System

Abstract

A system is provided.SOLUTION: A system including a means in which a data provider uploads data by using a terminal, a means in which a server classifies and tags data received by the server and stores the data in a database, a means in which a user transmits a data request from a terminal, a means in which the server generates data by using a generated AI based on a request of the user and transmits the data to a user terminal, a means in which the server records a use situation of the data used by the server in a log, and a means in which the server calculates and pays a usage fee for each data provider.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] In recent years, the amount of data has increased rapidly, making it difficult for users to quickly obtain the data they need. It is also difficult for data providers to grasp the extent to which the data they provide is being used, and no reward system has been established. As a result, there is a problem of declining motivation among data providers, which hinders improvements in the quality and quantity of data. [Means for solving the problem]

[0005] The present invention provides a system in which data providers upload data using their terminals, and the server receives, classifies, tags, and stores the data in a database. Users send data requests from their terminals, and the server uses a generation AI to generate data according to the request and sends it to the user's terminal. Furthermore, the server records the usage status of the data in a log, tallying the number of times each data provider uses it, calculates rewards, and introduces a mechanism for periodically paying rewards, thereby solving the above-mentioned problems.

[0006] "Data provider" means a person who uploads data that he or she owns.

[0007] "Terminal" means a device through which a data provider or user communicates with a server via an internet connection. Examples include a personal computer, smartphone, tablet, etc.

[0008] A "server" is a computer system that receives, categorizes, tags, stores, and generates data upon request over the Internet.

[0009] "Data" is a collection of information, which may be in various forms such as text, numbers, images, audio, or video.

[0010] A "database" is a system for centrally managing and storing classified and tagged data.

[0011] A "user" is someone who requests and receives the desired data.

[0012] A "data request" is an action in which a user requests data based on specific conditions.

[0013] "Generative AI" is an artificial intelligence program that generates, analyzes, processes, etc. data in response to requests.

[0014] A "data log" is a log file or database record that records how much of what data is used.

[0015] "Remuneration" refers to the price paid when the data provided by the data provider is used.

[0016] A "specified period" is a set time frame for calculating and paying compensation, typically a month or a week. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention relates to a system in which data providers upload data via their devices, and a server receives, classifies, tags, and stores the data in a database. In this system, when a user sends a data request from their device, the server uses a generation AI to generate data according to the request and sends it to the user's device. Furthermore, the server records the usage status of the data in a log, tallying the number of times each data provider uses it, calculates rewards, and periodically pays them.

[0039] Program processing

[0040] 1. Data Collection:

[0041] Terminal: The data provider uses the terminal to access a dedicated upload form. The data provider selects the data file they own, enters detailed information about the data (e.g., data category, date, location, etc.), and presses the upload button to send the data to the server.

[0042] Server: The server receives the data file and its detailed information sent from the terminal.

[0043] 2. Data storage:

[0044] Server: The server saves the received data files in a temporary folder.

[0045] Server: Analyzes the contents of data files and classifies them by category and tag. Appropriate tags (e.g., "weather data," "temperature," "humidity," etc.) are assigned to each data.

[0046] Server: Stores the classified and tagged data in a database.

[0047] 3. Requesting and generating data:

[0048] User: The user accesses the system through a terminal, inputs the conditions for the data they require (e.g., a specific period, location, data category, etc.), and submits a request.

[0049] Server: The server receives the user's request, searches the database, and extracts data that matches the criteria.

[0050] Server: The AI ​​generates the requested data based on the search results. For example, if a user requests "average temperature in Tokyo in March 2023," the AI ​​aggregates the temperature data for the relevant period and calculates the average temperature.

[0051] Server: Returns the generated data to the user's terminal.

[0052] Terminal: The user's terminal displays the generated data.

[0053] 4. Usage Tracking:

[0054] Server: The server logs which data providers' data the generating AI used and how much of it was used, thereby accurately understanding the data usage status of each data provider.

[0055] 5. Calculation and Payment of Rewards:

[0056] Server: The server aggregates the amount of use of data from each data provider for each specific period (for example, at the end of the month).

[0057] Server: Based on the aggregated results, calculate the reward for each data provider. For example, calculate the amount based on the total number of uses, assuming "10 yen per use."

[0058] Server: Transfers the calculated reward to the registered account of the data provider.

[0059] Specific examples

[0060] 1. Data Collection:

[0061] Terminal: The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal. Detailed information such as "weather data," "March 2023," and "Tokyo" is entered into the form.

[0062] Server: Receives the data and details and stores them in a temporary folder.

[0063] 2. Data storage:

[0064] Server: Analyzes the received data and tags it with information such as "weather data," "temperature," "humidity," "March 2023," and "Tokyo."

[0065] Server: Stores the tagged data in a database.

[0066] 3. Requesting and generating data:

[0067] User: A weather forecaster requests "average temperature in Tokyo in March 2023" on their device.

[0068] Server: Receives the request and searches the database. The relevant data is extracted and the generating AI calculates the average temperature.

[0069] Server: Provides the calculation results to the user's terminal.

[0070] Terminal: Display "The average temperature in Tokyo in March 2023 will be 15 degrees" on the user terminal.

[0071] 4. Usage Tracking:

[0072] Server: Logs which data is used and how much. For example, it records how many times a researcher's weather data is used.

[0073] 5. Calculation and Payment of Rewards:

[0074] Server: At the end of the collection period, the server counts the 100 times the researcher's data has been used.

[0075] Server: Calculates a reward of 10 yen per 100 uses and transfers 1,000 yen to the researcher.

[0076] In this way, the system and program of the present invention enable users to quickly and accurately obtain the data they need while increasing the motivation of data providers.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] Terminal: The data provider logs into the system using a terminal.

[0080] Terminal: The data provider accesses the upload form, selects the data file, and enters data details (category, date, location, etc.).

[0081] Terminal: When the data provider presses the upload button, the terminal sends the data file and detailed information to the server.

[0082] Step 2:

[0083] Server: The server receives the data file and detailed information sent from the device.

[0084] Server: Save the received data in a temporary folder.

[0085] Step 3:

[0086] Server: Analyzes the stored data files and assigns categories and tags based on the data content. For example, tags such as "weather data," "temperature," and "humidity" are assigned.

[0087] Server: Stores the classified and tagged data in a database.

[0088] Step 4:

[0089] User: The user accesses the system through a terminal and navigates to the data request form.

[0090] User: Enters the criteria for the data they need (e.g., a specific time period, location, data category, etc.) and submits a request.

[0091] Step 5:

[0092] Server: The server receives the user's request and searches the database to extract data that matches the criteria.

[0093] Step 6:

[0094] Server: The generation AI generates the requested data based on the search results. For example, if a request is made for "average temperature in Tokyo in March 2023," the generation AI aggregates the relevant data and calculates the average temperature.

[0095] Server: Returns the generated data to the user's terminal.

[0096] Step 7:

[0097] Terminal: The user's terminal receives the generated data and displays it on the screen.

[0098] Step 8:

[0099] Server: Records details of the data used by the generating AI in a log, including which data provider's data was used and the number of times it was used.

[0100] Step 9:

[0101] Server: For each specific period (e.g., at the end of the month), the number of times data is used for each data provider is tallied.

[0102] Server: Calculates the reward based on the aggregated results. For example, 10 yen per use.

[0103] Step 10:

[0104] Server: Transfers the calculated reward to the registered account of the data provider.

[0105] In this way, data providers, users, and servers cooperate to operate the system, ensuring efficient and fair provision and use of data.

[0106] Example 1

[0107] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0108] In recent years, data collection, management, and provision have become important issues in many industries, but there is a lack of a system that can efficiently manage data uploaded by various data providers, quickly provide necessary data to users, and distribute appropriate rewards to data providers.In addition, there is a challenge in accurately understanding the usage status of the data uploaded by data providers and paying rewards in a transparent manner.

[0109] 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.

[0110] In this invention, the server includes: a means for a data provider to upload data using a terminal; a means for the server to classify and tag the received data and store it in an information storage device; a means for a user to send a data request from the terminal; a means for the server to generate data using a generative AI model based on the user request and send it to the user terminal; a means for the server to record the usage status of the data used by the server on a recording medium; and a means for the server to tally the number of times each data provider uses the data and calculate and pay rewards. This enables efficient management of data uploaded by data providers, fast and accurate provision of data to users, and appropriate distribution of rewards to data providers.

[0111] A "data provider" is a person or organization that uses a terminal to upload data to the system.

[0112] "Terminal" means a computing device used by a user or data provider to access the system and to send and receive data.

[0113] A "server" is a central computing system that receives, stores, analyzes, and generates data.

[0114] An "information storage device" is a database or storage system that stores data persistently after it has been classified and tagged.

[0115] A "generative AI model" is an artificial intelligence model used to generate necessary data based on a user request.

[0116] A "recording medium" is a log system or database that records the usage of data used by a generative AI model.

[0117] "Remuneration" is the consideration paid to the data provider based on the number of times the data is used.

[0118] "Use count" refers to the number of times a particular piece of data is requested and used by a user.

[0119] A "data request" is a request for data of specific conditions that a user requests from the system via a terminal.

[0120] MODE FOR CARRYING OUT THE INVENTION

[0121] The present invention relates to a system in which data providers upload data via their terminals, and a server receives, classifies, tags, and stores the data in an information storage device. In this system, when a user sends a data request from their terminal, the server generates data according to the request using a generative AI model and sends it to the user's terminal. Furthermore, the server records the usage status of the data used on a recording medium, tallying the number of times each data provider has used it, calculates rewards, and periodically pays them.

