Specific operation: The emotion engine analyzes the user's facial expressions and input content to determine their emotions.

[0692] Data calculation: If the emotion engine determines that the user is happy, it sends an instruction to the knowledge generation platform to provide information that matches the user's interests.

[0693] Output: The knowledge generation platform provides additional information that matches the user's interests.

[0694] In this way, the system processes a series of steps, starting with the user's search behavior, acquiring search results, storing them in a knowledge database, providing information to other users, and finally recognizing emotions and optimizing information provision using an emotion engine.

[0695] (Application example 2)

[0696] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0697] Conventional knowledge provision systems make it difficult for users to efficiently obtain the information they need, and they do not provide appropriate information based on their emotions. Furthermore, there is a lack of effective ways to utilize search results for information on electronic payments, so improvements in user convenience and operational efficiency are needed.

[0698] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0699] In this invention, the server includes means for accumulating high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the accumulated knowledge to users in the form of an ecosystem, means for improving convenience and business efficiency, means for selecting knowledge according to the user's emotions using an emotion recognition engine, means for searching for information related to electronic payments and saving the results in the knowledge database, and means for filtering knowledge based on the user's emotions and providing appropriate information. This enables users to efficiently obtain the information they need and provides appropriate information according to their emotions.

[0700] "High-precision results" are accurate and reliable information obtained by users through searches and operations.

[0701] A "knowledge generation platform" is a system for collecting information generated by users and storing it in a knowledge database.

[0702] A "knowledge database" is a database that stores collected information and provides it as needed.

[0703] An "ecosystem" is an integrated system environment for providing accumulated knowledge to users.

[0704] "Improving convenience" refers to increasing the ease and efficiency with which users can use the system.

[0705] "Improving business efficiency" refers to increasing the efficiency of business operations.

[0706] An "emotion recognition engine" is an engine that recognizes the user's emotions and provides appropriate information based on those emotions.

[0707] "Electronic payment" is a payment method that is carried out using the Internet or electronic devices.

[0708] "Filtering" refers to selecting information based on specific conditions.

[0709] The following system configuration will be described as an embodiment of the present invention.

[0710] System Configuration

[0711] Hardware

[0712] server

[0713] Smartphone

[0714] Emotion Recognition Engine (EmotionEngine)

[0715] software

[0716] Knowledge Generation Platform

[0717] Knowledge Database (KnowledgeDB)

[0718] Search Engine API (using the requests library)

[0719] Emotion recognition software (EmotionEngine)

[0720] Program processing

[0721] server

[0722] The server accumulates highly accurate results generated by users in a knowledge database via a knowledge generation platform. Specifically, when a user searches for information about electronic payments, the search results are sent to the knowledge generation platform and stored in the knowledge database. It also uses an emotion recognition engine to recognize the user's emotions, filter knowledge based on those emotions, and provide appropriate information.

[0723] Smartphone

[0724] The smartphone provides an interface for users to search for information about electronic payments. The search results are sent to a knowledge generation platform and stored in a knowledge database. If the user needs the same information again, an emotion recognition engine recognizes the user's emotions and provides appropriate knowledge.

[0725] Emotion Recognition Engine

[0726] The emotion recognition engine analyzes the user's voice and facial expression data to recognize their emotions, and then filters and provides appropriate information from a knowledge database based on the recognized emotions.

[0727] Specific examples

[0728] For example, if a user searches for information on "electronic payment security," the search results are stored in a knowledge database. When another user needs the same information, if the emotion recognition engine recognizes the user's emotion as "happy," positive information will be provided first.

[0729] Prompt Sentence Examples

[0730] When a user searches for information about "electronic payment security," write a Python program that stores the search results in a knowledge database and uses an emotion recognition engine to provide appropriate information when another user needs the same information.

[0731] In this way, users can efficiently obtain the information they need and be provided with appropriate information according to their emotions.

[0732] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0733] Step 1:

[0734] A user searches for information about electronic payments using a smartphone.

[0735] Input: User's search query (e.g., "electronic payment security")

[0736] Output: The search query is sent to the server.

[0737] Specific action: The user types "electronic payment security" into the search bar on their smartphone and presses the search button.

[0738] Step 2:

[0739] The server receives the search query and sends a request to an external search engine API.

[0740] Input: User's search query

[0741] Output: Search results from the search engine API

[0742] Specific operation: The server sends a search query to the search engine API and receives search results.

[0743] Step 3:

[0744] The server sends the received search results to the knowledge generation platform and stores them in the knowledge database.

[0745] Input: Search results from the search engine API

[0746] Output: Search results stored in a knowledge database

[0747] Specific operation: The server sends the search results to the knowledge generation platform and stores them in the knowledge database.

[0748] Step 4:

[0749] Another user searches for the same information using a smartphone.

[0750] Input: Another user's search query (e.g., "electronic payment security")

[0751] Output: The search query is sent to the server.

[0752] Specific action: Another user types "electronic payment security" into the search bar of their smartphone and presses the search button.

[0753] Step 5:

[0754] A server receives a search query and retrieves relevant information from a knowledge database.

[0755] Input: Another user's search query

[0756] Output: Relevant information retrieved from the knowledge database

[0757] Specific operation: The server sends a query to the knowledge database to retrieve relevant information.

[0758] Step 6:

[0759] The server uses an emotion recognition engine to recognize the emotion of another user.

[0760] Input: Voice and facial expression data of another user

[0761] Output: Recognized emotion (e.g., "happy")

[0762] Specific operation: The server sends another user's voice and facial expression data to the emotion recognition engine to recognize the emotion.

[0763] Step 7:

[0764] The server filters the information retrieved from the knowledge database based on the recognized sentiment.

[0765] Input: Relevant information retrieved from the knowledge database, recognized emotions

[0766] Output: Filtered information

[0767] Specific operation: The server filters the information retrieved from the knowledge database based on the recognized emotion.

[0768] Step 8:

[0769] The server provides the filtered information to another user.

[0770] Input: Filtered information

[0771] Output: Information provided to another user

[0772] Specific operation: The server sends the filtered information to another user's smartphone and displays it.

[0773] Example 3

[0774] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0775] Conventional information search systems have difficulty in quickly providing users with the information they need, and when users are particularly confused, it takes a long time for them to find the appropriate information. Furthermore, because information is not provided in response to the user's emotions, convenience and operational efficiency have not been sufficiently improved.

[0776] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0777] In this invention, the server includes means for accumulating high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the accumulated knowledge to users in the form of an ecosystem, means for improving convenience and business efficiency, means for adjusting the operation of the knowledge generation platform using an emotion engine that recognizes the user's emotions, means for generating appropriate information based on the analysis results of the emotion engine, means for providing the generated information to users, and means for accumulating the generated information in the knowledge database. This makes it possible to quickly provide appropriate information according to the user's emotions, thereby shortening information search time and improving business efficiency.

[0778] A "user" is an individual or organization that uses the system to search for information and to streamline their work.

[0779] "Highly Accurate Results" means user-generated information or data that is accurate and reliable.

[0780] A "knowledge generation platform" is a system for processing information generated by users and storing it in a knowledge database.

[0781] A "knowledge database" is a database that stores accumulated information and data and provides them as needed.

[0782] An "ecosystem" is an integrated system environment for providing accumulated knowledge to users.

[0783] "Improving convenience" means improving the ease and comfort of use when users use the system.

[0784] "Improving business efficiency" means enabling users to perform their work more quickly and effectively.

[0785] An "emotion engine" is a technology that recognizes the user's emotions and adjusts the system's behavior according to those emotions.

[0786] "Analysis results" refer to the results of the emotion engine's analysis of the user's emotions.

[0787] "Appropriate information" is the most useful and relevant information provided based on the user's emotions and situation.

[0788] MODE FOR CARRYING OUT THE INVENTION

[0789] This invention is a system that accumulates highly accurate results generated by users in a knowledge database via a knowledge generation platform and provides the accumulated knowledge to users in the form of an ecosystem. Furthermore, it uses an emotion engine to recognize the user's emotions and adjusts the operation of the knowledge generation platform according to those emotions, thereby improving convenience and business efficiency.

[0790] Hardware and software used

[0791] The server uses a general cloud service as a knowledge generation platform. Specifically, it uses a cloud service that provides an "emotion recognition API" as the emotion engine, and a cloud service that provides a "natural language processing API" as the knowledge generation platform. This makes it possible to analyze the user's emotions and generate appropriate information.

[0792] Terminals are devices that users use to access the system, and include PCs, smartphones, tablets, etc. Terminals are equipped with cameras and microphones, which are used to capture the user's facial expressions and voice.

[0793] Program processing

[0794] The server receives the user's facial expressions and voice data sent from the device and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotions. For example, if the server determines that the user is confused, it sends instructions to the knowledge generation platform to generate the information the user needs.

[0795] The generated information is provided to the user's device via the server. The user can check the provided information on their device and carry out their work efficiently. The generated information is also stored in a knowledge database, allowing other users to quickly obtain similar information in the future.

[0796] Specific examples

[0797] For example, if a user is confused about how to use new software, the following actions may occur:

[0798] 1. A user accesses the system through a terminal and types, "I don't know how to use the new software."

[0799] 2. The device's camera captures the user's confused expression and sends the data to the emotion engine.

[0800] 3. The emotion engine analyzes the user's facial expression and determines that they are "confused."

[0801] 4. The server receives the analysis results of the emotion engine and sends instructions to the knowledge generation platform to generate detailed guides on how to use the software.

[0802] 5. The knowledge generation platform generates a detailed guide and sends it to the server.

[0803] 6. The server displays the generated guide on the user's device.

[0804] 7. The user reads the guide and learns how to use the software.

[0805] 8. The server stores the generated guide in a knowledge database, allowing other users to quickly retrieve the same information in the future.

[0806] Prompt Sentence Examples

[0807] "I'm having trouble figuring out how to use new software. Could you please tell me the specific steps?"

[0808] In this way, the system can shorten information search time and improve work efficiency by quickly providing appropriate information according to the user's emotions. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0809] Step 1:

[0810] A user accesses the system through a terminal.

[0811] Input: A user accesses a URL on the system using a terminal.

[0812] Output: The system home page is displayed on the terminal.

[0813] Specific operation: A user opens a web browser, enters the system's URL, and accesses it. The system's home page is displayed, and the user is ready to start searching for information.

[0814] Step 2:

[0815] The terminal transmits the user's emotions to the emotion engine.

[0816] Input: The device's camera and microphone capture the user's facial expressions and voice.

[0817] Output: The captured data is sent to the emotion engine.

[0818] How it works: The device's camera captures the user's face and the microphone records the user's voice, and this data is sent to the emotion engine in real time.

[0819] Step 3:

[0820] The emotion engine analyzes the user's emotions.

[0821] Input: Facial expression and voice data sent from the device.

[0822] Output: Analysis results showing user sentiment.

[0823] Specific operation: The emotion engine analyzes the received data and identifies the user's emotion, such as whether they are confused, happy, angry, etc. For example, if they are determined to be confused, the result is sent to the server.

[0824] Step 4:

[0825] The server receives the analysis results of the emotion engine.

[0826] Input: Analysis results sent from the emotion engine.

[0827] Output: Instructions are generated based on the analysis results.

[0828] Specific operation: The server receives the analysis results sent from the emotion engine and generates instructions to provide appropriate information according to the user's emotions.

[0829] Step 5:

[0830] The server sends instructions to the knowledge generation platform.

[0831] Input: Instructions based on the emotion engine's analysis results.

[0832] Output: Instructions sent to the knowledge generation platform.

[0833] Specific operation: If the server determines that the user is confused, it sends an instruction to the knowledge generation platform to "generate a detailed guide on how to use the software."

[0834] Step 6:

[0835] The knowledge generation platform generates relevant information.

[0836] Input: Instructions sent by the server.

[0837] Output: The information generated.

[0838] Specific Actions: Based on the instructions, the knowledge generation platform generates a detailed guide on how to use the software the user is having trouble with, including specific operating procedures and troubleshooting information.

[0839] Step 7:

[0840] The server provides the generated information to the user.

[0841] Input: Information sent from the knowledge generation platform.

[0842] Output: Information displayed on the user's terminal.

[0843] Specific operation: The server receives the generated guide and sends it to the user's device. The user then checks the detailed guide on the device and understands how to use the software.

[0844] Step 8:

[0845] The server stores the generated information in a knowledge database.

[0846] Input: Information sent from the knowledge generation platform.

[0847] Output: Information stored in the knowledge database.

[0848] Specific operation: The server stores the generated guide in a knowledge database so that other users can quickly obtain the same information in the future, for example, other users who are confused about how to use the same software can access it.

[0849] (Application example 3)

[0850] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0851] Robots working in factories face the challenge of quickly providing appropriate knowledge when they become confused or make errors during their work. There is also a need to efficiently store new knowledge acquired during work in a knowledge database and use it in real time. Furthermore, it is necessary to improve convenience and operational efficiency by adjusting the behavior of the knowledge generation platform according to the user's emotions.

[0852] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0853] In this invention, the server includes: means for storing high-precision results generated by users in a knowledge DB via a knowledge generation platform; means for providing the stored knowledge to users in the form of an ecosystem; means for improving convenience and work efficiency; means for recognizing user emotions using an emotion engine and adjusting the operation of the knowledge generation platform in accordance with those emotions; means for being installed in robots performing work in a factory and providing knowledge in real time; means for quickly providing appropriate knowledge when a robot senses confusion or makes an error; and means for automatically storing new knowledge acquired during work in a knowledge DB. This makes it possible to significantly improve work efficiency in a factory.

[0854] A "user" is a person or machine that uses the system to obtain information or perform a task.

[0855] "High accuracy results" means user-generated information or data that is accurate and reliable.

[0856] A "knowledge generation platform" is a system that collects, organizes, and stores information generated by users, and provides it as needed.

[0857] "Knowledge DB" is a database in which information accumulated by the knowledge generation platform is stored.

[0858] An "ecosystem" is a system that provides information accumulated in a knowledge database to users and allows them to share information with each other.

[0859] "Improving convenience" means increasing the ease and efficiency with which users use the system.

[0860] "Improving business efficiency" means making it possible to carry out work or tasks more quickly and effectively.

[0861] An "emotion engine" is an engine that has the function of recognizing the user's emotions and adjusting the system's behavior in response to those emotions.

[0862] A "robot working in a factory" is a machine that automatically performs specific tasks in a factory.

[0863] "Means of providing knowledge in real time" is a function that provides necessary information instantly.

[0864] "Means to quickly provide appropriate knowledge in the event of confusion or an error" refers to a function that immediately provides solutions and related information when the robot detects a problem.

[0865] "Means for automatically storing new knowledge gained during work in a knowledge database" is a function that automatically saves new information and knowledge discovered during work in a database.

[0866] As an embodiment of this invention, we will explain a system installed on a robot that works in a factory. This system accumulates high-precision results generated by users in a knowledge database via a knowledge generation platform and provides the accumulated knowledge to users in the form of an ecosystem. It also uses an emotion engine to recognize the user's emotions and adjusts the operation of the knowledge generation platform according to those emotions.

[0867] Hardware and software used

[0868] Hardware: Factory robots, cameras (for emotion recognition)

[0869] Software: Python, emotion_recognition library, knowledge_db library

[0870] Data processing and calculation

[0871] emotion recognition

[0872] The camera captures the robot's actions and errors, and the emotion_recognition library is used to recognize emotions. The emotion engine detects the robot's emotions when it feels confused or makes an error, and sends instructions to the knowledge generation platform to provide appropriate knowledge.

[0873] Knowledge provision

[0874] Based on the emotions recognized by the emotion engine, the knowledge generation platform uses the knowledge_db library to retrieve appropriate information from the knowledge database and provide it to the robot, allowing it to quickly resolve confusions and errors.