[0122] Data collection

[0123] Terminal: The data provider accesses the dedicated web application and clicks the "Upload Data" button. They select the file to upload from their PC (e.g., "Tokyo weather data for March 2023.csv"), enter detailed information such as the data category (e.g., weather data), date (e.g., March 2023), and location (e.g., Tokyo) in the form, and click the upload button to send the data to the server.

[0124] Server: Receives uploaded data files and their details and safely stores them in a temporary folder. The HTTP protocol is used for receiving, and uploaded files are temporarily stored on the server's disk.

[0125] Data storage

[0126] Server: Analyze the data files saved in the temporary folder using an automatic analysis program (e.g., Python's pandas library). During analysis, the data items and formats are checked and the contents are structured.

[0127] Server: Based on the analyzed data, it assigns categories (e.g., weather data) and tags (e.g., temperature, humidity, location, date, etc.). For tagging, it may use a predefined schema file.

[0128] Server: Stores tagged data in an information storage device such as MongoDB, allowing for quick search when retrieving data.

[0129] Requesting and Generating Data

[0130] User: The user accesses a data request form through the web application, where they enter specific criteria (e.g., average temperature in Tokyo in March 2023) and press the submit button.

[0131] Server: Receives user requests and queries the database to find data that matches the criteria. This is done using MongoDB's query functionality.

[0132] Server: Based on the search results, a generative AI model (e.g., TENSORFLOW (registered trademark) or GPT-3 (registered trademark)) is used to generate the requested data. For example, if a user requests "average temperature in Tokyo in March 2023," the generative AI model aggregates temperature data for the relevant period and calculates the average temperature.

[0133] Server: Converts the generated data into JSON format and returns it to the user's device.

[0134] Device: The user's device analyzes the returned data and displays a message on a web page, such as "The average temperature in Tokyo in March 2023 will be 15 degrees."

[0135] Usage Tracking

[0136] Server: Records detailed logs of which data providers' data the generative AI model used and to which users it provided it to, for example, by creating log entries containing information such as data provider ID, data item, and number of uses.

[0137] Server: This information is stored on a recording medium using a log management system such as MySQL (registered trademark) or ElasticSearch (registered trademark).

[0138] Reward calculation and payment

[0139] Server: Periodically (e.g., at the end of each month), aggregate how much data from each data provider has been used. Scan the log data and record the number of times each data provider has been used.

[0140] Server: Calculates rewards based on the number of uses. For example, it is calculated as "10 yen per use."

[0141] Server: Based on the statistical results, the reward is transferred to the data provider's registered bank account. This can be done using a financial API (e.g., Stripe or PayPal).

[0142] Examples of concrete examples and prompts

[0143] 1. Data collection

[0144] Terminal: The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal. He enters "weather data," "March 2023," and "Tokyo" into the form.

[0145] Server: Receives this data and details and stores them in a temporary folder.

[0146] 2. Data Storage

[0147] Server: Analyzes the stored data using the pandas library, adds categories and tags, and stores them in a MongoDB database.

[0148] 3. Requesting and Generating Data

[0149] User: A weather forecaster requests "Average temperature in Tokyo in March 2023."

[0150] Server: Performs queries and calculates average temperatures using a generative AI model.

[0151] Server: Returns the result in JSON and displays "The average temperature in Tokyo in March 2023 will be 15 degrees" on the user's device.

[0152] 4. Usage Tracking

[0153] Server: Logs data usage. Example: A researcher's weather data is used 100 times.

[0154] 5. Calculation and Payment of Rewards

[0155] Server: At the end of the collection period, calculate the number of uses and the total reward. For example, if there are 100 uses, the total reward will be calculated as 10 yen per use, for a total of 1,000 yen.

[0156] Server: Transfers rewards to the data provider's bank account using Stripe API.

[0157] Prompt Sentence Examples

[0158] "What is the average temperature in Tokyo in March 2023?"

[0159] "Please provide a list of temperature and humidity data for Tokyo in March 2023."

[0160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0161] Step 1: Collect data

[0162] Terminal: The data provider accesses a dedicated web application. They log in with their user ID and password and click the "Upload Data" button. They select the file to upload from their PC (e.g., "Tokyo weather data for March 2023.csv"), enter detailed information in the form, such as the data category (e.g., weather data), date (e.g., March 2023), and location (e.g., Tokyo), and then press the submit button. Input: Data file and detailed information. Output: Upload request.

[0163] Server: Receives upload requests and saves data files and details in a temporary folder. Input: Upload request. Output: File saved in the temporary folder.

[0164] Step 2: Store the data

[0165] Server: Reads data files saved in a temporary folder and analyzes them using an automatic analysis program (e.g., Python's pandas library). During analysis, the data items and format are checked and the contents are structured. Input: Data files read from the temporary folder. Output: Structured data.

[0166] Server: Based on the structured data, assigns categories (e.g., weather data) and tags (e.g., temperature, humidity, location, date, etc.). A predefined schema is used for tagging. Input: Structured data. Output: Tagged data.

[0167] Server: Stores tagged data in an information storage device such as MongoDB. Input: Tagged data. Output: Data stored in the database.

[0168] Step 3: Request the data

[0169] User: The user accesses the data request form through the web application, enters specific criteria (e.g., average temperature in Tokyo in March 2023), and presses the submit button. Input: Data request. Output: Request submitted.

[0170] Server: Receives data requests and queries the database to find data that matches the criteria. Input: Data request. Output: Search results.

[0171] Step 4: Generate data

[0172] Server: Based on the search results, a generative AI model (e.g., TensorFlow or GPT-3) is used to generate the requested data. For example, if a user requests "average temperature in Tokyo in March 2023," the generative AI model aggregates temperature data for the relevant period and calculates the average temperature. Input: Search results. Output: Generated data.

[0173] Server: Converts the generated data into JSON format and returns it to the user's device. Input: Generated data. Output: Returned data.

[0174] Step 5: View the generated data

[0175] Terminal: The user's terminal receives the returned data, analyzes it, and displays it. For example, it displays "The average temperature in Tokyo in March 2023 is 15 degrees" on a web page. Input: The returned data. Output: The displayed data.

[0176] Step 6: Track usage

[0177] Server: Records detailed logs of which data providers' data the generative AI model used and to which users it provided it. For example, it creates log entries containing information such as data provider ID, data item, and number of uses. Input: Data usage information. Output: Recorded log.

[0178] Server: This information is stored on a recording medium using a log management system such as MySQL or Elasticsearch. Input: Recorded logs. Output: Stored logs.

[0179] Step 7: Calculating and paying rewards

[0180] Server: Periodically (e.g., at the end of each month), aggregate how much data from each data provider has been used. Scan the log data and count the number of uses for each data provider. Input: Stored logs. Output: Aggregated results of the number of uses.

[0181] Server: Calculates rewards based on the number of uses. For example, it is calculated as "10 yen per use." Input: Aggregated results of number of uses. Output: Reward calculation results.

[0182] Server: Based on the statistical results, the reward is transferred to the data provider's registered bank account. This is done using a financial API (e.g., Stripe or PayPal). Input: Reward calculation results. Output: Transfer completion notification.

[0183] (Application example 1)

[0184] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0185] In the manufacturing field, while the real-time collection and use of various data is important, data collection, management, and optimization are complex and there is a lack of efficient systems. Furthermore, data providers often do not receive fair compensation, which reduces their motivation to provide data. In such an environment, optimizing the manufacturing process is difficult, and as a result, production efficiency is likely to decline.

[0186] 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.

[0187] In this invention, the server

[0188] A means for a data provider to upload data using a terminal;

[0189] means for classifying and tagging the data received by the server and storing it in a database;

[0190] A means for a user to send a data request from a terminal;

[0191] A means for the server to generate data using a generation AI based on a user request and transmit the data to the user terminal;

[0192] a means for logging usage of the data used by the server;

[0193] The server counts the number of uses for each data provider, calculates the reward, and pays it;

[0194] A means for the robot to collect data in real time according to the manufacturing process and upload it to a server;

[0195] a means for the robot to request data required for a particular manufacturing process from the server;

[0196] Includes:

[0197] This will enable efficient data collection, management, and optimization at manufacturing sites, and will also increase motivation for data provision by ensuring appropriate compensation for data providers. Furthermore, the use of generative AI will enable the rapid and accurate generation of requested data, facilitating optimization of manufacturing processes.

[0198] A "data provider" is an entity that generates valuable data related to manufacturing processes or other systems and provides it to the system.

[0199] "Terminal" means an electronic device used by Data Contributors and Users to access the System and to upload or request Data.

[0200] "Server" means a computer system that receives, categorizes, tags, stores data in a database, generates data based on requests, records logs, calculates rewards, and pays out.

[0201] A "user" is an entity that utilizes the system to request specific data and receive the results.

[0202] "Generative AI" is artificial intelligence used to generate necessary data based on user requests.

[0203] A "data request" is an act in which a user requests data required based on specific conditions from the system.

[0204] A "database" is an information system for systematically storing categorized and tagged data.

[0205] A "log" is a digital record generated by a server to record data usage.

[0206] The "number of times data is used" is an index that indicates the frequency with which a user accesses and uses specific data.

[0207] "Remuneration" means monetary or other consideration paid to a Data Provider based on the use of the Data provided by that Data Provider.

[0208] "Robots" are automated machines that collect and request data during the manufacturing process and work in conjunction with a server to optimize operations.

[0209] "Manufacturing process" refers to the series of steps used to produce a product from raw materials.