[0875] Knowledge DB update

[0876] New knowledge and information gained during work is automatically stored in the knowledge database through the knowledge generation platform, allowing for quick responses if similar problems arise in future work.

[0877] Specific examples

[0878] For example, if an error occurs when the robot is assembling part A, the emotion engine will recognize the robot's confusion and retrieve and provide information from the knowledge database on how to assemble part A. Based on this information, the robot can resolve the error and continue working.

[0879] Prompt Sentence Examples

[0880] If an error occurs when the robot assembles part A, the emotion engine should recognize the robot's confusion and retrieve information from the knowledge database on how to assemble part A and provide it.

[0881] In this way, it is possible to significantly improve work efficiency within the factory.

[0882] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0883] Step 1:

[0884] The server acquires video data in real time from the factory robot's camera.

[0885] Input: Factory robot camera footage

[0886] Output: Video data

[0887] Specific operation: The server receives video data in streaming format from the camera mounted on the factory robot.

[0888] Step 2:

[0889] The server uses the emotion_recognition library to recognize the robot's emotions from video data.

[0890] Input: Video data

[0891] Output: Emotion data (e.g., confusion, error)

[0892] Specific operation: The server inputs the received video data into the emotion_recognition library and analyzes the emotions from the robot's facial expressions and movements.

[0893] Step 3:

[0894] The server sends instructions to the knowledge generation platform based on the recognized emotion data.

[0895] Input: Emotion data

[0896] Output: Instructions to the knowledge generation platform

[0897] Specific operation: If the emotion data is "confusion" or "error", the server sends an instruction to the knowledge generation platform to provide appropriate knowledge.

[0898] Step 4:

[0899] The knowledge generation platform uses the knowledge_db library to retrieve the appropriate information from the knowledge DB.

[0900] Input: Instructions to the knowledge generation platform

[0901] Output: Relevant knowledge information

[0902] Specific operation: The knowledge generation platform queries the knowledge DB based on the instructions and obtains relevant information.

[0903] Step 5:

[0904] The server provides the acquired knowledge information to the factory robot.

[0905] Input: Appropriate knowledge information

[0906] Output: Providing knowledge information to factory robots

[0907] Specific operation: The server sends the acquired knowledge information to the factory robot, allowing the robot to continue working based on that information.

[0908] Step 6:

[0909] The server automatically accumulates new knowledge gained during work in the knowledge database through the knowledge generation platform.

[0910] Input: New knowledge or information

[0911] Output: Accumulation in knowledge DB

[0912] Specific operation: The server sends new knowledge and information acquired by the robot during work to the knowledge generation platform, where it is automatically stored in the knowledge database.

[0913] In this way, it is possible to significantly improve work efficiency within the factory.

[0914] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0915] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0916] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0917] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0918] [Second embodiment]

[0919] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0920] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0921] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0922] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0923] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0924] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0925] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0926] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0927] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0928] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0929] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0930] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0931] "Example 1"

[0932] The present invention is a system that stores highly accurate results generated by users using conventional search and chatbots in a knowledge database via a knowledge generation platform. Specifically, users send information obtained through search and chatbots to the knowledge generation platform, which stores the information in the knowledge database. This knowledge database stores knowledge in various fields and provides that knowledge to users in the form of an ecosystem.

[0933] "Example 2"

[0934] As a specific example, when a user searches for medical information, the search results are sent to the knowledge generation platform, and that information is stored in the knowledge database. If another user subsequently needs similar information, the relevant information is retrieved from the knowledge database and provided to the user. This allows users to efficiently obtain the information they need.

[0935] "Example 3"

[0936] Furthermore, the present invention is a system that aims to improve convenience and business efficiency. Specifically, by providing information stored in a knowledge database at the time the user needs it, the system reduces the time the user spends searching for information and improves business efficiency.

[0937] The processing flow of each embodiment will be described below.

[0938] "Example 1"

[0939] Step 1: The user generates information using traditional search or chatbots.

[0940] Step 2: Send the generated information to the knowledge generation platform.

[0941] Step 3: The knowledge generation platform stores the information in the knowledge database.

[0942] "Example 2"

[0943] Step 1: The user conducts a search for medical information.

[0944] Step 2: Submit the search results to the knowledge generation platform.

[0945] Step 3: The knowledge generation platform stores the information in the knowledge database.

[0946] Step 4: When another user needs similar information, the relevant information is retrieved from the knowledge database and provided to the user.

[0947] "Example 3"

[0948] Step 1: The user searches for the information they need.

[0949] Step 2: Obtain the relevant information from the knowledge database.

[0950] Step 3: Provide the acquired information to the user.

[0951] Example 1

[0952] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0953] The information obtained using conventional search engines and interactive agents is temporary and difficult to reuse. Furthermore, there is a lack of means to guarantee the accuracy and reliability of the information obtained, making it difficult for users to efficiently obtain highly accurate information. Furthermore, there is a need for a method to improve user convenience and enhance work efficiency by providing accumulated knowledge as an ecosystem.

[0954] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0955] In this invention, the server includes a means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, a means for providing the stored knowledge to users in the form of an ecosystem, and a means for improving convenience and business efficiency, thereby enabling the efficient storage and reuse of high-precision information obtained by users using search engines and interactive agents.

[0956] "User" means an individual or entity that uses the system to obtain information and submit the generated results to the knowledge generation platform.

[0957] "High-precision results" are accurate and reliable information obtained by users using search engines or interactive agents.

[0958] The "knowledge generation platform" is a system that analyzes information sent by users, converts it into an appropriate format, and stores it in a knowledge database.

[0959] A "knowledge database" is a database system that accumulates analyzed information and stores it in a form that can be reused later.

[0960] An "ecosystem" is an entire system that provides accumulated knowledge to users and enables them to efficiently obtain information.

[0961] A "search engine" is software that searches the Internet for relevant information based on keywords entered by a user and provides the results.

[0962] An "interactive agent" is software that responds to users' questions in natural language, and is also called a chatbot.

[0963] "Terminal" refers to a device that a user uses to access a search engine or interactive agent, including a PC or smartphone.

[0964] A "server" is a computer system that operates a knowledge generation platform and knowledge database, and analyzes and stores information.

[0965] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is used to understand the meaning of information and convert it into an appropriate format.

[0966] The "HTTPS protocol" is a communication protocol for securely sending and receiving data over the Internet.

[0967] This invention is a system that accumulates high-precision results generated by users in a knowledge database via a knowledge generation platform and provides the accumulated knowledge to users in the form of an ecosystem. Specific embodiments of this system are described below.

[0968] System configuration

[0969] Hardware

[0970] Devices: Devices such as PCs, smartphones, and tablets that allow users to access search engines and interactive agents.

[0971] Server: A computer system for operating the knowledge generation platform and knowledge database, equipped with a high-performance processor and large-capacity storage.

[0972] software

[0973] Search engine: Software that searches the Internet for relevant information based on keywords entered by the user and provides results.

[0974] Conversational Agent: Software that responds to user questions in natural language, using generative AI models to generate appropriate answers to the questions.

[0975] Knowledge generation platform: A system that analyzes information sent by users, converts it into an appropriate format, and stores it in a knowledge database. It uses natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding.

[0976] Knowledge database: A database system that accumulates analyzed information and stores it in a form that can be reused later. It uses a database management system such as MySQL or PostgreSQL.

[0977] Data processing and calculation

[0978] Acquisition and transmission of information

[0979] A user uses a device to access a search engine or conversational agent to obtain information. For example, the user asks the conversational agent, "Tell me about the latest AI technology." The conversational agent uses a generative AI model to generate an appropriate answer to the question. If this answer is determined to be highly accurate, the device sends this information to the knowledge generation platform. Transmission is via the HTTPS protocol.

[0980] Analysis and accumulation of information

[0981] The server analyzes the information received by the knowledge generation platform. This analysis uses natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding. The server understands the meaning of the information and converts it into an appropriate format. The analyzed information is then stored in a knowledge database.

[0982] Reuse of information

[0983] When a user retrieves information using a search engine or a conversational agent again, the accumulated information from the knowledge database is reused. For example, if another user asks, "Tell me about the latest AI technology," the conversational agent will retrieve information from the knowledge database, such as, "This includes generative AI models and deep learning," and respond.

[0984] Examples of concrete examples and prompts

[0985] As a concrete example, consider the case where a user asks a conversational agent, "Tell me about the latest AI technologies." The conversational agent responds, "The latest AI technologies include generative AI models and deep learning." If this answer is deemed highly accurate, the device sends this information to the knowledge generation platform. The server analyzes the information using the Google Cloud Natural Language API and stores it in a MySQL database. This information can be reused when other users search for "the latest AI technologies."

[0986] Examples of prompts to input to a generative AI model might include:

[0987] "Tell me about the latest AI technology"

[0988] By inputting this prompt sentence into the interactive agent, the user can obtain highly accurate information, which is then stored in a knowledge database.

[0989] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0990] Step 1:

[0991] Users retrieve information using search engines or interactive agents.

[0992] Specifically, the user uses a terminal to ask the conversational agent, "Tell me about the latest AI technology."

[0993] Input: User question (prompt)

[0994] Output: Answer from the conversational agent (answer from the generative AI model)

[0995] Step 2:

[0996] The terminal transmits the acquired information to the knowledge generation platform.

[0997] Specifically, the terminal sends the answer it receives from the conversational agent, "Modern AI technologies include generative AI models and deep learning," to the knowledge generation platform using the HTTPS protocol.

[0998] Input: Answer from the conversational agent

[0999] Output: Data sent to the knowledge generation platform

[1000] Step 3:

[1001] The server analyzes the information on the knowledge generation platform.

[1002] Specifically, the server uses the Google Cloud Natural Language API to analyze the received information, understand the meaning of the information, and convert it into an appropriate format.

[1003] Input: Data sent to the knowledge generation platform

[1004] Output: Parsed information (data converted into the appropriate format)

[1005] Step 4:

[1006] The server stores the analyzed information in a knowledge database.

[1007] Specifically, the server inserts the parsed information into a MySQL database for later reuse.

[1008] Input: Parsed information

[1009] Output: Data stored in the knowledge database

[1010] Step 5:

[1011] Users reuse information from the knowledge database.

[1012] Specifically, when another user asks the conversational agent, "Tell me about the latest AI technology," the conversational agent retrieves information from the knowledge database that says, "This includes generative AI models and deep learning," and responds.

[1013] Input: User question (prompt)

[1014] Output: Information retrieved from the knowledge database (answer by the conversational agent)

[1015] (Application example 1)

[1016] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1017] In conventional factory work, it was difficult for workers to quickly obtain the information they needed, resulting in a decline in work efficiency. Also, a lot of time was often spent troubleshooting and learning optimal work procedures, resulting in a decline in overall productivity. Furthermore, while automation using robots is progressing, there was also the problem of insufficient cooperation with workers, making efficient operation difficult.

[1018] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1019] In this invention, the server includes a means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, a means for providing the stored knowledge to users in the form of an ecosystem, a means for improving convenience and work efficiency, a means for providing a knowledge-sharing robot assistant to improve work efficiency within the factory, a means for displaying information to workers in real time via smart devices, and a means for the robot to transport necessary parts according to the worker's instructions and automate simple tasks. This allows workers to quickly obtain the information they need and efficiently troubleshoot and optimize work procedures. It also strengthens collaboration with the robot, improving overall productivity.

[1020] "User" refers to a person who uses the system to search for information or acquire knowledge.

[1021] "High-precision results" refers to accurate and reliable information obtained through search and chatbots.

[1022] A "knowledge generation platform" refers to a system that collects information obtained by users and stores it in a knowledge database.

[1023] "Knowledge DB" refers to a database that accumulates knowledge from various fields and provides it to users in the form of an ecosystem.

[1024] An "ecosystem" refers to a system in which information accumulated in a knowledge database is provided to users and mutually utilized.

[1025] "Improving convenience" means enabling users to quickly and easily obtain the information they need.

[1026] "Improving business efficiency" refers to increasing work efficiency and improving productivity.

[1027] A "knowledge-sharing robot assistant" is a robot that retrieves information from a knowledge database and provides it to workers in order to improve work efficiency within the factory.

[1028] "Smart device" refers to devices for displaying information, such as smartphones, smart glasses, and head-mounted displays.

[1029] "Displaying information in real time" means displaying the necessary information instantly so that workers can use it on the spot.

[1030] A "robot" refers to a machine that transports necessary parts according to the instructions of a worker or automates simple tasks.

[1031] To implement the present invention, the following system configuration and program are required.

[1032] System Configuration

[1033] 1. Hardware

[1034] Smart devices: smartphones, smart glasses, head-mounted displays (e.g., Google Glass, Microsoft HoloLens)

[1035] Factory robots: Robots that transport parts and automate simple tasks (e.g., Universal Robots UR series)

[1036] Server: Server for hosting the knowledge generation platform and knowledge DB

[1037] 2. Software

[1038] API server: Server software (e.g., Flask) for providing the functionality of the knowledge generation platform

[1039] Database: Database software that functions as a knowledge database (e.g., MongoDB)

[1040] Program processing

[1041] The server executes a program that includes the following means:

[1042] 1. Knowledge Generation Platform

[1043] Users use searches and chatbots via their smart devices, and the highly accurate results obtained are sent to a knowledge generation platform.

[1044] The knowledge generation platform stores the received information in a knowledge database.

[1045] 2. Knowledge DB

[1046] The knowledge database accumulates knowledge in various fields and provides it to users in the form of an ecosystem.

[1047] When a user requests the information they need, the most appropriate information is retrieved from the knowledge database and displayed in real time on the smart device.

[1048] 3. Knowledge-Sharing Robot Assistant

[1049] To improve work efficiency within the factory, information is retrieved from the knowledge database and provided to workers.

[1050] The robot follows the worker's instructions to transport the necessary parts and automate simple tasks.

[1051] Specific examples

[1052] For example, when a worker searches for "the best welding technique," the knowledge generation platform stores that information in the knowledge database. Next, when another worker needs the same information, the information about the best welding technique is retrieved from the knowledge database and displayed in real time on the smart glasses.

[1053] If troubleshooting is required, the worker can search for "how to solve error code 123," and the solution will be retrieved from the knowledge database and displayed on the head-mounted display.The robot will then transport the necessary parts according to the worker's instructions, automating the work.

[1054] Prompt Sentence Examples

[1055] "What is the best welding technique?"

[1056] How do I resolve error code 123?

[1057] This allows workers to quickly obtain the information they need, allowing them to efficiently troubleshoot and optimize work procedures. It also strengthens collaboration with robots, improving overall productivity.

[1058] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1059] Step 1:

[1060] A user uses a smart device to input information through a search query or a chatbot. The input query is sent to a knowledge generation platform. The input is text data about the information the user wants to know or the problem they want to solve. The output is the query data sent to the knowledge generation platform.

[1061] Step 2:

[1062] The server processes the query data received through the knowledge generation platform and generates relevant high-precision results. This process involves using a generative AI model to generate optimal answers to the queries. The input is the query data submitted by the user, and the output is the generated high-precision results.

[1063] Step 3:

[1064] The server stores the generated high-precision results in a knowledge database. The input here is the generated high-precision results, and the output is the data stored in the knowledge database. Data processing involves converting the result data into an appropriate format and saving it in the database.

[1065] Step 4:

[1066] When the user needs the information again, he / she sends a request to the knowledge DB via his / her smart device. The input is the request data related to the information the user wants to know, and the output is the request data sent to the knowledge DB.

[1067] Step 5:

[1068] The server retrieves information corresponding to the request from the knowledge database. The input is the request data from the user, and the output is the related information retrieved from the knowledge database. Data calculation involves searching the database based on the request and extracting the most appropriate information.