[0210] The present invention provides a system for collecting data in a factory and optimizing the data. Specific embodiments of the present invention will be described below.

[0211] System Configuration

[0212] This system consists of a data provider terminal, a user terminal, a server, and a factory robot.

[0213] Data provider terminal

[0214] Data providers upload data using terminals. Terminals include devices that can input and send data, such as PCs, smartphones, and tablets. The provided data includes valuable information related to the manufacturing process (e.g., sensor data, work history, quality check data, etc.).

[0215] server

[0216] The server analyzes the received data and categorizes and tags it. When storing it in a database, it organizes it by assigning specific categories and tags (e.g., "weather data," "temperature," "humidity," etc.). It also receives data requests from users and generates appropriate data using generation AI. It also tracks usage and records the number of times each data provider has used the service in a log. It also calculates compensation to data providers for specific periods and transfers it to their registered accounts.

[0217] Factory robots

[0218] Factory robots collect data in real time according to the manufacturing process and upload it to the server. If the robot needs data, it sends a data request to the server and optimizes the manufacturing process based on the data provided by the server.

[0219] Processing flow

[0220] 1. Data Collection and Upload

[0221] Data providers upload data through terminals, and factory robots also use sensors to collect data in real time and send it to a server.

[0222] 2. Data Storage and Classification

[0223] The server analyzes the received data, categorizes and tags it appropriately, and stores it in a database using a database management system (e.g., MySQL).

[0224] 3. Data Request and Generation

[0225] When users and factory robots request specific data, they send conditional requests to the server. The server uses generative AI (e.g., natural language processing models) to generate the required data. For example, a prompt might be entered: "Calculate the average temperature of production line A in October 2023."

[0226] 4. Usage Tracking and Reward Calculation

[0227] The server logs the usage of the generated data and tally the number of times each data provider has used it for a specific period (e.g., the end of the month). Based on this, the reward is calculated and transferred to the data provider's registered account. The software used includes log management tools (e.g., the Python logging module) and data analysis tools (e.g., Pandas).

[0228] Specific examples

[0229] Specific examples of data collection and uploading

[0230] The data provider uploads "Production Line A's October temperature data.csv" from their terminal. The factory robot sends "Production Line A's real-time temperature data for October 2023" to the server.

[0231] Examples of data request and generation

[0232] The user requests "the average temperature of production line A in October 2023." The server uses the generative AI and the "generative AI model" to generate data based on the prompt, "Please calculate the average temperature of production line A in October 2023," and sends it to the user's device.

[0233] In this way, the system can achieve effective data management and optimization of the manufacturing process, while also ensuring fair compensation for data providers.

[0234] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0235] Step 1:

[0236] The data provider uploads data using a terminal. On the terminal, the provider selects a data file and also enters detailed data information (e.g., category, date, location, etc.) through a dedicated upload form. When executed, the data and its details are sent to the server. The input data is, for example, "Temperature data.csv for production line A" and detailed information such as "October 2023, production line A, temperature data," and the output is an HTTP request containing the uploaded data file and the detailed information.

[0237] Step 2:

[0238] The server classifies and tags the received data and stores it in a database. First, it saves the transmitted data file in a temporary folder. Next, it analyzes the content of the data and assigns appropriate categories and tags (e.g., "Temperature," "Humidity," "Production Line A," "October 2023," etc.). After that, it uses a database management system (e.g., MySQL) to store the classified and tagged data in a database. The input is the data file and detailed information received by the server, and the output is the classified and tagged data stored in the database.

[0239] Step 3:

[0240] Users and factory robots request specific data. The user terminal or factory robot accesses the system, inputs the conditions for the required data (e.g., specific period, location, data category, etc.), and sends a request. The input is the conditions requested by the user or robot, and the output is the data request sent to the server.

[0241] Step 4:

[0242] The server receives a user or robot request and searches the database. Based on the search results, a generative AI is used to generate the requested data. For example, a prompt statement such as "Please calculate the average temperature of production line A in October 2023" is input into the generative AI model. The generative AI analyzes the relevant data and calculates the average temperature. The inputs are the search conditions and the prompt statement input into the generative AI, and the output is the calculated average temperature data.

[0243] Step 5:

[0244] The server sends the generated data to the user terminal or factory robot. The generation AI formats the generated data and sends it to the user or robot terminal in a form that can be displayed. The input is the data generated by the generation AI, and the output is the data sent to the user or robot terminal.

[0245] Step 6:

[0246] The server logs the usage of the data used. By recording which data provider's information the generative AI model used and the number of times it was used, the system tracks the details of usage for each data provider. The input is the usage information for the generated data, and the output is the usage data recorded in the log file.

[0247] Step 7:

[0248] The server tally the number of uses for each data provider, calculates the reward, and pays it. At specific intervals, the server tally the number of uses and calculates the reward for the data provider based on a predetermined reward unit price. The reward is then transferred to the provider's registered account. The input is the number of uses for each data provider, and the output is the reward calculation result and a transfer completion notification.

[0249] 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.

[0250] This invention relates to a system in which data providers upload data via their devices, and a server receives, classifies, tags, and stores the data in a database. The system also incorporates an emotion engine that recognizes user emotions and reflects them in data generation. When a user sends a data request from their device, the system's generation AI and emotion engine generate data according to the request and send it to the user's device. Furthermore, the server records the usage status of the data in a log, tallying the number of times each data provider uses the data, and calculates and periodically pays rewards.

[0251] Program processing

[0252] 1. Data Collection:

[0253] Terminal: The data provider logs in to the system using a terminal. The data provider accesses a dedicated upload form, selects a data file, enters detailed data information (e.g., category, date, location, etc.), and presses the upload button, which sends the data and detailed information to the server.

[0254] 2. Data storage:

[0255] Server: The server receives the data files and detailed information sent from the device and stores them in a temporary folder. It then analyzes the data files and assigns categories and tags based on their content (e.g., "weather data," "temperature," "humidity," etc.). The categorized and tagged data is then stored in a database.

[0256] 3. Requesting and generating data:

[0257] User: The user accesses the system through a terminal, inputs the conditions for the required data (e.g., a specific period, location, category, etc.), and submits a request.

[0258] Terminal: At the same time, the user's emotional data (e.g., emotional state from text analysis or data from emotion recognition sensors) is acquired by the emotion engine.

[0259] Server: The server receives the user's request and emotion data, and the generation AI searches the database to extract data that matches the conditions.

[0260] Server: The generation AI generates data that is optimal for the user based on the emotional data provided by the emotion engine. For example, if a user requests the "average temperature in Tokyo in March 2023" and the "feeling relaxed" is detected based on the emotional data, the generation AI will present the data in a format that gives a feeling of relaxation.

[0261] Server: Returns the generated data to the user's terminal.

[0262] 4. Usage Tracking:

[0263] Server: Logs details of the data used by the generating AI (such as which data provider's data was used and how many times it was used).

[0264] 5. Calculation and Payment of Rewards:

[0265] Server: The server tally the number of times each data provider uses data for a specific period (e.g., the end of the month) and calculates the reward (e.g., "10 yen per use").

[0266] Server: Transfers the calculated reward to the registered account of the data provider.

[0267] Specific examples

[0268] 1. Data Collection:

[0269] Terminal: The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal and enters detailed information such as "weather data," "March 2023," and "Tokyo" into the form. When the researcher presses the upload button, the data is sent to the server.

[0270] Server: Receives data and detailed information and saves it in a temporary folder. Analyzes it, assigns tags such as "weather data," "temperature," "humidity," "March 2023," and "Tokyo," and stores it in a database.

[0271] 2. Requesting and generating data:

[0272] User: A weather forecaster requests "Average temperature in Tokyo in March 2023" on their device. The emotion engine detects "tension" in the user's text.

[0273] Server: Receives the request and emotion data, and the generation AI searches the database to extract the relevant data.

[0274] Server: The generation AI calculates and presents average temperature data in a format that gives a sense of relaxation suitable for users in a "tense" state.

[0275] Server: Provides the generated results to the user's device. The device displays, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[0276] 3. Usage Tracking:

[0277] Server: Logs which data is used and how much. For example, it records how many times a researcher's weather data is used.

[0278] 4. Calculation and payment of rewards:

[0279] Server: At the end of the collection period, the server counts the 100 times the researcher's data has been used.

[0280] Server: Calculates a reward of 10 yen per 100 uses and transfers 1,000 yen to the researcher.

[0281] In this way, the system and program of the present invention improve the user experience by incorporating emotion recognition, increasing the motivation of data providers while enabling users to quickly and accurately obtain the data they need.

[0282] The processing flow will be explained below.

[0283] Step 1:

[0284] Terminal: The data provider logs into the system using a terminal.

[0285] Terminal: The data provider accesses the dedicated upload form, selects the data file (e.g., "Tokyo weather data for March 2023.csv"), enters the data details (category, date, location), and presses the upload button.

[0286] Step 2:

[0287] Server: The server receives the data file and detailed information sent from the device.

[0288] Server: Save the received data in a temporary folder.

[0289] Step 3:

[0290] Server: Analyzes the stored data files and assigns categories and tags based on their content ("weather data," "temperature," "humidity," "March 2023," "Tokyo").

[0291] Server: Stores the classified and tagged data in a database.

[0292] Step 4:

[0293] User: The user accesses the system through a terminal and navigates to the data request form.

[0294] User: Enters the criteria for the data they need (specific time period, location, category, etc.) and submits a request.