[1069] Step 6:

[1070] The server sends the acquired information to the smart device and displays it to the user in real time. The input is information acquired from the knowledge database, and the output is information displayed on the smart device. Specifically, the server converts the information into an appropriate format and displays it on the smart device's display.

[1071] Step 7:

[1072] When a user issues an instruction to a robot, the instruction is sent to the robot through a smart device. The input is instruction data from the user, and the output is instruction data sent to the robot.

[1073] Step 8:

[1074] The robot transports necessary parts and automates simple tasks based on received instructions. The input is instruction data from the user, and the output is the results of the performed work. Specifically, the robot operates according to the instructions and performs the specified task.

[1075] This allows users to quickly obtain the information they need, allowing them to efficiently troubleshoot and find the optimal work procedure. It also strengthens collaboration with the robot, improving overall productivity.

[1076] Example 2

[1077] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1078] Conventional medical information search systems make it difficult for users to efficiently obtain the information they need. Furthermore, when multiple users search for the same information, they have to repeat the same search process each time, which is time-consuming and labor-intensive. Furthermore, there is a lack of a way to effectively utilize accumulated knowledge, and improvements in convenience and operational efficiency are needed.

[1079] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1080] In this invention, the server includes: means for accumulating high-precision results generated by users in a knowledge database via a knowledge generation platform; means for providing the accumulated knowledge to users in the form of an ecosystem; means for improving convenience and work efficiency; means for users to search for medical information and send the search results to the knowledge generation platform; and means for retrieving the relevant information from the knowledge database and providing it to another user when another user needs similar information. This allows users to efficiently obtain the medical information they need, reducing the time and effort required when multiple users search for the same information. Furthermore, by effectively utilizing the accumulated knowledge, convenience and work efficiency can be improved.

[1081] "User" means an individual or organization that uses the System to retrieve medical information.

[1082] "High-precision results" are accurate and reliable information that users receive when they search.

[1083] A "knowledge generation platform" is a system for processing search results and storing them in a knowledge database.

[1084] A "knowledge database" is a database system for storing and managing accumulated medical information and other knowledge.

[1085] An "ecosystem" is an overall system environment that provides accumulated knowledge to users and allows them to mutually utilize it.

[1086] "Improving convenience" means enabling users to quickly and easily obtain the information they need.

[1087] "Improved business efficiency" refers to the ability to increase business efficiency and reduce time and effort by using the system.

[1088] "Medical information" refers to all information related to medical care, such as disease treatment methods, symptoms, and drug information.

[1089] "Search results" are a list of information obtained when a user performs a search.

[1090] "Send" refers to the act of sending data from a terminal to a server.

[1091] "Retrieval" refers to the act of the server retrieving the necessary information from the knowledge database.

[1092] "Providing" refers to the act of displaying the information acquired by the server to the user.

[1093] The present invention is a system for enabling a user to efficiently obtain medical information. A specific embodiment of this system will be described below.

[1094] System configuration

[1095] The system mainly consists of the following components:

[1096] User's device (PC, smartphone, etc.)

[1097] server

[1098] Knowledge Generation Platform

[1099] Knowledge Database

[1100] Hardware and software used

[1101] Device: A device such as a PC or smartphone. Searches are performed using a web browser (e.g., Google Chrome, Mozilla Firefox).

[1102] Server: Receives and processes search queries. Uses a web server (e.g., Apache HTTP Server, Nginx) and a database management system (e.g., MySQL, PostgreSQL).

[1103] Knowledge generation platform: Processes search results and stores them in a knowledge database. Uses a data streaming platform (e.g., Apache Kafka).

[1104] Knowledge database: Stores and manages accumulated medical information. Uses a database management system (e.g., MySQL, PostgreSQL).

[1105] System Operation

[1106] 1. A user searches for medical information

[1107] Users open a web browser on their PC or smartphone and access a search engine or dedicated medical information search site.

[1108] Enter a keyword such as "diabetes treatment" in the search box and click the search button.

[1109] 2. The device sends the search results to the server

[1110] The terminal transmits the generated search query to the server as an HTTP request.

[1111] Example: A terminal sends a request to a server: "GET / search?q=diabetes treatment HTTP / 1.1".

[1112] 3. The server processes the data in the knowledge generation platform

[1113] The server analyzes the received search query and collects relevant medical information.

[1114] The server transmits the collected information to the knowledge generation platform.

[1115] Example: A server collects information on "diabetes treatment" and sends it to a knowledge generation platform.

[1116] 4. The server stores the data in a knowledge database

[1117] The knowledge generation platform stores the received information in a knowledge database.

[1118] Example: A knowledge database stores information about "treatments for diabetes."

[1119] 5. Another user searches for similar information

[1120] Another user similarly searches for medical information using a PC or smartphone.

[1121] Enter "diabetes cure" in the search box and click the search button.

[1122] 6. The server retrieves the relevant information from the knowledge database and provides it to the user.

[1123] The server searches and acquires the relevant information from the knowledge database.

[1124] The server sends the acquired information to the user's terminal as an HTTP response.

[1125] Example: The server retrieves information about "diabetes treatment" from a knowledge database and displays it to the user.

[1126] Examples of concrete examples and prompts

[1127] Specific examples

[1128] User A searches for "treatment of high blood pressure" and the results are stored in the knowledge database.

[1129] When user B searches for "treatment of high blood pressure" at a later date, information is retrieved from the knowledge database and provided to user B.

[1130] Prompt Sentence Examples

[1131] "What's the latest information on treatments for high blood pressure?"

[1132] "Please provide detailed information on diabetes treatment."

[1133] This system allows users to efficiently obtain the medical information they need. It also reduces the time and effort required when multiple users search for the same information. By effectively utilizing the accumulated knowledge, convenience and work efficiency can be improved.

[1134] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1135] Step 1:

[1136] Users search for medical information

[1137] Input: A user uses a PC or smartphone, opens a web browser, and accesses a search engine or dedicated medical information search site. They enter a keyword such as "diabetes treatment" in the search box and click the search button.

[1138] Data processing: A search query is generated.

[1139] Output: The generated search query is saved on the device.

[1140] Specific operation: A user types "diabetes cure" and clicks the search button, generating a search query.

[1141] Step 2:

[1142] The device sends the search results to the server

[1143] Input: The generated search query.

[1144] Data processing: The device sends the search query to the server as an HTTP request.

[1145] Output: The server receives the search query.

[1146] Specific operation: The terminal sends the request "GET / search?q=diabetes treatment HTTP / 1.1" to the server.

[1147] Step 3:

[1148] The server processes the data in the knowledge generation platform

[1149] Input: The search query received by the server.

[1150] Data processing: The server analyzes the search query and collects relevant medical information, which is then sent to the knowledge generation platform.

[1151] Output: The knowledge generation platform receives the information.

[1152] Specific operation: The server collects information on "diabetes treatment methods" and sends it to the knowledge generation platform.

[1153] Step 4:

[1154] The server stores the data in a knowledge database

[1155] Input: Information received by the knowledge generation platform.

[1156] Data processing: The knowledge generation platform stores the received information in a knowledge database.

[1157] Output: Information is stored in a knowledge database.

[1158] Specific operation: Information about "treatments for diabetes" is stored in the knowledge database.

[1159] Step 5:

[1160] Another user searches for similar information

[1161] Input: Another user uses a PC or smartphone, opens a web browser, accesses a search engine or dedicated medical information search site, types "diabetes treatment" in the search box, and clicks the search button.

[1162] Data processing: A search query is generated.

[1163] Output: The generated search query is saved on the device.

[1164] Specific behavior: Another user types "diabetes cure" and clicks the search button, generating a search query.

[1165] Step 6:

[1166] The server retrieves the relevant information from the knowledge database and provides it to the user.

[1167] Input: The search query received by the server.

[1168] Data processing: The server searches for and retrieves the relevant information from the knowledge database, and sends the retrieved information to the user's device as an HTTP response.

[1169] Output: Information is displayed on the user's terminal.

[1170] Specific operation: The server retrieves information about "diabetes treatment" from the knowledge database and displays it to the user.

[1171] (Application example 2)

[1172] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1173] In modern brick-and-mortar stores, it is difficult for users to efficiently obtain product information. In particular, when users want to know detailed information or stock status about a specific product, they must inquire with a store clerk, which takes time and effort. Furthermore, when multiple users request the same information, duplicate information acquisition occurs, which is inefficient. Furthermore, conventional search systems and chatbots have difficulty providing information in real time, which reduces user convenience.

[1174] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the accumulated knowledge to users in the form of an ecosystem, means for improving convenience and business efficiency, and means for providing product information in real time using smart devices in physical stores. This allows users to improve their shopping experience in physical stores and obtain the product information they need quickly and efficiently.

[1175] "User" means any person or entity that uses the System to search for and retrieve information.

[1176] "Highly Accurate Results" are search results that provide accurate and relevant information in response to a user's search query.

[1177] A "knowledge generation platform" is a system for collecting information generated by users and storing it in a knowledge database.

[1178] A "knowledge database" is a database that stores collected information and provides it to users as needed.

[1179] An "ecosystem" is an integrated system environment for providing users with information stored in a knowledge database.

[1180] "Improving convenience" means enabling users to obtain the information they need quickly and efficiently.

[1181] "Improving business efficiency" refers to preventing duplicate information acquisition and increasing the efficiency of the entire system.

[1182] A "brick and mortar store" is a store that sells products in a physical location.

[1183] A "smart device" is an electronic device that is connected to the Internet and can display and operate information.

[1184] "Real-time" refers to information being obtained and provided immediately.

[1185] The present invention relates to a system for providing real-time product information using smart devices in a physical store. Specific embodiments of this system are described below.

[1186] System Configuration

[1187] The system consists of the following main components:

[1188] 1. Knowledge generation platform: Collects high-precision results generated by users and stores them in a knowledge database.

[1189] 2. Knowledge database: Stores accumulated information and makes it available when needed.

[1190] 3. Smart Devices: Devices used by customers in physical stores (e.g., smart glasses).

[1191] 4. Ecosystem: An integrated system that provides users with information stored in a knowledge database.

[1192] Program processing

[1193] The server accumulates the highly accurate results generated by users in a knowledge database via a knowledge generation platform. Specifically, when a user searches for product information using a smart device, the search results are sent to the knowledge generation platform. The knowledge generation platform analyzes the search results and stores them in the knowledge database.

[1194] Next, when another user needs information about the same product, they access the knowledge database through their smart device to retrieve the relevant information, which is then displayed in real time on the smart device's display.

[1195] Hardware and software used

[1196] Hardware: Smart glasses, server

[1197] Software: Knowledge generation platform, knowledge database, API

[1198] Specific examples

[1199] For example, if a user is searching for "medical masks" in a physical store, the user wearing smart glasses can search for "medical masks" by voice, and the smart glasses' display will show detailed information about the masks and their stock availability. This information is retrieved in real time from the knowledge database.

[1200] Prompt Sentence Examples

[1201] "Search for information on medical masks."

[1202] "Get medical mask information from our knowledge database."

[1203] "Display medical mask information on smart glasses."

[1204] In this way, users can improve their shopping experience in physical stores and obtain the product information they need quickly and efficiently.

[1205] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1206] Step 1:

[1207] A user searches for product information using a smart device (e.g., smart glasses). The user inputs a search query through voice input or touch operation. The input query is sent from the smart device to a server. The input data is the search query, and the output data is the search request sent to the server.

[1208] Step 2:

[1209] The server sends the received search query to the knowledge generation platform. The knowledge generation platform analyzes the search query and generates relevant high-precision results. The input data is the search query, and the output data is the high-precision results. In particular, the knowledge generation platform analyzes the search query and retrieves relevant information from the database.

[1210] Step 3:

[1211] The knowledge generation platform stores the generated high-precision results in a knowledge database. The input data is the high-precision results, and the output data is the information stored in the knowledge database. In particular, the knowledge generation platform writes the high-precision results into the database.

[1212] Step 4:

[1213] If another user wants to search for information about the same product, they will use their smart device to enter a search query again. The entered query will be sent from the smart device to the server. The input data is the search query, and the output data is the search request sent to the server.

[1214] Step 5:

[1215] The server accesses the knowledge database to retrieve relevant information. The input data is a search query, and the output data is the information retrieved from the knowledge database. Specifically, the server sends a query to the knowledge database to retrieve relevant information.

[1216] Step 6:

[1217] The server sends the acquired information to the smart device. The input data is the information acquired from the knowledge database, and the output data is the information sent to the smart device. Specifically, the server transfers the acquired information to the smart device.

[1218] Step 7:

[1219] The smart device displays the received information to the user. The input data is the information sent from the server, and the output data is the information displayed on the smart device's display. Specifically, the smart device displays the received information on the display.

[1220] In this way, users can improve their shopping experience in physical stores and obtain the product information they need quickly and efficiently.

[1221] Example 3

[1222] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1223] With conventional information search systems, it was difficult for users to quickly obtain the information they needed, and information searches could take a long time. In addition, search results often did not necessarily meet the user's needs, which led to problems with the inability to improve work efficiency. Furthermore, conventional systems lacked a means to effectively utilize accumulated knowledge, making it difficult to reuse knowledge.

[1224] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1225] In this invention, the server includes means for accumulating high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the accumulated knowledge to users in the form of an ecosystem, means for receiving and analyzing queries sent by users from their terminals, means for searching for related information from the knowledge database based on the analyzed queries, means for inputting the search results into a generative AI model as prompt sentences to generate appropriate answers, and means for providing the generated answers to users. This allows users to quickly obtain the information they need, shortening information search time and improving work efficiency.

[1226] "User" means an individual or organization that uses the system to search for information and obtain the data they need.

[1227] "High-accuracy results" are search results that provide accurate and relevant information in response to a user's query.

[1228] A "knowledge generation platform" is a system for collecting information generated by users and storing it in a knowledge database.

[1229] A "knowledge database" is a database that stores accumulated information and knowledge and can be searched and retrieved as needed.

[1230] An "ecosystem" is the overall structure of a system that provides accumulated knowledge to users and promotes the reuse and sharing of information.

[1231] A "terminal" is a device (e.g., a PC or smartphone) through which a user inputs a query and communicates with the server.

[1232] A "query" is a question or request that a user enters into a system to retrieve information.

[1233] A "server" is a computer system that receives and analyzes queries, retrieves information from a knowledge database, and generates answers using generative AI models.

[1234] A "generative AI model" is an artificial intelligence model that generates appropriate answers in natural language based on an input prompt sentence.

[1235] A "prompt sentence" is an instruction sentence input to a generative AI model, and is the sentence that serves as the basis for the model to generate an appropriate answer.

[1236] MODE FOR CARRYING OUT THE INVENTION

[1237] The present invention is a system for enabling users to quickly obtain the information they need and improving work efficiency. A specific embodiment of this system will be described below.

[1238] System configuration

[1239] Hardware

[1240] Server: High-performance server (e.g. general-purpose server)

[1241] Device: The device used by the user (e.g., computer, smartphone)

[1242] software

[1243] Database Management System: Software for managing knowledge databases (e.g., MySQL)

[1244] Generative AI model: An artificial intelligence model that performs natural language processing (e.g., a general-purpose generative AI model)

[1245] Program processing

[1246] The server receives and analyzes queries sent from the device. Based on the analyzed queries, the server searches for relevant information from a knowledge database. The search results are input as prompt sentences into the generative AI model, which generates an appropriate answer. The generated answer is sent from the server to the device and provided to the user.

[1247] Specific examples

[1248] For example, consider the case where a user enters the query "Tell me about the latest project management tools."