[0295] Step 5:

[0296] Device: The emotion engine collects the user's emotional data. For example, it analyzes the user's emotional state, such as "tension," from the user's text input and data from the facial recognition camera.

[0297] Terminal: The emotion engine sends the user's emotion data to the server.

[0298] Step 6:

[0299] Server: The server receives the user's request and emotion data.

[0300] Server: Searches the database and extracts data that matches the conditions.

[0301] Step 7:

[0302] Server: The generation AI generates data based on the search results. For example, if a user requests "average temperature in Tokyo in March 2023" and the emotional data indicates "tension," the generation AI generates data in a format that gives a sense of relaxation.

[0303] Server: Sends the generated data to the user's terminal.

[0304] Step 8:

[0305] Terminal: The user's terminal receives the generated data and displays it on the screen. For example, it displays "The average temperature in Tokyo in March 2023 will be 15 degrees."

[0306] Step 9:

[0307] Server: Records details of the data used by the generating AI in a log, including which data provider's data was used and the number of times it was used.

[0308] Step 10:

[0309] Server: For each specific period (e.g., at the end of the month), the number of times data is used for each data provider is tallied.

[0310] Server: Calculates the reward based on the aggregated results. For example, 10 yen per use.

[0311] Step 11:

[0312] Server: Transfers the calculated reward to the registered account of the data provider.

[0313] In this way, data providers, users, and servers can cooperate to operate a comprehensive data provision and utilization system, thereby ensuring efficient and fair data provision and an improved user experience.

[0314] Example 2

[0315] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0316] Existing data management systems not only lack the ability to upload, classify, tag, and store data in a database, but also lack the ability to provide optimal data by utilizing user emotion recognition. As a result, it is difficult to provide data that meets user needs, and the user experience cannot be improved. In addition, the process of calculating and paying rewards to data providers is often manual and inefficient.

[0317] 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.

[0318] In this invention, the server includes: means for a data provider to upload data using a terminal; means for analyzing, classifying, and tagging the received data and storing it in a database; means for a user to send a data request from the terminal; means for the terminal to acquire the user's emotional data; means for the server to generate data using a generation AI based on the user's request and emotional data and send it to the user's terminal; means for the server to record in a log how the data has been used; and means for the server to tally the number of times each data provider has used the data and calculate and pay a reward. This makes it possible to provide optimal data according to the user's emotions, improving the user experience and the motivation of data providers at the same time.

[0319] "Data Provider" means any person or entity that uploads and provides data through the System.

[0320] "Terminal" means a computer or mobile device used by a User or Data Contributor to access and operate the System.

[0321] "Server" refers to a central computer system that receives, processes, stores, and distributes data.

[0322] A "database" refers to a structured collection of information that allows a server to classify, tag, and efficiently store and manage data.

[0323] "Generative AI" refers to artificial intelligence that extracts necessary data from a database based on a user request and then generates the data in the optimal format based on the user's emotional data.

[0324] An "emotion engine" refers to software or hardware that has the ability to analyze a user's emotional data and provide that emotional state to a generative AI.

[0325] "Log" refers to a file or database that records system activity, such as data usage.

[0326] "Remuneration" refers to monetary compensation paid according to the number of times the data provided by the data provider is used.

[0327] The "temporary folder" refers to a folder for temporarily storing data received by the server.

[0328] "User" means any person or entity accessing the System and making a request for data.

[0329] The present invention relates to a system in which users and data providers process data mutually through terminals, and a server manages and optimizes this. Specific embodiments are described below.

[0330] Data upload by data contributors

[0331] Terminal

[0332] Data providers log in to the system using a terminal. To log in, they use a username and password. After logging in, they access a dedicated upload form. Using this form, they select a data file (e.g., "weatherdata.csv") and enter detailed data information (e.g., category, date, location, etc.). When they press the upload button, the data file and its details are sent to the server.

[0333] Server processing and storage of data

[0334] server

[0335] The server saves the received data files and detailed information in a temporary folder. The server then analyzes the data files and assigns categories and tags based on their content (e.g., "Weather Data," "Temperature," "Humidity," "March 2023," "Tokyo," etc.). Python libraries such as pandas and NumPy can be used for analysis. The classified and tagged data is stored in a database using an RDBMS such as MySQL or PostgreSQL.

[0336] User data requests and emotion data acquisition

[0337] Terminal

[0338] The user accesses the system from their device, inputs the conditions for the required data (e.g., period, location, category, etc.), and sends a request. At the same time, the user's emotional data is acquired. To acquire the emotional data, a text analysis library (e.g., TextBlob) or an emotion recognition sensor is used.

[0339] Server-generated data

[0340] server

[0341] The server receives the user's request and emotion data. The generation AI then searches the database based on the request conditions and extracts matching data. The generation AI can use OpenAI's (registered trademark) GPT model, for example. Furthermore, it generates data in a format that is optimal for the user based on the emotion data provided by the emotion engine. For example, if a user requests "average temperature in Tokyo in March 2023" and "relaxed feeling" is detected based on the emotion data, the generation AI presents the data in a format that gives a sense of relaxation.

[0342] Returning data

[0343] server

[0344] The server sends the generated data back to the user's device, which displays, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[0345] Usage tracking and reward calculation

[0346] server

[0347] The server logs details of the data used by the generating AI (such as which data provider's data was used and the number of times it was used). In addition, the server aggregates the number of times each data provider used it for a specific period (e.g., the end of the month) and calculates the reward (e.g., "10 yen per use"). The calculated reward is transferred to the data provider's registered account.

[0348] Specific examples

[0349] 1. Data provider operations

[0350] The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal, enters detailed information such as "weather data," "March 2023," and "Tokyo" into the form, and presses the upload button.

[0351] The server receives the data and detailed information, saves it in a temporary folder, analyzes it, assigns tags such as "weather data," "temperature," "humidity," "March 2023," and "Tokyo," and stores it in a database.

[0352] 2. User Operation

[0353] A weather forecaster requests "average temperature in Tokyo in March 2023." The emotion engine detects "tension" from the user's text.

[0354] The server receives the request and emotion data, and the generation AI searches the database to extract the relevant data.

[0355] The generating AI presents average temperature data in a format that gives a sense of relaxation suitable for users in a state of "tension."

[0356] The server provides the generated results to the user's device, which displays the message, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[0357] This will enable the realization of a system that improves the user experience by incorporating emotion recognition, increases the motivation of data providers, and enables users to quickly and accurately obtain the data they need.

[0358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0359] Step 1:

[0360] Data provider login

[0361] User: The data provider logs into the system using a terminal. As input, they use their username and password. As output, they are granted access to the system and a dedicated upload form is displayed.

[0362] Step 2:

[0363] Uploading data

[0364] User: The data provider selects a data file in the upload form and enters detailed information (category, date, location, etc.). The input is the data file (e.g., "weatherdata.csv") and the detailed information. The output is the data file and the detailed information sent from the terminal to the server.

[0365] Step 3:

[0366] Receiving and temporarily storing data

[0367] Server: Receives data files and detailed information and saves them in a temporary folder. As input, it receives data files and detailed information sent from the device. As output, it saves the data files in a temporary folder.

[0368] Step 4:

[0369] Data analysis and tagging

[0370] Server: Analyzes data files saved in a temporary folder. The input is the saved data file. Specifically, the data file is analyzed using Python libraries such as pandas and NumPy. As output, categories and tags (e.g., "weather data," "temperature," "humidity," "March 2023," "Tokyo," etc.) are assigned based on the content of the data.

[0371] Step 5:

[0372] Storage in the database

[0373] Server: Stores the classified and tagged data in an RDBMS such as MySQL or PostgreSQL. The input is a tagged data file. The specific operation is to store the data in the database using SQL queries. The output is structured data stored in the database.

[0374] Step 6:

[0375] Sending a Data Request

[0376] User: The user inputs the conditions for the required data and submits a request. As input, there are the conditions for the required data (e.g., period, location, category, etc.). As output, the request is sent to the server. At the same time, emotion data is obtained.

[0377] Step 7:

[0378] Acquiring emotion data

[0379] Device: Obtain user emotional data using a text analysis library (e.g., TextBlob) or an emotion recognition sensor. Inputs include user text information and sensor data. Outputs include analyzed emotional data.

[0380] Step 8:

[0381] Receiving requests and emotion data

[0382] Server: Receives user request data and emotion data. As input, there is the request data and emotion data sent by the user. As output, the received data is passed to the generation AI.

[0383] Step 9:

[0384] Data retrieval and generation

[0385] Server: The generation AI searches the database based on the request conditions and emotion data, and extracts matching data. The inputs are the request conditions and emotion data. The specific operation is to search the database using the generation AI (e.g., OpenAI's GPT model). The output is the extraction of matching data.

[0386] Step 10:

[0387] Optimal Data Generation

[0388] Server: The generation AI generates the data in the optimal data format for the user based on the emotion data provided by the emotion engine. The inputs are emotion data and extracted data. Specifically, the generation AI creates a data format that matches the emotion. The output is optimized data.

[0389] Step 11:

[0390] Returning data

[0391] Server: Returns the generated data to the user's device. The input is the optimized data. The output is the data displayed on the user's device. The user's device displays, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[0392] Step 12:

[0393] Usage record

[0394] Server: Records details of the data used by the generating AI in a log. The input is the data used and its details. Specific operations include recording operations to log files and databases. The output is a record of usage.

[0395] Step 13:

[0396] Reward calculation and payment

[0397] Server: Aggregates the number of uses by each data provider for a specific period and calculates rewards. The input is usage data from the log. Specific operations include aggregation and reward calculation. The output is the calculated reward being deposited into the data provider's registered account.