[1249] 1. Query reception: A user sends a query from their device saying, "Please tell me about the latest project management tools."

[1250] 2. Query analysis: The server analyzes the query and searches the knowledge database for information about "project management tools."

[1251] 3. Information retrieval: The server uses MySQL to retrieve relevant information from the knowledge database.

[1252] 4. Use of generative AI model: The server inputs the acquired information into the generative AI model and formats the information in a form that is easy for the user to understand.

[1253] 5. Information provision: The server sends the formatted information to the user's terminal.

[1254] An example of a prompt to be input to the generative AI model would be, "Please provide information about the latest project management tools. Please use the following data as a basis," followed by information retrieved from the knowledge database.

[1255] In this way, the user can quickly obtain the necessary information, shortening the time required for searching information and improving work efficiency. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1256] Step 1:

[1257] The user enters a query.

[1258] The user inputs the information they want to obtain from their device (e.g., a PC or smartphone). For example, they might input, "Tell me about the latest project management tools." The input query is saved in an input field on the device.

[1259] Step 2:

[1260] The device sends a query to the server.

[1261] The terminal sends the query entered by the user to the server as an HTTP request, and data is sent over the Internet. The input is the user's query, and the output is an HTTP request to the server.

[1262] Step 3:

[1263] The server receives and parses the query.

[1264] The server receives the HTTP request sent from the terminal. It analyzes the received query and identifies the type of information the user is seeking. The input is the HTTP request, and the output is the analyzed query. Specifically, it recognizes the query content as information about the "project management tool."

[1265] Step 4:

[1266] The server retrieves relevant information from a knowledge database.

[1267] The server searches for relevant information from a knowledge database based on the analysis results. For example, it uses a database management system (e.g., MySQL) to retrieve the latest information on "project management tools." The input is the analyzed query, and the output is the search results. Specifically, it executes the query "SELECT FROM knowledge_db WHERE topic='project management tools' ORDER BY date DESC LIMIT 1."

[1268] Step 5:

[1269] The server inputs a prompt sentence into the generative AI model.

[1270] The server inputs a prompt to the generative AI model based on information obtained from the knowledge database. An example of a prompt is, "Please provide information about the latest project management tools. Please use the following data as a basis." The input is the search results, and the output is a prompt to the generative AI model.

[1271] Step 6:

[1272] A generative AI model generates the appropriate answer.

[1273] The generative AI model generates an appropriate answer based on the input prompt. The generated answer is expressed in natural language that is easy for users to understand. The input is the prompt, and the output is the generated answer. Specifically, it generates an answer such as, "Modern project management tools help with task management, project progress visualization, and team collaboration."

[1274] Step 7:

[1275] The server sends the generated answer to the user.

[1276] The server sends the answer obtained from the generative AI model to the user's device. At this time, data is sent over the Internet. The input is the generated answer, and the output is an HTTP response to the user's device. Specifically, the answer is sent in JSON format.

[1277] Step 8:

[1278] The user receives the answer.

[1279] The user receives the response from the server on their device and checks the displayed answer. This allows the user to quickly obtain the information they need. The input is the HTTP response from the server, and the output is the answer displayed on the user's device.

[1280] (Application example 3)

[1281] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1282] With conventional systems, it was difficult for users to quickly obtain the information they needed, and there were issues with time-consuming information searches, particularly in the maintenance and troubleshooting of factory robots, which reduced work efficiency.In addition, there was a lack of means to provide appropriate information for error codes and maintenance requests, making it difficult to respond quickly.

[1283] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1284] In this invention, the server includes means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the stored knowledge to users in the form of an ecosystem, means for improving convenience and work efficiency, and means for the robot to detect error codes and maintenance requests and search for and provide related information from the knowledge database. This enables users to quickly obtain the information they need, enabling quick and appropriate responses to factory robot maintenance and troubleshooting.

[1285] "User" refers to an individual or organization that uses the system to obtain information and perform business.

[1286] "High accuracy results" refers to user-generated information and data that is accurate and reliable.

[1287] A "knowledge generation platform" refers to a system or tool that collects information generated by users and stores it in a knowledge database.

[1288] A "knowledge database" refers to a database that systematically stores accumulated information and knowledge and allows it to be searched and provided as needed.

[1289] An "ecosystem" refers to an integrated system or environment for providing users with information stored in a knowledge database.

[1290] "Improving convenience" refers to increasing the ease and comfort of use for users when using the system.

[1291] "Improving business efficiency" refers to reducing the time and effort required to carry out business operations and increasing productivity.

[1292] A "robot" refers to an automated mechanical device used in factories and other places.

[1293] An "error code" refers to an identification number or string of characters that is displayed when a robot or system detects an abnormality.

[1294] "Maintenance Request" refers to a notification or instruction issued when a robot or system requires routine maintenance or repair.

[1295] "Related information" refers to specific measures and procedures that address error codes and maintenance requests.

[1296] As an embodiment of the present invention, a "smart maintenance assistant" system installed in a factory robot will be described as an example.

[1297] System Program

[1298] The system consists of the following main components:

[1299] 1. Knowledge Generation Platform

[1300] 2. Knowledge Database

[1301] 3. Ecosystem

[1302] 4. Robot error detection and maintenance request detection function

[1303] Program processing explanation

[1304] Hardware used

[1305] Factory robots (e.g., general industrial robots)

[1306] Robot-mounted computer

[1307] Software used

[1308] Python Program

[1309] Knowledge database (database management system)

[1310] Data processing and calculation

[1311] 1. Knowledge Generation Platform

[1312] The server accumulates the high-precision results generated by users in a knowledge database via a knowledge generation platform, which allows the information generated by users to be systematically stored.

[1313] 2. Knowledge Database

[1314] The server provides users with the accumulated knowledge in the form of an ecosystem. The knowledge database includes maintenance information and troubleshooting guides.

[1315] 3. Ecosystem

[1316] Through the ecosystem, the server quickly provides the information users need, thereby improving convenience and operational efficiency.

[1317] 4. Robot error detection and maintenance request detection function

[1318] The robot detects error codes and maintenance requests and provides relevant information from a knowledge database, enabling fast and accurate robot maintenance and troubleshooting.

[1319] Specific examples

[1320] For example, if the robot detects "error_101," it retrieves the information "Please check the belt tension" from the knowledge database and displays it on the robot's display.

[1321] Prompt Sentence Examples

[1322] "When the robot detects an error code, retrieve the maintenance information corresponding to that error code from the knowledge database and display it."

[1323] The above is an embodiment of the present invention. This system allows users to quickly obtain the necessary information, enabling quick and appropriate responses in maintenance and troubleshooting of factory robots.

[1324] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1325] Step 1:

[1326] The server accumulates the high-precision results generated by users in a knowledge database via a knowledge generation platform.

[1327] Input: User-generated high-precision results

[1328] Data processing: High-precision results are formalized in the knowledge generation platform and stored in the knowledge database.

[1329] Output: High-precision results stored in a knowledge database

[1330] Specific operation: The server receives the data entered by the user and stores it in the database in the appropriate format.

[1331] Step 2:

[1332] The server provides the accumulated knowledge to users in the form of an ecosystem.

[1333] Input: Information stored in the knowledge database

[1334] Data processing: Search for necessary information from the knowledge database and provide it through the ecosystem

[1335] Output: Knowledge information provided to the user

[1336] Specific operation: When a user requests information, the server searches for the relevant information from the database and provides it to the user through the ecosystem.

[1337] Step 3:

[1338] The robot detects error codes and maintenance requests.

[1339] Input: Data from the robot's sensors and internal systems

[1340] Data processing: Analyze and identify error codes and maintenance requests

[1341] Output: Detected error code and maintenance request

[1342] Specific operation: When the robot detects an abnormality during operation, it analyzes data from sensors and internal systems to identify error codes and maintenance requests.

[1343] Step 4:

[1344] The server searches for relevant information from a knowledge database based on the detected error code or maintenance request.

[1345] Input: Detected error code or maintenance request

[1346] Data processing: Search for relevant information from the knowledge database

[1347] Output: Related information

[1348] Specific operation: The server receives the error code or maintenance request and searches the knowledge database for corresponding information.

[1349] Step 5:

[1350] The server provides the retrieved relevant information to the robot.

[1351] Input: Relevant information retrieved from a knowledge database

[1352] Data processing: converting information into a format that a robot can understand

[1353] Output: Relevant information provided to the robot

[1354] Specific operation: The server sends the searched information to the robot, which displays it on the robot's display and outputs audio.

[1355] Step 6:

[1356] The robot performs maintenance and troubleshooting based on the information provided.

[1357] Input: Relevant information provided by the server

[1358] Data processing: Use the information to perform specific maintenance and troubleshooting procedures

[1359] Output: Maintenance and troubleshooting completed

[1360] Specific behavior: The robot performs necessary maintenance tasks or troubleshooting according to the information provided.

[1361] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1362] "Example 1"

[1363] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system includes a means for storing highly accurate results generated by users in a knowledge database via a knowledge generation platform, a means for providing the stored knowledge to users in the form of an ecosystem, and a means for improving convenience and business efficiency. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, the emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc., and adjusts the operation of the knowledge generation platform according to the emotions. For example, if the user feels angry, the emotion engine sends an instruction to the knowledge generation platform to prioritize providing knowledge that will quickly resolve the user's problem.

[1364] "Example 2"

[1365] Another embodiment of the present invention provides a system in which an emotion engine selects knowledge to provide based on a user's emotion. This system includes a means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, a means for providing the stored knowledge to users in the form of an ecosystem, and a means for improving convenience and business efficiency. Furthermore, this system includes an emotion engine that recognizes the user's emotion. Specifically, the emotion engine recognizes the user's emotion and selects knowledge to provide based on that emotion. For example, if the user feels happy, the emotion engine sends an instruction to the knowledge generation platform to provide knowledge that matches the user's interests and preferences.

[1366] "Example 3"

[1367] As a further embodiment of the present invention, a system is provided in which an emotion engine adjusts the operation of a knowledge generation platform in response to a user's emotion. The system includes a means for storing high-precision results generated by users in a knowledge DB via the knowledge generation platform, a means for providing the stored knowledge to users in the form of an ecosystem, and a means for improving convenience and work efficiency. The system further includes an emotion engine that recognizes the user's emotion. Specifically, the emotion engine recognizes the user's emotion and adjusts the operation of the knowledge generation platform in response to the emotion. For example, if the user feels confused, the emotion engine sends an instruction to the knowledge generation platform to quickly provide the information the user needs.

[1368] The processing flow of each embodiment will be described below.

[1369] "Example 1"

[1370] Step 1: A user uses the system to search or query for information.

[1371] Step 2: The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc.

[1372] Step 3: Adjust the behavior of the knowledge generation platform based on the emotions recognized by the emotion engine.

[1373] Step 4: The knowledge generation platform prioritizes and provides knowledge to quickly resolve users' problems.

[1374] "Example 2"

[1375] Step 1: A user uses the system to search or query for information.

[1376] Step 2: The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc.

[1377] Step 3: Select the knowledge to provide based on the emotion recognized by the emotion engine.

[1378] Step 4: The knowledge generation platform provides knowledge that matches the user's interests and preferences.

[1379] "Example 3"

[1380] Step 1: A user uses the system to search or query for information.

[1381] Step 2: The emotion engine recognizes emotions from the user's tone of voice, facial expressions, text input, etc.

[1382] Step 3: Adjust the behavior of the knowledge generation platform based on the emotions recognized by the emotion engine.

[1383] Step 4: The knowledge generation platform quickly provides users with the information they need.

[1384] Example 1

[1385] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1386] Information obtained using conventional search engines and chatbots is only used temporarily for individual users and is rarely accumulated as useful knowledge for other or future users. Furthermore, because information is provided without considering the user's feelings, user satisfaction and work efficiency can decline. To solve these issues, a system is needed that efficiently accumulates highly accurate information obtained by users and provides appropriate information according to their feelings.

[1387] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1388] In this invention, the server includes means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the stored knowledge to users in the form of an ecosystem, means for improving convenience and business efficiency, means including an emotion engine for recognizing user emotions, and means for adjusting the operation of the knowledge generation platform in accordance with the emotions recognized by the emotion engine. This makes it possible to efficiently store high-precision information obtained by users and provide appropriate information in accordance with their emotions.

[1389] "User" means an individual or organization that uses the System to search for information or obtain information using a chatbot.

[1390] "High-precision results" are accurate and reliable information that users obtain through search engines and chatbots.

[1391] A "knowledge generation platform" is a system that receives information obtained by users, analyzes it, and stores it in a knowledge database.

[1392] A "knowledge database" is a database that accumulates knowledge in various fields and provides it as needed.

[1393] An "ecosystem" is an entire system that provides accumulated knowledge to users and enables them to utilize that knowledge.

[1394] "Improving convenience" refers to increasing the ease and efficiency of using the system.

[1395] "Improving business efficiency" refers to increasing the efficiency in carrying out business.

[1396] The "emotion engine" is an engine that recognizes emotions from the user's tone of voice, facial expressions, text input, etc., and adjusts the system's behavior according to those emotions.

[1397] "Adjusting behavior" refers to changing the behavior of the knowledge generation platform based on the emotions recognized by the emotion engine.

[1398] This invention is a system that accumulates highly accurate results obtained by users using conventional search engines and chatbots in a knowledge database via a knowledge generation platform, and provides the accumulated knowledge to users in the form of an ecosystem. Furthermore, by combining it with an emotion engine, knowledge is provided according to the user's emotions.

[1399] System configuration

[1400] 1. User operations

[1401] Users use the devices to input information into search engines and chatbots.

[1402] Example: User types, "Tell me about the latest AI technology."

[1403] 2. Device Operation

[1404] The terminal transmits the information input by the user to the knowledge generation platform.

[1405] Software used: A library for sending HTTP requests (e.g., Axios)

[1406] 3. Server Operation

[1407] The server analyzes the received information and stores it in a knowledge database.

[1408] Hardware used: Server (e.g. cloud service)

[1409] Software used: Database management system (e.g. MySQL)

[1410] 4. Operation of the Emotion Engine

[1411] The emotion engine recognizes emotions from the user's tone of voice, facial expressions, and text input.

[1412] Software used: Emotion recognition library (e.g. OpenCV, NLP library)

[1413] Example: If a user types "This problem won't go away!", the emotion engine will recognize that the user is angry.

[1414] 5. Coordinating knowledge delivery

[1415] The server receives instructions from the emotion engine and adjusts the knowledge provided according to the user's emotions.

[1416] Example: If a user is angry, the server can quickly provide knowledge to help resolve the issue.

[1417] 6. Providing knowledge

[1418] The server provides the accumulated knowledge to users in the form of an ecosystem.

[1419] Software used: Web application framework (e.g., Django)

[1420] Specific examples

[1421] User searches or enters information into the chatbot

[1422] A user types, "Tell me about the latest AI technology."

[1423] The user types, "This problem persists!"

[1424] The terminal sends information to the knowledge generation platform

[1425] The device sends an HTTP request containing the information "Tell me about the latest AI technology."

[1426] The device sends an HTTP request containing the information "This problem persists!"

[1427] The server analyzes the information and stores it in a knowledge database

[1428] The server analyzes the information "Tell me about the latest AI technology" and stores it in a knowledge database.

[1429] The server analyzes the information "This problem cannot be solved!" and stores it in a knowledge database.

[1430] Emotion engine recognizes user emotions

[1431] The emotion engine recognizes that the user is angry from the text "This problem won't go away!"

[1432] The emotion engine recognizes that the user is sad from their facial expression.

[1433] The server adjusts knowledge provision according to emotions.

[1434] The server receives information that "the user is angry" and provides high-priority knowledge.

[1435] The server receives information that "the user is sad" and provides comforting knowledge.