[0398] (Application example 2)

[0399] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0400] Conventional data provision systems have the problem that the methods for utilizing data uploaded by data providers and the management of its usage status are complicated, and the data provided to users is simply in response to requests, making it difficult to provide optimal information according to the user's emotional state.In particular, autonomous vehicles are required to properly recognize the driver's emotions and provide optimal navigation and assistance based on those emotions, but conventional systems are not able to adequately address this issue either.

[0401] 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: a means for a data provider to upload data using a terminal; a means for the server to classify and tag the received data and store it in a database; a means for a user to send a data request from a terminal; a means for the server to generate data using a generation AI based on the user's request and send it to the user's terminal; a means for the server to log the usage status of the data used by the server; a means for the server to tally the number of uses for each data provider and calculate and pay a reward; a means for the server to analyze the driver's emotional data and apply it to the navigation system; and a means for the navigation system to generate an optimal route based on the emotional data and present it to the driver. This makes it easier for data providers to understand how their data is being used, and enables users to receive optimal data and navigation according to their emotional state.

[0402] "Data Provider" means an individual or entity that generates and uploads data to the system.

[0403] "Terminal" refers to a device used by data providers and users to upload data, send requests, etc.

[0404] A "server" is a computer that is the core of the system and is a device that performs various processes such as receiving, classifying, storing, generating, and logging data.

[0405] "Generative AI" is artificial intelligence that generates data based on user requests and sends it to the user's device.

[0406] A "database" is a storage device where the server stores classified and tagged data.

[0407] "Emotion data" is information indicating the emotional state of the driver analyzed based on data such as facial expressions, voice, and heart rate.

[0408] A "navigation system" is a system that generates an optimal route based on the driver's emotional data and presents it to the driver.

[0409] A "request" is a request made by a user to the system through a terminal for specific data or navigation information.

[0410] "Usage status" is information indicating the usage history and frequency of data generated by the server using the generated AI.

[0411] "Remuneration" means monetary compensation received by a data provider for the use of their data.

[0412] A "log" is data that records a series of operations and data usage status performed on a server.

[0413] A "prompt" is text data containing instructions or questions that are input into a generative AI model.

[0414] A "temporary folder" is an area where the server temporarily stores data received.

[0415] This invention provides an emotion-responsive navigation system for autonomous vehicles. This system provides optimal navigation according to the emotional state of the driver of the autonomous vehicle, realizing a comfortable driving environment.

[0416] First, the system uses the following hardware and software:

[0417] Hardware:

[0418] Internal camera (for driver facial expression recognition)

[0419] Various sensors (heart rate sensor, voice recognition microphone, etc.)

[0420] In-vehicle display

[0421] server

[0422] software:

[0423] Emotion recognition engine: Emotional AI SDK

[0424] Generative AI model: OpenAI GPT-4 (registered trademark)

[0425] Cloud database: AWS (registered trademark) RDS

[0426] Navigation data: Google® Maps API

[0427] When the driver gets into the vehicle, the internal camera and sensors collect emotional data such as facial expressions, voice, and heart rate in real time, which are then transmitted to the server via the terminal.

[0428] The server analyzes the received emotion data and identifies the driver's current emotional state using the Emotional AI SDK. Identified emotional states include "stress," "fatigue," and "relaxation."

[0429] The driver requests a destination and navigation conditions through the in-car display or voice assistant, and emotion data is sent to the server at the same time.

[0430] The server generates optimal navigation data based on the received request and emotion data using a generative AI model, OpenAI GPT-4, and Google Maps API to obtain the necessary navigation data.

[0431] For example, if a driver is in a "stressed" state, the generative AI model will select a scenic route that avoids crowds and encourages relaxation. If a driver is feeling "fatigued," the model will recommend a route with rest areas and toilets.

[0432] The generated navigation data is displayed on the driver's in-vehicle display, allowing the driver to receive navigation information optimized for their emotional state.

[0433] For example, a sample prompt for a generative AI model might look like this:

[0434] "My emotional state is 'stressed'. I will be driving from Tokyo to Yokohama at 1:00 PM on October 20, 2023. Please advise me of a relaxing route that avoids crowds."

[0435] This system allows drivers to relax even in stressful driving situations, resulting in an overall more comfortable driving experience.The invention also takes into consideration the interests of data providers, automatically calculating and paying compensation for data used based on how the data is used.

[0436] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0437] Step 1:

[0438] The server collects the driver's emotional data in real time from the internal camera and various sensors. Specifically, the camera recognizes facial expressions, and the sensors acquire heart rate and voice data. Based on this input data, the Emotional AI SDK is used to analyze the driver's emotional state and output it as emotional data.

[0439] Step 2:

[0440] The device receives a navigation request from the driver. The driver inputs the destination and navigation conditions through the display or voice assistant. For example, a condition such as "I want to avoid congestion" is specified. The request data is sent to the server.

[0441] Step 3:

[0442] The server receives the emotion data obtained in step 1 and the request data obtained in step 2. Based on this data, it processes the input using a generative AI model (OpenAI GPT-4) and generates appropriate navigation data. This generation process takes into account relaxing routes and routes that avoid congestion.

[0443] Step 4:

[0444] The server then connects the generated navigation data to the Google Maps API to obtain more detailed route information. This completes the final navigation data, which is then ready to be provided to the driver. The output data is optimized route information.

[0445] Step 5:

[0446] The generated navigation data is sent to the driver's device, and route information is displayed on the in-vehicle display for the driver to review. In addition, the voice assistant provides navigation guidance, improving driving safety and comfort.

[0447] Step 6:

[0448] The server records the usage of navigation data in a log. This logs information such as how much data from which data provider was used and details of the data used by the generating AI. This accumulates basic data for subsequent reward calculations.

[0449] Step 7:

[0450] The server tally up the number of times data providers have used the service for a specific period, such as the end of the month. It calculates how much data from each data provider has been used and calculates rewards based on that. The calculated reward data is then transferred to the data provider's registered account.

[0451] 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.

[0452] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0453] 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.

[0454] [Second embodiment]

[0455] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0467] This invention relates to a system in which data providers upload data via their devices, and a server receives, classifies, tags, and stores the data in a database. In this system, when a user sends a data request from their device, the server uses a generation AI to generate data according to the request and sends it to the user's device. Furthermore, the server records the usage status of the data in a log, tallying the number of times each data provider uses it, calculates rewards, and periodically pays them.

[0468] Program processing

[0469] 1. Data Collection:

[0470] Terminal: The data provider uses the terminal to access a dedicated upload form. The data provider selects the data file they own, enters detailed information about the data (e.g., data category, date, location, etc.), and presses the upload button to send the data to the server.

[0471] Server: The server receives the data file and its detailed information sent from the terminal.

[0472] 2. Data storage:

[0473] Server: The server saves the received data files in a temporary folder.

[0474] Server: Analyzes the contents of data files and classifies them by category and tag. Appropriate tags (e.g., "weather data," "temperature," "humidity," etc.) are assigned to each data.

[0475] Server: Stores the classified and tagged data in a database.

[0476] 3. Requesting and generating data:

[0477] User: The user accesses the system through a terminal, inputs the conditions for the data they require (e.g., a specific period, location, data category, etc.), and submits a request.

[0478] Server: The server receives the user's request, searches the database, and extracts data that matches the criteria.

[0479] Server: The AI ​​generates the requested data based on the search results. For example, if a user requests "average temperature in Tokyo in March 2023," the AI ​​aggregates the temperature data for the relevant period and calculates the average temperature.

[0480] Server: Returns the generated data to the user's terminal.

[0481] Terminal: The user's terminal displays the generated data.

[0482] 4. Usage Tracking:

[0483] Server: The server logs which data providers' data the generating AI used and how much of it was used, thereby accurately understanding the data usage status of each data provider.

[0484] 5. Calculation and Payment of Rewards:

[0485] Server: The server aggregates the amount of use of data from each data provider for each specific period (for example, at the end of the month).

[0486] Server: Based on the aggregated results, calculate the reward for each data provider. For example, calculate the amount based on the total number of uses, assuming "10 yen per use."

[0487] Server: Transfers the calculated reward to the registered account of the data provider.

[0488] Specific examples

[0489] 1. Data Collection:

[0490] Terminal: The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal. Detailed information such as "weather data," "March 2023," and "Tokyo" is entered into the form.

[0491] Server: Receives the data and details and stores them in a temporary folder.

[0492] 2. Data storage:

[0493] Server: Analyzes the received data and tags it with information such as "weather data," "temperature," "humidity," "March 2023," and "Tokyo."

[0494] Server: Stores the tagged data in a database.

[0495] 3. Requesting and generating data:

[0496] User: A weather forecaster requests "average temperature in Tokyo in March 2023" on their device.

[0497] Server: Receives the request and searches the database. The relevant data is extracted and the generating AI calculates the average temperature.

[0498] Server: Provides the calculation results to the user's terminal.

[0499] Terminal: Display "The average temperature in Tokyo in March 2023 will be 15 degrees" on the user terminal.

[0500] 4. Usage Tracking:

[0501] Server: Logs which data is used and how much. For example, it records how many times a researcher's weather data is used.

[0502] 5. Calculation and Payment of Rewards:

[0503] Server: At the end of the collection period, the server counts the 100 times the researcher's data has been used.

[0504] Server: Calculates a reward of 10 yen per 100 uses and transfers 1,000 yen to the researcher.