[1436] The server provides knowledge to the user

[1437] The server displays the knowledge "Tell me about the latest AI technology" on a web page.

[1438] The server displays the knowledge "This problem is not solved!" on a web page.

[1439] This system allows users to efficiently obtain highly accurate information and receive appropriate support according to their emotions.

[1440] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1441] Step 1:

[1442] A user searches for or enters information into a chatbot.

[1443] Input: A user uses a device to enter information into a search engine or chatbot.

[1444] What happens: A user opens a browser and types "Tell me about the latest AI technology" into the search bar.

[1445] Output: Information entered by the user is sent to the terminal.

[1446] Step 2:

[1447] The terminal transmits the information to the knowledge generation platform.

[1448] Input: Information entered by the user.

[1449] Specific operation: The terminal generates an HTTP request and sends data including the input information to the server. The library used is Axios.

[1450] Output: The information entered by the user is sent to the knowledge generation platform.

[1451] Step 3:

[1452] The server analyzes the information and stores it in a knowledge database.

[1453] Input: Information sent from the device.

[1454] Specific operation: The server runs a Python script to analyze the received data and saves the analysis results in a MySQL database.

[1455] Output: The analyzed information is stored in a knowledge database.

[1456] Step 4:

[1457] The emotion engine recognizes the user's emotions.

[1458] Input: User tone of voice, facial expressions, and text input.

[1459] What it does: The emotion engine uses OpenCV to analyze the user's facial expressions and NLP libraries to extract emotions from text input.

[1460] Output: The user's emotion is recognized.

[1461] Step 5:

[1462] The server adjusts the knowledge provision according to emotions.

[1463] Input: Emotion recognition results from the emotion engine.

[1464] Specific operation: The server receives information from the emotion engine that "the user is angry," searches for high-priority knowledge, and provides it.

[1465] Output: Knowledge provision instructions are generated according to the emotion.

[1466] Step 6:

[1467] The server provides knowledge to the user.

[1468] Input: Information stored in the knowledge database and instructions from the emotion engine.

[1469] What happens: The server uses the Django framework to generate a web page and display the knowledge to the user.

[1470] Output: The knowledge provided to the user is displayed on a web page.

[1471] (Application example 1)

[1472] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1473] Conventional knowledge management systems have had difficulty efficiently storing and appropriately providing information generated by users. They also lack the ability to respond to users' emotions, making it difficult to improve customer satisfaction. Furthermore, in brick-and-mortar stores, there was a lack of a way for staff to provide appropriate knowledge in real time when dealing with customers.

[1474] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for storing high-precision results generated by users in a knowledge DB via a knowledge generation platform, means for providing the stored knowledge to users in the form of an ecosystem, means for improving convenience and business efficiency, means for recognizing the user's emotions using an emotion engine and adjusting the operation of the knowledge generation platform according to those emotions, and means for providing knowledge in real time via a smart device. This makes it possible to provide appropriate knowledge according to the user's emotions, thereby improving the quality of customer service in physical stores.

[1475] "User" means an individual or entity that uses the system to generate information and acquire knowledge.

[1476] "High-precision results" are accurate and reliable information that users obtain through searches and chatbots.

[1477] The "knowledge generation platform" is a system for collecting information generated by users and storing it in a knowledge database.

[1478] A "knowledge DB" is a database that accumulates knowledge in various fields.

[1479] An "ecosystem" is an integrated system environment for providing accumulated knowledge to users.

[1480] "Improving convenience" refers to making it easier and more efficient for users to use the system.

[1481] "Improving business efficiency" refers to increasing business productivity and efficiency by using a system.

[1482] The "emotion engine" is a system that recognizes emotions from the user's tone of voice, facial expressions, text input, etc.

[1483] "Smart devices" are devices with internet connectivity such as smartphones, smart glasses, and head-mounted displays.

[1484] "Real-time" refers to immediate processing or response without delay.

[1485] As an embodiment of this invention, we will explain a customer service system using smart devices in a brick-and-mortar store. This system accumulates high-precision results generated by users in a knowledge database via a knowledge generation platform and provides the accumulated knowledge to users in the form of an ecosystem. Furthermore, it uses an emotion engine to recognize the user's emotions and adjusts the operation of the knowledge generation platform according to those emotions.

[1486] Hardware and software used

[1487] Hardware: Smart glasses (including camera, microphone, and display)

[1488] software:

[1489] EmotionRecognizer: A library for recognizing emotions from facial expressions

[1490] KnowledgeDB: A library for accessing the Knowledge DB and getting answers to questions.

[1491] SmartGlassesInterface: An interface for operating the camera, microphone, and display of smart glasses.

[1492] System Operation

[1493] The server captures the customer's face through the smart glasses' camera and uses EmotionRecognizer to recognize emotions from facial expressions. It then receives the customer's voice input question through the smart glasses' microphone and sends the question to the KnowledgeDB to obtain an appropriate answer. The knowledge generation platform then adjusts its behavior based on the recognized emotion, providing appropriate knowledge first. Finally, the answer is displayed on the smart glasses' display.

[1494] Specific examples

[1495] For example, if a customer asks a question about a product in a store and looks a little anxious, the emotion engine will recognize this. The server will then prioritize answers that will give the customer a sense of security from the knowledge database and display them on the smart glasses' display. This allows the customer to choose a product with peace of mind.

[1496] Prompt Sentence Examples

[1497] "If your customer looks anxious, provide a reassuring response."

[1498] In this way, the quality of customer service in physical stores can be improved.

[1499] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1500] Step 1:

[1501] The server captures the customer's face through the camera of the smart glasses. The input is the camera image and the output is the captured image data. This image data is sent to EmotionRecognizer.

[1502] Step 2:

[1503] The server recognizes the customer's emotions from the captured image data using EmotionRecognizer. The input is the image data, and the output is the recognized emotion data. EmotionRecognizer analyzes facial expressions and identifies emotions.

[1504] Step 3:

[1505] The server receives the customer's voice input of the question through the microphone of the smart glasses. The input is voice data and the output is text data. The server uses voice recognition software to convert the voice data into text.

[1506] Step 4:

[1507] The server sends the acquired text data to KnowledgeDB to obtain the appropriate answer. The input is text data and emotion data, and the output is the answer text. KnowledgeDB searches for answers to the question and provides appropriate answers preferentially based on the emotion data.

[1508] Step 5:

[1509] The server displays the obtained answer text on the display of the smart glasses. The input is the answer text and the output is the text displayed on the display. The answer is displayed using the display interface of the smart glasses.

[1510] Step 6:

[1511] The user checks the answer displayed on the smart glasses display and responds appropriately to the customer. The input is the text displayed on the display, and the output is the response to the customer. The user provides explanations and guidance to the customer based on the displayed information.

[1512] Example 2

[1513] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1514] Conventional information search systems have the problem that it is difficult for users to efficiently obtain the information they need, and the information provided does not respond to the user's emotions, resulting in low user satisfaction. Furthermore, there is a lack of means to effectively utilize accumulated knowledge, which makes it difficult to improve work efficiency.

[1515] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1516] In this invention, the server includes means for accumulating high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the accumulated knowledge to users in the form of an ecosystem, means for improving convenience and business efficiency, means for receiving user search queries and acquiring related information, means for transmitting the acquired information to the knowledge generation platform, and means for recognizing user emotions using an emotion engine and selecting knowledge to provide based on those emotions. This allows users to efficiently acquire the information they need, makes it possible to provide information based on emotions, and improves user satisfaction. Furthermore, by effectively utilizing the accumulated knowledge, business efficiency can be improved.

[1517] "User" means an individual or entity that uses the System to search for information and receive results.

[1518] "Highly accurate results" are search results that contain highly relevant and accurate information in response to a user's search query.

[1519] A "knowledge generation platform" is a system for collecting, analyzing, and storing search results and other information in a knowledge database.

[1520] A "knowledge database" is a database that stores accumulated knowledge and information and allows it to be searched and retrieved as needed.

[1521] An "ecosystem" is an integrated system or environment that provides accumulated knowledge to users.

[1522] "Improving convenience" refers to increasing the ease and efficiency with which users use the system.

[1523] "Improving business efficiency" refers to increasing the speed and effectiveness of business processing by using a system.

[1524] A "search query" is a keyword or phrase that a user enters when searching for information.

[1525] An "emotion engine" is a system that recognizes the user's emotions and provides appropriate information according to those emotions.

[1526] "Relevant information" refers to information that is appropriate and useful to a user's search query.

[1527] "Information provision" refers to the system presenting search results and other knowledge to users.

[1528] This invention is a system that, when a user searches for medical information, sends the search results to a knowledge generation platform and stores them in a knowledge database. Furthermore, by using an emotion engine to provide information according to the user's emotions, it improves user satisfaction.

[1529] Hardware and software used

[1530] Hardware

[1531] Device (PC, smartphone, etc.)

[1532] Server (data processing and knowledge database management)

[1533] software

[1534] Knowledge generation platform (data accumulation and provision)

[1535] Emotion engine (recognition of user emotions)

[1536] Search engines (medical information search)

[1537] Program processing

[1538] 1. User search behavior

[1539] A user uses a device to search for medical information. For example, the user opens a browser, types "diabetes cure" in the search bar, and clicks the search button.

[1540] 2. Obtaining and sending search results

[1541] The device sends a search query to the server. Specifically, the device sends the search query "diabetes treatment" to the server as an HTTP request.

[1542] The server processes the search query and retrieves relevant medical information. The server uses databases and external APIs to gather information about "diabetes treatments."

[1543] The server sends the acquired search results to the knowledge generation platform. The server sends the search results in JSON format to the knowledge generation platform.

[1544] 3. Accumulation in the knowledge database

[1545] The knowledge generation platform accumulates the received search results in a knowledge database, analyzes the search results, and stores them in the knowledge database in an appropriate format.

[1546] 4. Providing information to other users

[1547] Another user searches for similar information. Another user uses the device to search again for "diabetes cure."

[1548] The server retrieves the relevant information from the knowledge database. The server sends a query to the knowledge database to retrieve information about "diabetes treatment."

[1549] The server provides the acquired information to the user. The server sends the acquired information to the terminal, and the terminal displays the information to the user.

[1550] 5. Operation of the Emotion Engine

[1551] The emotion engine recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and input content to determine their emotions.

[1552] If the emotion engine senses that the user is happy, it sends instructions to the knowledge generation platform to provide knowledge that matches the user's interests and preferences. If the emotion engine determines that the user is "happy," it sends instructions to the knowledge generation platform to "provide information that matches the user's interests."

[1553] Examples and prompts

[1554] Specific examples

[1555] User A searches for "diabetes treatment."

[1556] The server obtains the relevant medical information and sends it to the knowledge generation platform.

[1557] The knowledge generation platform stores information in a knowledge database.

[1558] When User B searches for "diabetes treatment," the server retrieves information from the knowledge database and provides it to User B.

[1559] If the emotion engine recognizes User B's emotion as "happy," it provides additional information that matches User B's interests.

[1560] Prompt Sentence Examples

[1561] When a user searches for "diabetes treatment," the search results should be sent to a knowledge generation platform and stored in a knowledge database. If another user subsequently searches for similar information, the system should retrieve the relevant information from the knowledge database and provide it to the user. It should also recognize the user's emotions and select the knowledge to provide based on those emotions.

[1562] This invention allows users to efficiently obtain the medical information they need and also provides information tailored to their emotions, thereby improving user satisfaction and work efficiency.

[1563] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1564] Step 1:

[1565] User search behavior

[1566] Input: A user uses a device to enter a search query.

[1567] What happens: A user opens a browser, types "diabetes cure" into the search bar, and clicks the search button.

[1568] Output: The device sends the search query to the server.

[1569] Step 2:

[1570] Getting search results

[1571] Input: The search query sent from the device.

[1572] Specific operation: The device sends the search query "diabetes treatment" to the server as an HTTP request.

[1573] Data processing: The server analyzes the search query and collects relevant medical information from databases and external APIs.

[1574] Output: The server generates the medical information it has acquired as a search result.

[1575] Step 3:

[1576] Submit search results

[1577] Input: Server-generated search results.

[1578] Specific operation: The server sends the search results in JSON format to the knowledge generation platform.

[1579] Output: The knowledge generation platform receives the search results.

[1580] Step 4:

[1581] Accumulation in knowledge database

[1582] Input: Search results received by the knowledge generation platform.

[1583] Specific operation: The knowledge generation platform analyzes the search results and stores them in the knowledge database in an appropriate format.

[1584] Data processing: Search results are organized as structured data and stored in a knowledge database.

[1585] Output: Search results stored in a knowledge database.

[1586] Step 5:

[1587] Providing information to other users

[1588] Input: A query where another user searches for similar information.

[1589] Specific action: Another user uses the device to search for "diabetes cure" again.

[1590] Data processing: The server queries the knowledge database to obtain information on "diabetes treatment."

[1591] Output: The server sends the acquired information to the terminal, which displays the information to the user.

[1592] Step 6:

[1593] Emotion Engine Operation

[1594] Input: User's emotional data (facial expressions and input content).

[1595] Specific operation: The emotion engine analyzes the user's facial expressions and input content to determine their emotions.

[1596] Data calculation: If the emotion engine determines that the user is happy, it sends an instruction to the knowledge generation platform to provide information that matches the user's interests.

[1597] Output: The knowledge generation platform provides additional information that matches the user's interests.

[1598] In this way, the system processes a series of steps, starting with the user's search behavior, acquiring search results, storing them in a knowledge database, providing information to other users, and finally recognizing emotions and optimizing information provision using an emotion engine.

[1599] (Application example 2)

[1600] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1601] Conventional knowledge provision systems make it difficult for users to efficiently obtain the information they need, and they do not provide appropriate information based on their emotions. Furthermore, there is a lack of effective ways to utilize search results for information on electronic payments, so improvements in user convenience and operational efficiency are needed.

[1602] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1603] In this invention, the server includes means for accumulating high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the accumulated knowledge to users in the form of an ecosystem, means for improving convenience and business efficiency, means for selecting knowledge according to the user's emotions using an emotion recognition engine, means for searching for information related to electronic payments and saving the results in the knowledge database, and means for filtering knowledge based on the user's emotions and providing appropriate information. This enables users to efficiently obtain the information they need and provides appropriate information according to their emotions.

[1604] "High-precision results" are accurate and reliable information obtained by users through searches and operations.

[1605] A "knowledge generation platform" is a system for collecting information generated by users and storing it in a knowledge database.

[1606] A "knowledge database" is a database that stores collected information and provides it as needed.

[1607] An "ecosystem" is an integrated system environment for providing accumulated knowledge to users.

[1608] "Improving convenience" refers to increasing the ease and efficiency with which users can use the system.

[1609] "Improving business efficiency" refers to increasing the efficiency of business operations.

[1610] An "emotion recognition engine" is an engine that recognizes the user's emotions and provides appropriate information based on those emotions.

[1611] "Electronic payment" is a payment method that is carried out using the Internet or electronic devices.

[1612] "Filtering" refers to selecting information based on specific conditions.

[1613] The following system configuration will be described as an embodiment of the present invention.

[1614] System Configuration

[1615] Hardware

[1616] server

[1617] Smartphone

[1618] Emotion Recognition Engine (EmotionEngine)

[1619] software

[1620] Knowledge Generation Platform

[1621] Knowledge Database (KnowledgeDB)

[1622] Search Engine API (using the requests library)

[1623] Emotion recognition software (EmotionEngine)

[1624] Program processing

[1625] server

[1626] The server accumulates highly accurate results generated by users in a knowledge database via a knowledge generation platform. Specifically, when a user searches for information about electronic payments, the search results are sent to the knowledge generation platform and stored in the knowledge database. It also uses an emotion recognition engine to recognize the user's emotions, filter knowledge based on those emotions, and provide appropriate information.