[0505] In this way, the system and program of the present invention enable users to quickly and accurately obtain the data they need while increasing the motivation of data providers.

[0506] The processing flow will be explained below.

[0507] Step 1:

[0508] Terminal: The data provider logs into the system using a terminal.

[0509] Terminal: The data provider accesses the upload form, selects the data file, and enters data details (category, date, location, etc.).

[0510] Terminal: When the data provider presses the upload button, the terminal sends the data file and detailed information to the server.

[0511] Step 2:

[0512] Server: The server receives the data file and detailed information sent from the device.

[0513] Server: Save the received data in a temporary folder.

[0514] Step 3:

[0515] Server: Analyzes the stored data files and assigns categories and tags based on the data content. For example, tags such as "weather data," "temperature," and "humidity" are assigned.

[0516] Server: Stores the classified and tagged data in a database.

[0517] Step 4:

[0518] User: The user accesses the system through a terminal and navigates to the data request form.

[0519] User: Enters the criteria for the data they need (e.g., a specific time period, location, data category, etc.) and submits a request.

[0520] Step 5:

[0521] Server: The server receives the user's request and searches the database to extract data that matches the criteria.

[0522] Step 6:

[0523] Server: The generation AI generates the requested data based on the search results. For example, if a request is made for "average temperature in Tokyo in March 2023," the generation AI aggregates the relevant data and calculates the average temperature.

[0524] Server: Returns the generated data to the user's terminal.

[0525] Step 7:

[0526] Terminal: The user's terminal receives the generated data and displays it on the screen.

[0527] Step 8:

[0528] Server: Records details of the data used by the generating AI in a log, including which data provider's data was used and the number of times it was used.

[0529] Step 9:

[0530] Server: For each specific period (e.g., at the end of the month), the number of times data is used for each data provider is tallied.

[0531] Server: Calculates the reward based on the aggregated results. For example, 10 yen per use.

[0532] Step 10:

[0533] Server: Transfers the calculated reward to the registered account of the data provider.

[0534] In this way, data providers, users, and servers cooperate to operate the system, ensuring efficient and fair provision and use of data.

[0535] Example 1

[0536] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0537] In recent years, data collection, management, and provision have become important issues in many industries, but there is a lack of a system that can efficiently manage data uploaded by various data providers, quickly provide necessary data to users, and distribute appropriate rewards to data providers.In addition, there is a challenge in accurately understanding the usage status of the data uploaded by data providers and paying rewards in a transparent manner.

[0538] 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.

[0539] In this invention, the server includes: a means for a data provider to upload data using a terminal; a means for the server to classify and tag the received data and store it in an information storage device; a means for a user to send a data request from the terminal; a means for the server to generate data using a generative AI model based on the user request and send it to the user terminal; a means for the server to record the usage status of the data used by the server on a recording medium; and a means for the server to tally the number of times each data provider uses the data and calculate and pay rewards. This enables efficient management of data uploaded by data providers, fast and accurate provision of data to users, and appropriate distribution of rewards to data providers.

[0540] A "data provider" is a person or organization that uses a terminal to upload data to the system.

[0541] "Terminal" means a computing device used by a user or data provider to access the system and to send and receive data.

[0542] A "server" is a central computing system that receives, stores, analyzes, and generates data.

[0543] An "information storage device" is a database or storage system that stores data persistently after it has been classified and tagged.

[0544] A "generative AI model" is an artificial intelligence model used to generate necessary data based on a user request.

[0545] A "recording medium" is a log system or database that records the usage of data used by a generative AI model.

[0546] "Remuneration" is the consideration paid to the data provider based on the number of times the data is used.

[0547] "Use count" refers to the number of times a particular piece of data is requested and used by a user.

[0548] A "data request" is a request for data of specific conditions that a user requests from the system via a terminal.

[0549] MODE FOR CARRYING OUT THE INVENTION

[0550] The present invention relates to a system in which data providers upload data via their terminals, and a server receives, classifies, tags, and stores the data in an information storage device. In this system, when a user sends a data request from their terminal, the server generates data according to the request using a generative AI model and sends it to the user's terminal. Furthermore, the server records the usage status of the data used on a recording medium, tallying the number of times each data provider has used it, calculates rewards, and periodically pays them.

[0551] Data collection

[0552] Terminal: The data provider accesses the dedicated web application and clicks the "Upload Data" button. They select the file to upload from their PC (e.g., "Tokyo weather data for March 2023.csv"), enter detailed information such as the data category (e.g., weather data), date (e.g., March 2023), and location (e.g., Tokyo) in the form, and click the upload button to send the data to the server.

[0553] Server: Receives uploaded data files and their details and safely stores them in a temporary folder. The HTTP protocol is used for receiving, and uploaded files are temporarily stored on the server's disk.

[0554] Data storage

[0555] Server: Analyze the data files saved in the temporary folder using an automatic analysis program (e.g., Python's pandas library). During analysis, the data items and formats are checked and the contents are structured.

[0556] Server: Based on the analyzed data, it assigns categories (e.g., weather data) and tags (e.g., temperature, humidity, location, date, etc.). For tagging, it may use a predefined schema file.

[0557] Server: Stores tagged data in an information storage device such as MongoDB, allowing for quick search when retrieving data.

[0558] Requesting and Generating Data

[0559] User: The user accesses a data request form through the web application, where they enter specific criteria (e.g., average temperature in Tokyo in March 2023) and press the submit button.

[0560] Server: Receives user requests and queries the database to find data that matches the criteria. This is done using MongoDB's query functionality.

[0561] Server: Based on the search results, a generative AI model (e.g., TensorFlow or GPT-3) is used to generate the requested data. For example, if a user requests "average temperature in Tokyo in March 2023," the generative AI model aggregates temperature data for the relevant period and calculates the average temperature.

[0562] Server: Converts the generated data into JSON format and returns it to the user's device.

[0563] Device: The user's device analyzes the returned data and displays a message on a web page, such as "The average temperature in Tokyo in March 2023 will be 15 degrees."

[0564] Usage Tracking

[0565] Server: Records detailed logs of which data providers' data the generative AI model used and to which users it provided it to, for example, by creating log entries containing information such as data provider ID, data item, and number of uses.

[0566] Server: This information is stored on a recording medium using a log management system such as MySQL or Elasticsearch.

[0567] Reward calculation and payment

[0568] Server: Periodically (e.g., at the end of each month), aggregate how much data from each data provider has been used. Scan the log data and record the number of times each data provider has been used.

[0569] Server: Calculates rewards based on the number of uses. For example, it is calculated as "10 yen per use."

[0570] Server: Based on the statistical results, the reward is transferred to the data provider's registered bank account. This can be done using a financial API (e.g., Stripe or PayPal).

[0571] Examples of concrete examples and prompts

[0572] 1. Data collection

[0573] Terminal: The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal. He enters "weather data," "March 2023," and "Tokyo" into the form.

[0574] Server: Receives this data and details and stores them in a temporary folder.

[0575] 2. Data Storage

[0576] Server: Analyzes the stored data using the pandas library, adds categories and tags, and stores them in a MongoDB database.

[0577] 3. Requesting and Generating Data

[0578] User: A weather forecaster requests "Average temperature in Tokyo in March 2023."

[0579] Server: Performs queries and calculates average temperatures using a generative AI model.

[0580] Server: Returns the result in JSON and displays "The average temperature in Tokyo in March 2023 will be 15 degrees" on the user's device.

[0581] 4. Usage Tracking

[0582] Server: Logs data usage. Example: A researcher's weather data is used 100 times.

[0583] 5. Calculation and Payment of Rewards

[0584] Server: At the end of the collection period, calculate the number of uses and the total reward. For example, if there are 100 uses, the total reward will be calculated as 10 yen per use, for a total of 1,000 yen.

[0585] Server: Transfers rewards to the data provider's bank account using Stripe API.

[0586] Prompt Sentence Examples

[0587] "What is the average temperature in Tokyo in March 2023?"

[0588] "Please provide a list of temperature and humidity data for Tokyo in March 2023."

[0589] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0590] Step 1: Collect data

[0591] Terminal: The data provider accesses a dedicated web application. They log in with their user ID and password and click the "Upload Data" button. They select the file to upload from their PC (e.g., "Tokyo weather data for March 2023.csv"), enter detailed information in the form, such as the data category (e.g., weather data), date (e.g., March 2023), and location (e.g., Tokyo), and then press the submit button. Input: Data file and detailed information. Output: Upload request.

[0592] Server: Receives upload requests and saves data files and details in a temporary folder. Input: Upload request. Output: File saved in the temporary folder.

[0593] Step 2: Store the data

[0594] Server: Reads data files saved in a temporary folder and analyzes them using an automatic analysis program (e.g., Python's pandas library). During analysis, the data items and format are checked and the contents are structured. Input: Data files read from the temporary folder. Output: Structured data.

[0595] Server: Based on the structured data, assigns categories (e.g., weather data) and tags (e.g., temperature, humidity, location, date, etc.). A predefined schema is used for tagging. Input: Structured data. Output: Tagged data.

[0596] Server: Stores tagged data in an information storage device such as MongoDB. Input: Tagged data. Output: Data stored in the database.

[0597] Step 3: Request the data

[0598] User: The user accesses the data request form through the web application, enters specific criteria (e.g., average temperature in Tokyo in March 2023), and presses the submit button. Input: Data request. Output: Request submitted.

[0599] Server: Receives data requests and queries the database to find data that matches the criteria. Input: Data request. Output: Search results.

[0600] Step 4: Generate data

[0601] Server: Based on the search results, a generative AI model (e.g., TensorFlow or GPT-3) is used to generate the requested data. For example, if a user requests "average temperature in Tokyo in March 2023," the generative AI model aggregates temperature data for the relevant period and calculates the average temperature. Input: Search results. Output: Generated data.

[0602] Server: Converts the generated data into JSON format and returns it to the user's device. Input: Generated data. Output: Returned data.

[0603] Step 5: View the generated data

[0604] Terminal: The user's terminal receives the returned data, analyzes it, and displays it. For example, it displays "The average temperature in Tokyo in March 2023 is 15 degrees" on a web page. Input: The returned data. Output: The displayed data.

[0605] Step 6: Track usage

[0606] Server: Records detailed logs of which data providers' data the generative AI model used and to which users it provided it. For example, it creates log entries containing information such as data provider ID, data item, and number of uses. Input: Data usage information. Output: Recorded log.

[0607] Server: This information is stored on a recording medium using a log management system such as MySQL or Elasticsearch. Input: Recorded logs. Output: Stored logs.

[0608] Step 7: Calculating and paying rewards

[0609] Server: Periodically (e.g., at the end of each month), aggregate how much data from each data provider has been used. Scan the log data and count the number of uses for each data provider. Input: Stored logs. Output: Aggregated results of the number of uses.

[0610] Server: Calculates rewards based on the number of uses. For example, it is calculated as "10 yen per use." Input: Aggregated results of number of uses. Output: Reward calculation results.

[0611] Server: Based on the statistical results, the reward is transferred to the data provider's registered bank account. This is done using a financial API (e.g., Stripe or PayPal). Input: Reward calculation results. Output: Transfer completion notification.

[0612] (Application example 1)

[0613] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0614] In the manufacturing field, while the real-time collection and use of various data is important, data collection, management, and optimization are complex and there is a lack of efficient systems. Furthermore, data providers often do not receive fair compensation, which reduces their motivation to provide data. In such an environment, optimizing the manufacturing process is difficult, and as a result, production efficiency is likely to decline.

[0615] 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.

[0616] In this invention, the server

[0617] A means for a data provider to upload data using a terminal;

[0618] means for classifying and tagging the data received by the server and storing it in a database;

[0619] A means for a user to send a data request from a terminal;

[0620] A means for the server to generate data using a generation AI based on a user request and transmit the data to the user terminal;

[0621] a means for logging usage of the data used by the server;

[0622] The server counts the number of uses for each data provider, calculates the reward, and pays it;

[0623] A means for the robot to collect data in real time according to the manufacturing process and upload it to a server;

[0624] a means for the robot to request data required for a particular manufacturing process from the server;

[0625] Includes:

[0626] This will enable efficient data collection, management, and optimization at manufacturing sites, and will also increase motivation for data provision by ensuring appropriate compensation for data providers. Furthermore, the use of generative AI will enable the rapid and accurate generation of requested data, facilitating optimization of manufacturing processes.

[0627] A "data provider" is an entity that generates valuable data related to manufacturing processes or other systems and provides it to the system.

[0628] "Terminal" means an electronic device used by Data Contributors and Users to access the System and to upload or request Data.

[0629] "Server" means a computer system that receives, categorizes, tags, stores data in a database, generates data based on requests, records logs, calculates rewards, and pays out.

[0630] A "user" is an entity that utilizes the system to request specific data and receive the results.

[0631] "Generative AI" is artificial intelligence used to generate necessary data based on user requests.

[0632] A "data request" is an act in which a user requests data required based on specific conditions from the system.

[0633] A "database" is an information system for systematically storing categorized and tagged data.

[0634] A "log" is a digital record generated by a server to record data usage.

[0635] The "number of times data is used" is an index that indicates the frequency with which a user accesses and uses specific data.

[0636] "Remuneration" means monetary or other consideration paid to a Data Provider based on the use of the Data provided by that Data Provider.

[0637] "Robots" are automated machines that collect and request data during the manufacturing process and work in conjunction with a server to optimize operations.

[0638] "Manufacturing process" refers to the series of steps used to produce a product from raw materials.

[0639] The present invention provides a system for collecting data in a factory and optimizing the data. Specific embodiments of the present invention will be described below.

[0640] System Configuration

[0641] This system consists of a data provider terminal, a user terminal, a server, and a factory robot.

[0642] Data provider terminal

[0643] Data providers upload data using terminals. Terminals include devices that can input and send data, such as PCs, smartphones, and tablets. The provided data includes valuable information related to the manufacturing process (e.g., sensor data, work history, quality check data, etc.).

[0644] server

[0645] The server analyzes the received data and categorizes and tags it. When storing it in a database, it organizes it by assigning specific categories and tags (e.g., "weather data," "temperature," "humidity," etc.). It also receives data requests from users and generates appropriate data using generation AI. It also tracks usage and records the number of times each data provider has used the service in a log. It also calculates compensation to data providers for specific periods and transfers it to their registered accounts.

[0646] Factory robots

[0647] Factory robots collect data in real time according to the manufacturing process and upload it to the server. If the robot needs data, it sends a data request to the server and optimizes the manufacturing process based on the data provided by the server.

[0648] Processing flow

[0649] 1. Data Collection and Upload

[0650] Data providers upload data through terminals, and factory robots also use sensors to collect data in real time and send it to a server.

[0651] 2. Data Storage and Classification

[0652] The server analyzes the received data, categorizes and tags it appropriately, and stores it in a database using a database management system (e.g., MySQL).

[0653] 3. Data Request and Generation

[0654] When users and factory robots request specific data, they send conditional requests to the server. The server uses generative AI (e.g., natural language processing models) to generate the required data. For example, a prompt might be entered: "Calculate the average temperature of production line A in October 2023."

[0655] 4. Usage Tracking and Reward Calculation

[0656] The server logs the usage of the generated data and tally the number of times each data provider has used it for a specific period (e.g., the end of the month). Based on this, the reward is calculated and transferred to the data provider's registered account. The software used includes log management tools (e.g., the Python logging module) and data analysis tools (e.g., Pandas).

[0657] Specific examples

[0658] Specific examples of data collection and uploading

[0659] The data provider uploads "Production Line A's October temperature data.csv" from their terminal. The factory robot sends "Production Line A's real-time temperature data for October 2023" to the server.

[0660] Examples of data request and generation

[0661] The user requests "the average temperature of production line A in October 2023." The server uses the generative AI and the "generative AI model" to generate data based on the prompt, "Please calculate the average temperature of production line A in October 2023," and sends it to the user's device.

[0662] In this way, the system can achieve effective data management and optimization of the manufacturing process, while also ensuring fair compensation for data providers.

[0663] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0664] Step 1:

[0665] The data provider uploads data using a terminal. On the terminal, the provider selects a data file and also enters detailed data information (e.g., category, date, location, etc.) through a dedicated upload form. When executed, the data and its details are sent to the server. The input data is, for example, "Temperature data.csv for production line A" and detailed information such as "October 2023, production line A, temperature data," and the output is an HTTP request containing the uploaded data file and the detailed information.

[0666] Step 2:

[0667] The server classifies and tags the received data and stores it in a database. First, it saves the transmitted data file in a temporary folder. Next, it analyzes the content of the data and assigns appropriate categories and tags (e.g., "Temperature," "Humidity," "Production Line A," "October 2023," etc.). After that, it uses a database management system (e.g., MySQL) to store the classified and tagged data in a database. The input is the data file and detailed information received by the server, and the output is the classified and tagged data stored in the database.

[0668] Step 3:

[0669] Users and factory robots request specific data. The user terminal or factory robot accesses the system, inputs the conditions for the required data (e.g., specific period, location, data category, etc.), and sends a request. The input is the conditions requested by the user or robot, and the output is the data request sent to the server.

[0670] Step 4:

[0671] The server receives a user or robot request and searches the database. Based on the search results, a generative AI is used to generate the requested data. For example, a prompt statement such as "Please calculate the average temperature of production line A in October 2023" is input into the generative AI model. The generative AI analyzes the relevant data and calculates the average temperature. The inputs are the search conditions and the prompt statement input into the generative AI, and the output is the calculated average temperature data.

[0672] Step 5:

[0673] The server sends the generated data to the user terminal or factory robot. The generation AI formats the generated data and sends it to the user or robot terminal in a form that can be displayed. The input is the data generated by the generation AI, and the output is the data sent to the user or robot terminal.

[0674] Step 6:

[0675] The server logs the usage of the data used. By recording which data provider's information the generative AI model used and the number of times it was used, the system tracks the details of usage for each data provider. The input is the usage information for the generated data, and the output is the usage data recorded in the log file.

[0676] Step 7:

[0677] The server tally the number of uses for each data provider, calculates the reward, and pays it. At specific intervals, the server tally the number of uses and calculates the reward for the data provider based on a predetermined reward unit price. The reward is then transferred to the provider's registered account. The input is the number of uses for each data provider, and the output is the reward calculation result and a transfer completion notification.

[0678] 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.

[0679] This invention relates to a system in which data providers upload data via their devices, and a server receives, classifies, tags, and stores the data in a database. The system also incorporates an emotion engine that recognizes user emotions and reflects them in data generation. When a user sends a data request from their device, the system's generation AI and emotion engine generate data according to the request and send it to the user's device. Furthermore, the server records the usage status of the data in a log, tallying the number of times each data provider uses the data, and calculates and periodically pays rewards.