[1627] Smartphone

[1628] The smartphone provides an interface for users to search for information about electronic payments. The search results are sent to a knowledge generation platform and stored in a knowledge database. If the user needs the same information again, an emotion recognition engine recognizes the user's emotions and provides appropriate knowledge.

[1629] Emotion Recognition Engine

[1630] The emotion recognition engine analyzes the user's voice and facial expression data to recognize their emotions, and then filters and provides appropriate information from a knowledge database based on the recognized emotions.

[1631] Specific examples

[1632] For example, if a user searches for information on "electronic payment security," the search results are stored in a knowledge database. When another user needs the same information, if the emotion recognition engine recognizes the user's emotion as "happy," positive information will be provided first.

[1633] Prompt Sentence Examples

[1634] When a user searches for information about "electronic payment security," write a Python program that stores the search results in a knowledge database and uses an emotion recognition engine to provide appropriate information when another user needs the same information.

[1635] In this way, users can efficiently obtain the information they need and be provided with appropriate information according to their emotions.

[1636] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1637] Step 1:

[1638] A user searches for information about electronic payments using a smartphone.

[1639] Input: User's search query (e.g., "electronic payment security")

[1640] Output: The search query is sent to the server.

[1641] Specific action: The user types "electronic payment security" into the search bar on their smartphone and presses the search button.

[1642] Step 2:

[1643] The server receives the search query and sends a request to an external search engine API.

[1644] Input: User's search query

[1645] Output: Search results from the search engine API

[1646] Specific operation: The server sends a search query to the search engine API and receives search results.

[1647] Step 3:

[1648] The server sends the received search results to the knowledge generation platform and stores them in the knowledge database.

[1649] Input: Search results from the search engine API

[1650] Output: Search results stored in a knowledge database

[1651] Specific operation: The server sends the search results to the knowledge generation platform and stores them in the knowledge database.

[1652] Step 4:

[1653] Another user searches for the same information using a smartphone.

[1654] Input: Another user's search query (e.g., "electronic payment security")

[1655] Output: The search query is sent to the server.

[1656] Specific action: Another user types "electronic payment security" into the search bar of their smartphone and presses the search button.

[1657] Step 5:

[1658] A server receives a search query and retrieves relevant information from a knowledge database.

[1659] Input: Another user's search query

[1660] Output: Relevant information retrieved from the knowledge database

[1661] Specific operation: The server sends a query to the knowledge database to retrieve relevant information.

[1662] Step 6:

[1663] The server uses an emotion recognition engine to recognize the emotion of another user.

[1664] Input: Voice and facial expression data of another user

[1665] Output: Recognized emotion (e.g., "happy")

[1666] Specific operation: The server sends another user's voice and facial expression data to the emotion recognition engine to recognize the emotion.

[1667] Step 7:

[1668] The server filters the information retrieved from the knowledge database based on the recognized sentiment.

[1669] Input: Relevant information retrieved from the knowledge database, recognized emotions

[1670] Output: Filtered information

[1671] Specific operation: The server filters the information retrieved from the knowledge database based on the recognized emotion.

[1672] Step 8:

[1673] The server provides the filtered information to another user.

[1674] Input: Filtered information

[1675] Output: Information provided to another user

[1676] Specific operation: The server sends the filtered information to another user's smartphone and displays it.

[1677] Example 3

[1678] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1679] Conventional information search systems have difficulty in quickly providing users with the information they need, and when users are particularly confused, it takes a long time for them to find the appropriate information. Furthermore, because information is not provided in response to the user's emotions, convenience and operational efficiency have not been sufficiently improved.

[1680] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1681] In this invention, the server includes means for accumulating high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the accumulated knowledge to users in the form of an ecosystem, means for improving convenience and business efficiency, means for adjusting the operation of the knowledge generation platform using an emotion engine that recognizes the user's emotions, means for generating appropriate information based on the analysis results of the emotion engine, means for providing the generated information to users, and means for accumulating the generated information in the knowledge database. This makes it possible to quickly provide appropriate information according to the user's emotions, thereby shortening information search time and improving business efficiency.

[1682] A "user" is an individual or organization that uses the system to search for information and to streamline their work.

[1683] "Highly Accurate Results" means user-generated information or data that is accurate and reliable.

[1684] A "knowledge generation platform" is a system for processing information generated by users and storing it in a knowledge database.

[1685] A "knowledge database" is a database that stores accumulated information and data and provides them as needed.

[1686] An "ecosystem" is an integrated system environment for providing accumulated knowledge to users.

[1687] "Improving convenience" means improving the ease and comfort of use when users use the system.

[1688] "Improving business efficiency" means enabling users to perform their work more quickly and effectively.

[1689] An "emotion engine" is a technology that recognizes the user's emotions and adjusts the system's behavior according to those emotions.

[1690] "Analysis results" refer to the results of the emotion engine's analysis of the user's emotions.

[1691] "Appropriate information" is the most useful and relevant information provided based on the user's emotions and situation.

[1692] MODE FOR CARRYING OUT THE INVENTION

[1693] This invention is a system that accumulates highly accurate results generated by users in a knowledge database via a knowledge generation platform and provides the accumulated knowledge to users in the form of an ecosystem. Furthermore, it uses an emotion engine to recognize the user's emotions and adjusts the operation of the knowledge generation platform according to those emotions, thereby improving convenience and business efficiency.

[1694] Hardware and software used

[1695] The server uses a general cloud service as a knowledge generation platform. Specifically, it uses a cloud service that provides an "emotion recognition API" as the emotion engine, and a cloud service that provides a "natural language processing API" as the knowledge generation platform. This makes it possible to analyze the user's emotions and generate appropriate information.

[1696] Terminals are devices that users use to access the system, and include PCs, smartphones, tablets, etc. Terminals are equipped with cameras and microphones, which are used to capture the user's facial expressions and voice.

[1697] Program processing

[1698] The server receives the user's facial expressions and voice data sent from the device and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotions. For example, if the server determines that the user is confused, it sends instructions to the knowledge generation platform to generate the information the user needs.

[1699] The generated information is provided to the user's device via the server. The user can check the provided information on their device and carry out their work efficiently. The generated information is also stored in a knowledge database, allowing other users to quickly obtain similar information in the future.

[1700] Specific examples

[1701] For example, if a user is confused about how to use new software, the following actions may occur:

[1702] 1. A user accesses the system through a terminal and types, "I don't know how to use the new software."

[1703] 2. The device's camera captures the user's confused expression and sends the data to the emotion engine.

[1704] 3. The emotion engine analyzes the user's facial expression and determines that they are "confused."

[1705] 4. The server receives the analysis results of the emotion engine and sends instructions to the knowledge generation platform to generate detailed guides on how to use the software.

[1706] 5. The knowledge generation platform generates a detailed guide and sends it to the server.

[1707] 6. The server displays the generated guide on the user's device.

[1708] 7. The user reads the guide and learns how to use the software.

[1709] 8. The server stores the generated guide in a knowledge database, allowing other users to quickly retrieve the same information in the future.

[1710] Prompt Sentence Examples

[1711] "I'm having trouble figuring out how to use new software. Could you please tell me the specific steps?"

[1712] In this way, the system can shorten information search time and improve work efficiency by quickly providing appropriate information according to the user's emotions. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1713] Step 1:

[1714] A user accesses the system through a terminal.

[1715] Input: A user accesses a URL on the system using a terminal.

[1716] Output: The system home page is displayed on the terminal.

[1717] Specific operation: A user opens a web browser, enters the system's URL, and accesses it. The system's home page is displayed, and the user is ready to start searching for information.

[1718] Step 2:

[1719] The terminal transmits the user's emotions to the emotion engine.

[1720] Input: The device's camera and microphone capture the user's facial expressions and voice.

[1721] Output: The captured data is sent to the emotion engine.

[1722] How it works: The device's camera captures the user's face and the microphone records the user's voice, and this data is sent to the emotion engine in real time.

[1723] Step 3:

[1724] The emotion engine analyzes the user's emotions.

[1725] Input: Facial expression and voice data sent from the device.

[1726] Output: Analysis results showing user sentiment.

[1727] Specific operation: The emotion engine analyzes the received data and identifies the user's emotion, such as whether they are confused, happy, angry, etc. For example, if they are determined to be confused, the result is sent to the server.

[1728] Step 4:

[1729] The server receives the analysis results of the emotion engine.

[1730] Input: Analysis results sent from the emotion engine.

[1731] Output: Instructions are generated based on the analysis results.

[1732] Specific operation: The server receives the analysis results sent from the emotion engine and generates instructions to provide appropriate information according to the user's emotions.

[1733] Step 5:

[1734] The server sends instructions to the knowledge generation platform.

[1735] Input: Instructions based on the emotion engine's analysis results.

[1736] Output: Instructions sent to the knowledge generation platform.

[1737] Specific operation: If the server determines that the user is confused, it sends an instruction to the knowledge generation platform to "generate a detailed guide on how to use the software."

[1738] Step 6:

[1739] The knowledge generation platform generates relevant information.

[1740] Input: Instructions sent by the server.

[1741] Output: The information generated.

[1742] Specific Actions: Based on the instructions, the knowledge generation platform generates a detailed guide on how to use the software the user is having trouble with, including specific operating procedures and troubleshooting information.

[1743] Step 7:

[1744] The server provides the generated information to the user.

[1745] Input: Information sent from the knowledge generation platform.

[1746] Output: Information displayed on the user's terminal.

[1747] Specific operation: The server receives the generated guide and sends it to the user's device. The user then checks the detailed guide on the device and understands how to use the software.

[1748] Step 8:

[1749] The server stores the generated information in a knowledge database.

[1750] Input: Information sent from the knowledge generation platform.

[1751] Output: Information stored in the knowledge database.

[1752] Specific operation: The server stores the generated guide in a knowledge database so that other users can quickly obtain the same information in the future, for example, other users who are confused about how to use the same software can access it.

[1753] (Application example 3)

[1754] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1755] Robots working in factories face the challenge of quickly providing appropriate knowledge when they become confused or make errors during their work. There is also a need to efficiently store new knowledge acquired during work in a knowledge database and use it in real time. Furthermore, it is necessary to improve convenience and operational efficiency by adjusting the behavior of the knowledge generation platform according to the user's emotions.

[1756] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1757] In this invention, the server includes: means for storing high-precision results generated by users in a knowledge DB via a knowledge generation platform; means for providing the stored knowledge to users in the form of an ecosystem; means for improving convenience and work efficiency; means for recognizing user emotions using an emotion engine and adjusting the operation of the knowledge generation platform in accordance with those emotions; means for being installed in robots performing work in a factory and providing knowledge in real time; means for quickly providing appropriate knowledge when a robot senses confusion or makes an error; and means for automatically storing new knowledge acquired during work in a knowledge DB. This makes it possible to significantly improve work efficiency in a factory.

[1758] A "user" is a person or machine that uses the system to obtain information or perform a task.

[1759] "High accuracy results" means user-generated information or data that is accurate and reliable.

[1760] A "knowledge generation platform" is a system that collects, organizes, and stores information generated by users, and provides it as needed.

[1761] "Knowledge DB" is a database in which information accumulated by the knowledge generation platform is stored.

[1762] An "ecosystem" is a system that provides information accumulated in a knowledge database to users and allows them to share information with each other.

[1763] "Improving convenience" means increasing the ease and efficiency with which users use the system.

[1764] "Improving business efficiency" means making it possible to carry out work or tasks more quickly and effectively.

[1765] An "emotion engine" is an engine that has the function of recognizing the user's emotions and adjusting the system's behavior in response to those emotions.

[1766] A "robot working in a factory" is a machine that automatically performs specific tasks in a factory.

[1767] "Means of providing knowledge in real time" is a function that provides necessary information instantly.

[1768] "Means to quickly provide appropriate knowledge in the event of confusion or an error" refers to a function that immediately provides solutions and related information when the robot detects a problem.

[1769] "Means for automatically storing new knowledge gained during work in a knowledge database" is a function that automatically saves new information and knowledge discovered during work in a database.

[1770] As an embodiment of this invention, we will explain a system installed on a robot that works in a factory. This system accumulates high-precision results generated by users in a knowledge database via a knowledge generation platform and provides the accumulated knowledge to users in the form of an ecosystem. It also uses an emotion engine to recognize the user's emotions and adjusts the operation of the knowledge generation platform according to those emotions.

[1771] Hardware and software used

[1772] Hardware: Factory robots, cameras (for emotion recognition)

[1773] Software: Python, emotion_recognition library, knowledge_db library

[1774] Data processing and calculation

[1775] emotion recognition

[1776] The camera captures the robot's actions and errors, and the emotion_recognition library is used to recognize emotions. The emotion engine detects the robot's emotions when it feels confused or makes an error, and sends instructions to the knowledge generation platform to provide appropriate knowledge.

[1777] Knowledge provision

[1778] Based on the emotions recognized by the emotion engine, the knowledge generation platform uses the knowledge_db library to retrieve appropriate information from the knowledge database and provide it to the robot, allowing it to quickly resolve confusions and errors.

[1779] Knowledge DB update

[1780] New knowledge and information gained during work is automatically stored in the knowledge database through the knowledge generation platform, allowing for quick responses if similar problems arise in future work.

[1781] Specific examples

[1782] For example, if an error occurs when the robot is assembling part A, the emotion engine will recognize the robot's confusion and retrieve and provide information from the knowledge database on how to assemble part A. Based on this information, the robot can resolve the error and continue working.

[1783] Prompt Sentence Examples

[1784] If an error occurs when the robot assembles part A, the emotion engine should recognize the robot's confusion and retrieve information from the knowledge database on how to assemble part A and provide it.

[1785] In this way, it is possible to significantly improve work efficiency within the factory.

[1786] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1787] Step 1:

[1788] The server acquires video data in real time from the factory robot's camera.

[1789] Input: Factory robot camera footage

[1790] Output: Video data

[1791] Specific operation: The server receives video data in streaming format from the camera mounted on the factory robot.

[1792] Step 2:

[1793] The server uses the emotion_recognition library to recognize the robot's emotions from video data.

[1794] Input: Video data

[1795] Output: Emotion data (e.g., confusion, error)

[1796] Specific operation: The server inputs the received video data into the emotion_recognition library and analyzes the emotions from the robot's facial expressions and movements.

[1797] Step 3:

[1798] The server sends instructions to the knowledge generation platform based on the recognized emotion data.

[1799] Input: Emotion data

[1800] Output: Instructions to the knowledge generation platform

[1801] Specific operation: If the emotion data is "confusion" or "error", the server sends an instruction to the knowledge generation platform to provide appropriate knowledge.

[1802] Step 4:

[1803] The knowledge generation platform uses the knowledge_db library to retrieve the appropriate information from the knowledge DB.

[1804] Input: Instructions to the knowledge generation platform

[1805] Output: Relevant knowledge information

[1806] Specific operation: The knowledge generation platform queries the knowledge DB based on the instructions and obtains relevant information.

[1807] Step 5:

[1808] The server provides the acquired knowledge information to the factory robot.

[1809] Input: Appropriate knowledge information

[1810] Output: Providing knowledge information to factory robots

[1811] Specific operation: The server sends the acquired knowledge information to the factory robot, allowing the robot to continue working based on that information.

[1812] Step 6:

[1813] The server automatically accumulates new knowledge gained during work in the knowledge database through the knowledge generation platform.

[1814] Input: New knowledge or information

[1815] Output: Accumulation in knowledge DB

[1816] Specific operation: The server sends new knowledge and information acquired by the robot during work to the knowledge generation platform, where it is automatically stored in the knowledge database.

[1817] In this way, it is possible to significantly improve work efficiency within the factory.

[1818] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1819] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1820] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1821] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1822] [Third embodiment]

[1823] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1824] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1825] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1826] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1827] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1828] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1829] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1830] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1831] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1832] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1833] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1834] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1835] "Example 1"

[1836] The present invention is a system that stores highly accurate results generated by users using conventional search and chatbots in a knowledge database via a knowledge generation platform. Specifically, users send information obtained through search and chatbots to the knowledge generation platform, which stores the information in the knowledge database. This knowledge database stores knowledge in various fields and provides that knowledge to users in the form of an ecosystem.

[1837] "Example 2"

[1838] As a specific example, when a user searches for medical information, the search results are sent to the knowledge generation platform, and that information is stored in the knowledge database. If another user subsequently needs similar information, the relevant information is retrieved from the knowledge database and provided to the user. This allows users to efficiently obtain the information they need.

[1839] "Example 3"

[1840] Furthermore, the present invention is a system that aims to improve convenience and business efficiency. Specifically, by providing information stored in a knowledge database at the time the user needs it, the system reduces the time the user spends searching for information and improves business efficiency.

[1841] The processing flow of each embodiment will be described below.

[1842] "Example 1"

[1843] Step 1: The user generates information using traditional search or chatbots.

[1844] Step 2: Send the generated information to the knowledge generation platform.

[1845] Step 3: The knowledge generation platform stores the information in the knowledge database.

[1846] "Example 2"

[1847] Step 1: The user conducts a search for medical information.

[1848] Step 2: Submit the search results to the knowledge generation platform.

[1849] Step 3: The knowledge generation platform stores the information in the knowledge database.

[1850] Step 4: When another user needs similar information, the relevant information is retrieved from the knowledge database and provided to the user.

[1851] "Example 3"

[1852] Step 1: The user searches for the information they need.

[1853] Step 2: Obtain the relevant information from the knowledge database.

[1854] Step 3: Provide the acquired information to the user.

[1855] Example 1

[1856] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1857] The information obtained using conventional search engines and interactive agents is temporary and difficult to reuse. Furthermore, there is a lack of means to guarantee the accuracy and reliability of the information obtained, making it difficult for users to efficiently obtain highly accurate information. Furthermore, there is a need for a method to improve user convenience and enhance work efficiency by providing accumulated knowledge as an ecosystem.

[1858] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1859] In this invention, the server includes a means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, a means for providing the stored knowledge to users in the form of an ecosystem, and a means for improving convenience and business efficiency, thereby enabling the efficient storage and reuse of high-precision information obtained by users using search engines and interactive agents.

[1860] "User" means an individual or entity that uses the system to obtain information and submit the generated results to the knowledge generation platform.

[1861] "High-precision results" are accurate and reliable information obtained by users using search engines or interactive agents.

[1862] The "knowledge generation platform" is a system that analyzes information sent by users, converts it into an appropriate format, and stores it in a knowledge database.

[1863] A "knowledge database" is a database system that accumulates analyzed information and stores it in a form that can be reused later.

[1864] An "ecosystem" is an entire system that provides accumulated knowledge to users and enables them to efficiently obtain information.

[1865] A "search engine" is software that searches the Internet for relevant information based on keywords entered by a user and provides the results.

[1866] An "interactive agent" is software that responds to users' questions in natural language, and is also called a chatbot.

[1867] "Terminal" refers to a device that a user uses to access a search engine or interactive agent, including a PC or smartphone.

[1868] A "server" is a computer system that operates a knowledge generation platform and knowledge database, and analyzes and stores information.

[1869] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and is used to understand the meaning of information and convert it into an appropriate format.

[1870] The "HTTPS protocol" is a communication protocol for securely sending and receiving data over the Internet.

[1871] This invention is a system that accumulates high-precision results generated by users in a knowledge database via a knowledge generation platform and provides the accumulated knowledge to users in the form of an ecosystem. Specific embodiments of this system are described below.

[1872] System configuration

[1873] Hardware

[1874] Devices: Devices such as PCs, smartphones, and tablets that allow users to access search engines and interactive agents.

[1875] Server: A computer system for operating the knowledge generation platform and knowledge database, equipped with a high-performance processor and large-capacity storage.

[1876] software

[1877] Search engine: Software that searches the Internet for relevant information based on keywords entered by the user and provides results.

[1878] Conversational Agent: Software that responds to user questions in natural language, using generative AI models to generate appropriate answers to the questions.

[1879] Knowledge generation platform: A system that analyzes information sent by users, converts it into an appropriate format, and stores it in a knowledge database. It uses natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding.

[1880] Knowledge database: A database system that accumulates analyzed information and stores it in a form that can be reused later. It uses a database management system such as MySQL or PostgreSQL.

[1881] Data processing and calculation

[1882] Acquisition and transmission of information

[1883] A user uses a device to access a search engine or conversational agent to obtain information. For example, the user asks the conversational agent, "Tell me about the latest AI technology." The conversational agent uses a generative AI model to generate an appropriate answer to the question. If this answer is determined to be highly accurate, the device sends this information to the knowledge generation platform. Transmission is via the HTTPS protocol.

[1884] Analysis and accumulation of information

[1885] The server analyzes the information received by the knowledge generation platform. This analysis uses natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding. The server understands the meaning of the information and converts it into an appropriate format. The analyzed information is then stored in a knowledge database.

[1886] Reuse of information

[1887] When a user retrieves information using a search engine or a conversational agent again, the accumulated information from the knowledge database is reused. For example, if another user asks, "Tell me about the latest AI technology," the conversational agent will retrieve information from the knowledge database, such as, "This includes generative AI models and deep learning," and respond.

[1888] Examples of concrete examples and prompts

[1889] As a concrete example, consider the case where a user asks a conversational agent, "Tell me about the latest AI technologies." The conversational agent responds, "The latest AI technologies include generative AI models and deep learning." If this answer is deemed highly accurate, the device sends this information to the knowledge generation platform. The server analyzes the information using the Google Cloud Natural Language API and stores it in a MySQL database. This information can be reused when other users search for "the latest AI technologies."

[1890] Examples of prompts to input to a generative AI model might include:

[1891] "Tell me about the latest AI technology"

[1892] By inputting this prompt sentence into the interactive agent, the user can obtain highly accurate information, which is then stored in a knowledge database.

[1893] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1894] Step 1:

[1895] Users retrieve information using search engines or interactive agents.

[1896] Specifically, the user uses a terminal to ask the conversational agent, "Tell me about the latest AI technology."

[1897] Input: User question (prompt)

[1898] Output: Answer from the conversational agent (answer from the generative AI model)

[1899] Step 2:

[1900] The terminal transmits the acquired information to the knowledge generation platform.

[1901] Specifically, the terminal sends the answer it receives from the conversational agent, "Modern AI technologies include generative AI models and deep learning," to the knowledge generation platform using the HTTPS protocol.

[1902] Input: Answer from the conversational agent

[1903] Output: Data sent to the knowledge generation platform

[1904] Step 3:

[1905] The server analyzes the information on the knowledge generation platform.

[1906] Specifically, the server uses the Google Cloud Natural Language API to analyze the received information, understand the meaning of the information, and convert it into an appropriate format.

[1907] Input: Data sent to the knowledge generation platform

[1908] Output: Parsed information (data converted into the appropriate format)

[1909] Step 4:

[1910] The server stores the analyzed information in a knowledge database.

[1911] Specifically, the server inserts the parsed information into a MySQL database for later reuse.

[1912] Input: Parsed information

[1913] Output: Data stored in the knowledge database

[1914] Step 5:

[1915] Users reuse information from the knowledge database.

[1916] Specifically, when another user asks the conversational agent, "Tell me about the latest AI technology," the conversational agent retrieves information from the knowledge database that says, "This includes generative AI models and deep learning," and responds.

[1917] Input: User question (prompt)

[1918] Output: Information retrieved from the knowledge database (answer by the conversational agent)

[1919] (Application example 1)

[1920] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1921] In conventional factory work, it was difficult for workers to quickly obtain the information they needed, resulting in a decline in work efficiency. Also, a lot of time was often spent troubleshooting and learning optimal work procedures, resulting in a decline in overall productivity. Furthermore, while automation using robots is progressing, there was also the problem of insufficient cooperation with workers, making efficient operation difficult.

[1922] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1923] In this invention, the server includes a means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, a means for providing the stored knowledge to users in the form of an ecosystem, a means for improving convenience and work efficiency, a means for providing a knowledge-sharing robot assistant to improve work efficiency within the factory, a means for displaying information to workers in real time via smart devices, and a means for the robot to transport necessary parts according to the worker's instructions and automate simple tasks. This allows workers to quickly obtain the information they need and efficiently troubleshoot and optimize work procedures. It also strengthens collaboration with the robot, improving overall productivity.

[1924] "User" refers to a person who uses the system to search for information or acquire knowledge.

[1925] "High-precision results" refers to accurate and reliable information obtained through search and chatbots.

[1926] A "knowledge generation platform" refers to a system that collects information obtained by users and stores it in a knowledge database.

[1927] "Knowledge DB" refers to a database that accumulates knowledge from various fields and provides it to users in the form of an ecosystem.

[1928] An "ecosystem" refers to a system in which information accumulated in a knowledge database is provided to users and mutually utilized.

[1929] "Improving convenience" means enabling users to quickly and easily obtain the information they need.

[1930] "Improving business efficiency" refers to increasing work efficiency and improving productivity.

[1931] A "knowledge-sharing robot assistant" is a robot that retrieves information from a knowledge database and provides it to workers in order to improve work efficiency within the factory.

[1932] "Smart device" refers to devices for displaying information, such as smartphones, smart glasses, and head-mounted displays.

[1933] "Displaying information in real time" means displaying the necessary information instantly so that workers can use it on the spot.

[1934] A "robot" refers to a machine that transports necessary parts according to the instructions of a worker or automates simple tasks.

[1935] To implement the present invention, the following system configuration and program are required.

[1936] System Configuration

[1937] 1. Hardware

[1938] Smart devices: smartphones, smart glasses, head-mounted displays (e.g., Google Glass, Microsoft HoloLens)

[1939] Factory robots: Robots that transport parts and automate simple tasks (e.g., Universal Robots UR series)

[1940] Server: Server for hosting the knowledge generation platform and knowledge DB

[1941] 2. Software

[1942] API server: Server software (e.g., Flask) for providing the functionality of the knowledge generation platform

[1943] Database: Database software that functions as a knowledge database (e.g., MongoDB)

[1944] Program processing

[1945] The server executes a program that includes the following means:

[1946] 1. Knowledge Generation Platform

[1947] Users use searches and chatbots via their smart devices, and the highly accurate results obtained are sent to a knowledge generation platform.

[1948] The knowledge generation platform stores the received information in a knowledge database.

[1949] 2. Knowledge DB

[1950] The knowledge database accumulates knowledge in various fields and provides it to users in the form of an ecosystem.

[1951] When a user requests the information they need, the most appropriate information is retrieved from the knowledge database and displayed in real time on the smart device.

[1952] 3. Knowledge-Sharing Robot Assistant

[1953] To improve work efficiency within the factory, information is retrieved from the knowledge database and provided to workers.

[1954] The robot follows the worker's instructions to transport the necessary parts and automate simple tasks.

[1955] Specific examples

[1956] For example, when a worker searches for "the best welding technique," the knowledge generation platform stores that information in the knowledge database. Next, when another worker needs the same information, the information about the best welding technique is retrieved from the knowledge database and displayed in real time on the smart glasses.

[1957] If troubleshooting is required, the worker can search for "how to solve error code 123," and the solution will be retrieved from the knowledge database and displayed on the head-mounted display.The robot will then transport the necessary parts according to the worker's instructions, automating the work.

[1958] Prompt Sentence Examples

[1959] "What is the best welding technique?"

[1960] How do I resolve error code 123?

[1961] This allows workers to quickly obtain the information they need, allowing them to efficiently troubleshoot and optimize work procedures. It also strengthens collaboration with robots, improving overall productivity.

[1962] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1963] Step 1:

[1964] A user uses a smart device to input information through a search query or a chatbot. The input query is sent to a knowledge generation platform. The input is text data about the information the user wants to know or the problem they want to solve. The output is the query data sent to the knowledge generation platform.

[1965] Step 2:

[1966] The server processes the query data received through the knowledge generation platform and generates relevant high-precision results. This process involves using a generative AI model to generate optimal answers to the queries. The input is the query data submitted by the user, and the output is the generated high-precision results.

[1967] Step 3:

[1968] The server stores the generated high-precision results in a knowledge database. The input here is the generated high-precision results, and the output is the data stored in the knowledge database. Data processing involves converting the result data into an appropriate format and saving it in the database.

[1969] Step 4:

[1970] When the user needs the information again, he / she sends a request to the knowledge DB via his / her smart device. The input is the request data related to the information the user wants to know, and the output is the request data sent to the knowledge DB.

[1971] Step 5:

[1972] The server retrieves information corresponding to the request from the knowledge database. The input is the request data from the user, and the output is the related information retrieved from the knowledge database. Data calculation involves searching the database based on the request and extracting the most appropriate information.

[1973] Step 6:

[1974] The server sends the acquired information to the smart device and displays it to the user in real time. The input is information acquired from the knowledge database, and the output is information displayed on the smart device. Specifically, the server converts the information into an appropriate format and displays it on the smart device's display.

[1975] Step 7:

[1976] When a user issues an instruction to a robot, the instruction is sent to the robot through a smart device. The input is instruction data from the user, and the output is instruction data sent to the robot.

[1977] Step 8:

[1978] The robot transports necessary parts and automates simple tasks based on received instructions. The input is instruction data from the user, and the output is the results of the performed work. Specifically, the robot operates according to the instructions and performs the specified task.

[1979] This allows users to quickly obtain the information they need, allowing them to efficiently troubleshoot and find the optimal work procedure. It also strengthens collaboration with the robot, improving overall productivity.

[1980] Example 2

[1981] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1982] Conventional medical information search systems make it difficult for users to efficiently obtain the information they need. Furthermore, when multiple users search for the same information, they have to repeat the same search process each time, which is time-consuming and labor-intensive. Furthermore, there is a lack of a way to effectively utilize accumulated knowledge, and improvements in convenience and operational efficiency are needed.

[1983] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1984] In this invention, the server includes: means for accumulating high-precision results generated by users in a knowledge database via a knowledge generation platform; means for providing the accumulated knowledge to users in the form of an ecosystem; means for improving convenience and work efficiency; means for users to search for medical information and send the search results to the knowledge generation platform; and means for retrieving the relevant information from the knowledge database and providing it to another user when another user needs similar information. This allows users to efficiently obtain the medical information they need, reducing the time and effort required when multiple users search for the same information. Furthermore, by effectively utilizing the accumulated knowledge, convenience and work efficiency can be improved.

[1985] "User" means an individual or organization that uses the System to retrieve medical information.

[1986] "High-precision results" are accurate and reliable information that users receive when they search.

[1987] A "knowledge generation platform" is a system for processing search results and storing them in a knowledge database.

[1988] A "knowledge database" is a database system for storing and managing accumulated medical information and other knowledge.

[1989] An "ecosystem" is an overall system environment that provides accumulated knowledge to users and allows them to mutually utilize it.

[1990] "Improving convenience" means enabling users to quickly and easily obtain the information they need.

[1991] "Improved business efficiency" refers to the ability to increase business efficiency and reduce time and effort by using the system.

[1992] "Medical information" refers to all information related to medical care, such as disease treatment methods, symptoms, and drug information.

[1993] "Search results" are a list of information obtained when a user performs a search.

[1994] "Send" refers to the act of sending data from a terminal to a server.

[1995] "Retrieval" refers to the act of the server retrieving the necessary information from the knowledge database.

[1996] "Providing" refers to the act of displaying the information acquired by the server to the user.

[1997] The present invention is a system for enabling a user to efficiently obtain medical information. A specific embodiment of this system will be described below.

[1998] System configuration

[1999] The system mainly consists of the following components:

[2000] User's device (PC, smartphone, etc.)

[2001] server

[2002] Knowledge Generation Platform

[2003] Knowledge Database

[2004] Hardware and software used

[2005] Device: A device such as a PC or smartphone. Searches are performed using a web browser (e.g., Google Chrome, Mozilla Firefox).

[2006] Server: Receives and processes search queries. Uses a web server (e.g., Apache HTTP Server, Nginx) and a database management system (e.g., MySQL, PostgreSQL).

[2007] Knowledge generation platform: Processes search results and stores them in a knowledge database. Uses a data streaming platform (e.g., Apache Kafka).

[2008] Knowledge database: Stores and manages accumulated medical information. Uses a database management system (e.g., MySQL, PostgreSQL).

[2009] System Operation

[2010] 1. A user searches for medical information

[2011] Users open a web browser on their PC or smartphone and access a search engine or dedicated medical information search site.

[2012] Enter a keyword such as "diabetes treatment" in the search box and click the search button.

[2013] 2. The device sends the search results to the server

[2014] The terminal transmits the generated search query to the server as an HTTP request.

[2015] Example: A terminal sends a request to a server: "GET / search?q=diabetes treatment HTTP / 1.1".

[2016] 3. The server processes the data in the knowledge generation platform

[2017] The server analyzes the received search query and collects relevant medical information.

[2018] The server transmits the collected information to the knowledge generation platform.

[2019] Example: A server collects information on "diabetes treatment" and sends it to a knowledge generation platform.

[2020] 4. The server stores the data in a knowledge database

[2021] The knowledge generation platform stores the received information in a knowledge database.

[2022] Example: A knowledge database stores information about "treatments for diabetes."

[2023] 5. Another user searches for similar information

[2024] Another user similarly searches for medical information using a PC or smartphone.

[2025] Enter "diabetes cure" in the search box and click the search button.

[2026] 6. The server retrieves the relevant information from the knowledge database and provides it to the user.

[2027] The server searches and acquires the relevant information from the knowledge database.

[2028] The server sends the acquired information to the user's terminal as an HTTP response.

[2029] Example: The server retrieves information about "diabetes treatment" from a knowledge database and displays it to the user.

[2030] Examples of concrete examples and prompts

[2031] Specific examples

[2032] User A searches for "treatment for high blood pressure" and the results are stored in the knowledge database.

[2033] When user B searches for "treatment of high blood pressure" at a later date, information is retrieved from the knowledge database and provided to user B.

[2034] Prompt Sentence Examples

[2035] "What's the latest information on treatments for high blood pressure?"

[2036] "Please provide detailed information on diabetes treatment."

[2037] This system allows users to efficiently obtain the medical information they need. It also reduces the time and effort required when multiple users search for the same information. By effectively utilizing the accumulated knowledge, convenience and work efficiency can be improved.

[2038] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2039] Step 1:

[2040] Users search for medical information

[2041] Input: A user uses a PC or smartphone, opens a web browser, and accesses a search engine or dedicated medical information search site. They enter a keyword such as "diabetes treatment" in the search box and click the search button.

[2042] Data processing: A search query is generated.

[2043] Output: The generated search query is saved on the device.

[2044] Specific operation: A user types "diabetes cure" and clicks the search button, generating a search query.

[2045] Step 2:

[2046] The device sends the search results to the server

[2047] Input: The generated search query.

[2048] Data processing: The device sends the search query to the server as an HTTP request.

[2049] Output: The server receives the search query.

[2050] Specific operation: The terminal sends the request "GET / search?q=diabetes treatment HTTP / 1.1" to the server.

[2051] Step 3:

[2052] The server processes the data in the knowledge generation platform

[2053] Input: The search query received by the server.

[2054] Data processing: The server analyzes the search query and collects relevant medical information, which is then sent to the knowledge generation platform.

[2055] Output: The knowledge generation platform receives the information.

[2056] Specific operation: The server collects information on "diabetes treatment methods" and sends it to the knowledge generation platform.

[2057] Step 4:

[2058] The server stores the data in a knowledge database

[2059] Input: Information received by the knowledge generation platform.

[2060] Data processing: The knowledge generation platform stores the received information in a knowledge database.

[2061] Output: Information is stored in a knowledge database.

[2062] Specific operation: Information about "treatments for diabetes" is stored in the knowledge database.

[2063] Step 5:

[2064] Another user searches for similar information

[2065] Input: Another user uses a PC or smartphone, opens a web browser, accesses a search engine or dedicated medical information search site, types "diabetes treatment" in the search box, and clicks the search button.

[2066] Data processing: A search query is generated.

[2067] Output: The generated search query is saved on the device.

[2068] Specific behavior: Another user types "diabetes cure" and clicks the search button, generating a search query.

[2069] Step 6:

[2070] The server retrieves the relevant information from the knowledge database and provides it to the user.

[2071] Input: The search query received by the server.

[2072] Data processing: The server searches for and retrieves the relevant information from the knowledge database, and sends the retrieved information to the user's device as an HTTP response.

[2073] Output: Information is displayed on the user's terminal.

[2074] Specific operation: The server retrieves information about "diabetes treatment" from the knowledge database and displays it to the user.

[2075] (Application example 2)

[2076] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2077] In modern brick-and-mortar stores, it is difficult for users to efficiently obtain product information. In particular, when users want to know detailed information or stock status about a specific product, they must inquire with a store clerk, which takes time and effort. Furthermore, when multiple users request the same information, duplicate information acquisition occurs, which is inefficient. Furthermore, conventional search systems and chatbots have difficulty providing information in real time, which reduces user convenience.

[2078] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for storing high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the accumulated knowledge to users in the form of an ecosystem, means for improving convenience and business efficiency, and means for providing product information in real time using smart devices in physical stores. This allows users to improve their shopping experience in physical stores and obtain the product information they need quickly and efficiently.

[2079] "User" means any person or entity that uses the System to search for and retrieve information.

[2080] "Highly Accurate Results" are search results that provide accurate and relevant information in response to a user's search query.

[2081] A "knowledge generation platform" is a system for collecting information generated by users and storing it in a knowledge database.

[2082] A "knowledge database" is a database that stores collected information and provides it to users as needed.

[2083] An "ecosystem" is an integrated system environment for providing users with information stored in a knowledge database.

[2084] "Improving convenience" means enabling users to obtain the information they need quickly and efficiently.

[2085] "Improving business efficiency" refers to preventing duplicate information acquisition and increasing the efficiency of the entire system.

[2086] A "brick and mortar store" is a store that sells products in a physical location.

[2087] A "smart device" is an electronic device that is connected to the Internet and can display and operate information.

[2088] "Real-time" refers to information being obtained and provided immediately.

[2089] The present invention relates to a system for providing real-time product information using smart devices in a physical store. Specific embodiments of this system are described below.

[2090] System Configuration

[2091] The system consists of the following main components:

[2092] 1. Knowledge generation platform: Collects high-precision results generated by users and stores them in a knowledge database.

[2093] 2. Knowledge database: Stores accumulated information and makes it available when needed.

[2094] 3. Smart Devices: Devices used by customers in physical stores (e.g., smart glasses).

[2095] 4. Ecosystem: An integrated system that provides users with information stored in a knowledge database.

[2096] Program processing

[2097] The server accumulates the highly accurate results generated by users in a knowledge database via a knowledge generation platform. Specifically, when a user searches for product information using a smart device, the search results are sent to the knowledge generation platform. The knowledge generation platform analyzes the search results and stores them in the knowledge database.

[2098] Next, when another user needs information about the same product, they access the knowledge database through their smart device to retrieve the relevant information, which is then displayed in real time on the smart device's display.

[2099] Hardware and software used

[2100] Hardware: Smart glasses, server

[2101] Software: Knowledge generation platform, knowledge database, API

[2102] Specific examples

[2103] For example, if a user is searching for "medical masks" in a physical store, the user wearing smart glasses can search for "medical masks" by voice, and the smart glasses' display will show detailed information about the masks and their stock availability. This information is retrieved in real time from the knowledge database.

[2104] Prompt Sentence Examples

[2105] "Search for information on medical masks."

[2106] "Get medical mask information from our knowledge database."

[2107] "Display medical mask information on smart glasses."

[2108] In this way, users can improve their shopping experience in physical stores and obtain the product information they need quickly and efficiently.

[2109] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2110] Step 1:

[2111] A user searches for product information using a smart device (e.g., smart glasses). The user inputs a search query through voice input or touch operation. The input query is sent from the smart device to a server. The input data is the search query, and the output data is the search request sent to the server.

[2112] Step 2:

[2113] The server sends the received search query to the knowledge generation platform. The knowledge generation platform analyzes the search query and generates relevant high-precision results. The input data is the search query, and the output data is the high-precision results. In particular, the knowledge generation platform analyzes the search query and retrieves relevant information from the database.

[2114] Step 3:

[2115] The knowledge generation platform stores the generated high-precision results in a knowledge database. The input data is the high-precision results, and the output data is the information stored in the knowledge database. In particular, the knowledge generation platform writes the high-precision results into the database.

[2116] Step 4:

[2117] If another user wants to search for information about the same product, they will use their smart device to enter a search query again. The entered query will be sent from the smart device to the server. The input data is the search query, and the output data is the search request sent to the server.

[2118] Step 5:

[2119] The server accesses the knowledge database to retrieve relevant information. The input data is a search query, and the output data is the information retrieved from the knowledge database. Specifically, the server sends a query to the knowledge database to retrieve relevant information.

[2120] Step 6:

[2121] The server sends the acquired information to the smart device. The input data is the information acquired from the knowledge database, and the output data is the information sent to the smart device. Specifically, the server transfers the acquired information to the smart device.

[2122] Step 7:

[2123] The smart device displays the received information to the user. The input data is the information sent from the server, and the output data is the information displayed on the smart device's display. Specifically, the smart device displays the received information on the display.

[2124] In this way, users can improve their shopping experience in physical stores and obtain the product information they need quickly and efficiently.

[2125] Example 3

[2126] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2127] With conventional information search systems, it was difficult for users to quickly obtain the information they needed, and information searches could take a long time. In addition, search results often did not necessarily meet the user's needs, which led to problems with the inability to improve work efficiency. Furthermore, conventional systems lacked a means to effectively utilize accumulated knowledge, making it difficult to reuse knowledge.

[2128] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[2129] In this invention, the server includes means for accumulating high-precision results generated by users in a knowledge database via a knowledge generation platform, means for providing the accumulated knowledge to users in the form of an ecosystem, means for receiving and analyzing queries sent by users from their terminals, means for searching for related information from the knowledge database based on the analyzed queries, means for inputting the search results into a generative AI model as prompt sentences to generate appropriate answers, and means for providing the generated answers to users. This allows users to quickly obtain the information they need, shortening information search time and improving work efficiency.

[2130] "User" means an individual or organization that uses the system to search for information and obtain the data they need.

[2131] "High-accuracy results" are search results that provide accurate and relevant information in response to a user's query.

[2132] A "knowledge generation platform" is a system for collecting information generated by users and storing it in a knowledge database.

[2133] A "knowledge database" is a database that stores accumulated information and knowledge and can be searched and retrieved as needed.

[2134] An "ecosystem" is the overall structure of a system that provides accumulated knowledge to users and promotes the reuse and sharing of information.

[2135] A "terminal" is a device (e.g., a PC or smartphone) through which a user inputs a query and communicates with the server.

[2136] A "query" is a question or request that a user enters into a system to retrieve information.

[2137] A "server" is a computer system that receives and analyzes queries, retrieves information from a knowledge database, and generates answers using generative AI models.

[2138] A "generative AI model" is an artificial intelligence model that generates appropriate answers in natural language based on an input prompt sentence.

[2139] A "prompt sentence" is an instruction sentence input to a generative AI model, and is the sentence that serves as the basis for the model to generate an appropriate answer.

[2140] MODE FOR CARRYING OUT THE INVENTION

[2141] The present invention is a system for enabling users to quickly obtain the information they need and improving work efficiency. A specific embodiment of this system will be described below.

[2142] System configuration

[2143] Hardware

[2144] Server: High-performance server (e.g. general-purpose server)

[2145] Device: The device used by the user (e.g., computer, smartphone)

[2146] software

[2147] Database Management System: Software for managing knowledge databases (e.g., MySQL)

[2148] Generative AI model: An artificial intelligence model that performs natural language processing (e.g., a general-purpose generative AI model)

[2149] Program processing

[2150] The server receives and analyzes queries sent from the device. Based on the analyzed queries, the server searches for relevant information from a knowledge database. The search results are input as prompt sentences into the generative AI model, which generates an appropriate answer. The generated answer is sent from the server to the device and provided to the user.

[2151] Specific examples

[2152] For example, consider the case where a user enters the query "Tell me about the latest project management tools."

[2153] 1. Query reception: A user sends a query from their device saying, "Please tell me about the latest project management tools."

[2154] 2. Query analysis: The server analyzes the query and searches the knowledge database for information about "project management tools."

[2155] 3. Information retrieval: The server uses MySQL to retrieve relevant information from the knowledge database.

[2156] 4. Use of generative AI model: The server inputs the acquired information into the generative AI model and formats the information in a form that is easy for the user to understand.

[2157] 5. Information provision: The server sends the formatted information to the user's terminal.

[2158] An example of a prompt to be input to the generative AI model would be, "Please provide information about the latest project management tools. Please use the following data as a basis," followed by information retrieved from the knowledge database.

[2159] In this way, the user can quickly obtain the necessary information, shortening the time required for searching information and improving work efficiency. The flow of the identification process in the third embodiment will be described with reference to FIG.

[2160] Step 1:

[2161] The user enters a query.

[2162] The user inputs the information they want to obtain from their device (e.g., a PC or smartphone). For example, they might input, "Tell me about the latest project management tools." The input query is saved in an input field on the device.

[2163] Step 2:

[2164] The device sends a query to the server.

[2165] The terminal sends the query entered by the user to the server as an HTTP request, and data is sent over the Internet. The input is the user's query, and the output is an HTTP request to the server.

[2166] Step 3:

[2167] The server receives and parses the query.

[2168] The server receives the HTTP request sent from the terminal. It analyzes the received query and identifies the type of information the user is seeking. The input is the HTTP request, and the output is the analyzed query. Specifically, it recognizes the query content as information about the "project management tool."

[2169] Step 4:

[2170] The server retrieves relevant information from a knowledge database.

[2171] The server searches for relevant information from a knowledge database based on the analysis results. For example, it uses a database management system (e.g., MySQL) to retrieve the latest information on "project management tools." The input is the analyze...

Claims

1. a means for storing the user-generated high-precision results in a knowledge database via a knowledge generation platform; A means for providing the knowledge accumulated in the accumulating means to the user in the form of an ecosystem; Means to improve convenience and business efficiency; A means for a user to obtain information through the means for providing the information by using a search engine or an interactive agent; A means for transmitting information acquired by the terminal to the knowledge generation platform; means for a server to analyze information in said knowledge generation platform; a means for storing, in the knowledge database, information analyzed by the analyzing means in the server; means for said user to reuse information from said knowledge database; A system including:

2. The system of claim 1 , wherein the knowledge generation platform further comprises means for incorporating results generated using traditional search and interactive agents.

3. The system of claim 1 , wherein the ecosystem further comprises means for providing knowledge of various domains.

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