[0680] Program processing

[0681] 1. Data Collection:

[0682] Terminal: The data provider logs in to the system using a terminal. The data provider accesses a dedicated upload form, selects a data file, enters detailed data information (e.g., category, date, location, etc.), and presses the upload button, which sends the data and detailed information to the server.

[0683] 2. Data storage:

[0684] Server: The server receives the data files and detailed information sent from the device and stores them in a temporary folder. It then analyzes the data files and assigns categories and tags based on their content (e.g., "weather data," "temperature," "humidity," etc.). The categorized and tagged data is then stored in a database.

[0685] 3. Requesting and generating data:

[0686] User: The user accesses the system through a terminal, inputs the conditions for the required data (e.g., a specific period, location, category, etc.), and submits a request.

[0687] Terminal: At the same time, the user's emotional data (e.g., emotional state from text analysis or data from emotion recognition sensors) is acquired by the emotion engine.

[0688] Server: The server receives the user's request and emotion data, and the generation AI searches the database to extract data that matches the conditions.

[0689] Server: The generation AI generates data that is optimal for the user based on the emotional data provided by the emotion engine. For example, if a user requests the "average temperature in Tokyo in March 2023" and the "feeling relaxed" is detected based on the emotional data, the generation AI will present the data in a format that gives a feeling of relaxation.

[0690] Server: Returns the generated data to the user's terminal.

[0691] 4. Usage Tracking:

[0692] Server: Logs details of the data used by the generating AI (such as which data provider's data was used and how many times it was used).

[0693] 5. Calculation and Payment of Rewards:

[0694] Server: The server tally the number of times each data provider uses data for a specific period (e.g., the end of the month) and calculates the reward (e.g., "10 yen per use").

[0695] Server: Transfers the calculated reward to the registered account of the data provider.

[0696] Specific examples

[0697] 1. Data Collection:

[0698] Terminal: The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal and enters detailed information such as "weather data," "March 2023," and "Tokyo" into the form. When the researcher presses the upload button, the data is sent to the server.

[0699] Server: Receives data and detailed information and saves it in a temporary folder. Analyzes it, assigns tags such as "weather data," "temperature," "humidity," "March 2023," and "Tokyo," and stores it in a database.

[0700] 2. Requesting and generating data:

[0701] User: A weather forecaster requests "Average temperature in Tokyo in March 2023" on their device. The emotion engine detects "tension" in the user's text.

[0702] Server: Receives the request and emotion data, and the generation AI searches the database to extract the relevant data.

[0703] Server: The generation AI calculates and presents average temperature data in a format that gives a sense of relaxation suitable for users in a "tense" state.

[0704] Server: Provides the generated results to the user's device. The device displays, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[0705] 3. Usage Tracking:

[0706] Server: Logs which data is used and how much. For example, it records how many times a researcher's weather data is used.

[0707] 4. Calculation and payment of rewards:

[0708] Server: At the end of the collection period, the server counts the 100 times the researcher's data has been used.

[0709] Server: Calculates a reward of 10 yen per 100 uses and transfers 1,000 yen to the researcher.

[0710] In this way, the system and program of the present invention improve the user experience by incorporating emotion recognition, increasing the motivation of data providers while enabling users to quickly and accurately obtain the data they need.

[0711] The processing flow will be explained below.

[0712] Step 1:

[0713] Terminal: The data provider logs into the system using a terminal.

[0714] Terminal: The data provider accesses the dedicated upload form, selects the data file (e.g., "Tokyo weather data for March 2023.csv"), enters the data details (category, date, location), and presses the upload button.

[0715] Step 2:

[0716] Server: The server receives the data file and detailed information sent from the device.

[0717] Server: Save the received data in a temporary folder.

[0718] Step 3:

[0719] Server: Analyzes the stored data files and assigns categories and tags based on their content ("weather data," "temperature," "humidity," "March 2023," "Tokyo").

[0720] Server: Stores the classified and tagged data in a database.

[0721] Step 4:

[0722] User: The user accesses the system through a terminal and navigates to the data request form.

[0723] User: Enters the criteria for the data they need (specific time period, location, category, etc.) and submits a request.

[0724] Step 5:

[0725] Device: The emotion engine collects the user's emotional data. For example, it analyzes the user's emotional state, such as "tension," from the user's text input and data from the facial recognition camera.

[0726] Terminal: The emotion engine sends the user's emotion data to the server.

[0727] Step 6:

[0728] Server: The server receives the user's request and emotion data.

[0729] Server: Searches the database and extracts data that matches the conditions.

[0730] Step 7:

[0731] Server: The generation AI generates data based on the search results. For example, if a user requests "average temperature in Tokyo in March 2023" and the emotional data indicates "tension," the generation AI generates data in a format that gives a sense of relaxation.

[0732] Server: Sends the generated data to the user's terminal.

[0733] Step 8:

[0734] Terminal: The user's terminal receives the generated data and displays it on the screen. For example, it displays "The average temperature in Tokyo in March 2023 will be 15 degrees."

[0735] Step 9:

[0736] Server: Records details of the data used by the generating AI in a log, including which data provider's data was used and the number of times it was used.

[0737] Step 10:

[0738] Server: For each specific period (e.g., at the end of the month), the number of times data is used for each data provider is tallied.

[0739] Server: Calculates the reward based on the aggregated results. For example, 10 yen per use.

[0740] Step 11:

[0741] Server: Transfers the calculated reward to the registered account of the data provider.

[0742] In this way, data providers, users, and servers can cooperate to operate a comprehensive data provision and utilization system, thereby ensuring efficient and fair data provision and an improved user experience.

[0743] Example 2

[0744] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0745] Existing data management systems not only lack the ability to upload, classify, tag, and store data in a database, but also lack the ability to provide optimal data by utilizing user emotion recognition. As a result, it is difficult to provide data that meets user needs, and the user experience cannot be improved. In addition, the process of calculating and paying rewards to data providers is often manual and inefficient.

[0746] 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.

[0747] In this invention, the server includes: means for a data provider to upload data using a terminal; means for analyzing, classifying, and tagging the received data and storing it in a database; means for a user to send a data request from the terminal; means for the terminal to acquire the user's emotional data; means for the server to generate data using a generation AI based on the user's request and emotional data and send it to the user's terminal; means for the server to record in a log how the data has been used; and means for the server to tally the number of times each data provider has used the data and calculate and pay a reward. This makes it possible to provide optimal data according to the user's emotions, improving the user experience and the motivation of data providers at the same time.

[0748] "Data Provider" means any person or entity that uploads and provides data through the System.

[0749] "Terminal" means a computer or mobile device used by a User or Data Contributor to access and operate the System.

[0750] "Server" refers to a central computer system that receives, processes, stores, and distributes data.

[0751] A "database" refers to a structured collection of information that allows a server to classify, tag, and efficiently store and manage data.

[0752] "Generative AI" refers to artificial intelligence that extracts necessary data from a database based on a user request and then generates the data in the optimal format based on the user's emotional data.

[0753] An "emotion engine" refers to software or hardware that has the ability to analyze a user's emotional data and provide that emotional state to a generative AI.

[0754] "Log" refers to a file or database that records system activity, such as data usage.

[0755] "Remuneration" refers to monetary compensation paid according to the number of times the data provided by the data provider is used.

[0756] The "temporary folder" refers to a folder for temporarily storing data received by the server.

[0757] "User" means any person or entity accessing the System and making a request for data.

[0758] The present invention relates to a system in which users and data providers process data mutually through terminals, and a server manages and optimizes this. Specific embodiments are described below.

[0759] Data upload by data contributors

[0760] Terminal

[0761] Data providers log in to the system using a terminal. To log in, they use a username and password. After logging in, they access a dedicated upload form. Using this form, they select a data file (e.g., "weatherdata.csv") and enter detailed data information (e.g., category, date, location, etc.). When they press the upload button, the data file and its details are sent to the server.

[0762] Server processing and storage of data

[0763] server

[0764] The server saves the received data files and detailed information in a temporary folder. The server then analyzes the data files and assigns categories and tags based on their content (e.g., "Weather Data," "Temperature," "Humidity," "March 2023," "Tokyo," etc.). Python libraries such as pandas and NumPy can be used for analysis. The classified and tagged data is stored in a database using an RDBMS such as MySQL or PostgreSQL.

[0765] User data requests and emotion data acquisition

[0766] Terminal

[0767] The user accesses the system from their device, inputs the conditions for the required data (e.g., period, location, category, etc.), and sends a request. At the same time, the user's emotional data is acquired. To acquire the emotional data, a text analysis library (e.g., TextBlob) or an emotion recognition sensor is used.

[0768] Server-generated data

[0769] server

[0770] The server receives the user's request and emotion data. The generation AI then searches the database based on the request conditions and extracts matching data. The generation AI can use OpenAI's GPT model, for example. Furthermore, it generates data in a format that is optimal for the user based on the emotion data provided by the emotion engine. For example, if a user requests "average temperature in Tokyo in March 2023" and "relaxed feeling" is detected based on the emotion data, the generation AI will present the data in a format that gives a sense of relaxation.

[0771] Returning data

[0772] server

[0773] The server sends the generated data back to the user's device, which displays, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[0774] Usage tracking and reward calculation

[0775] server

[0776] The server logs details of the data used by the generating AI (such as which data provider's data was used and the number of times it was used). In addition, the server aggregates the number of times each data provider used it for a specific period (e.g., the end of the month) and calculates the reward (e.g., "10 yen per use"). The calculated reward is transferred to the data provider's registered account.

[0777] Specific examples

[0778] 1. Data provider operations

[0779] The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal, enters detailed information such as "weather data," "March 2023," and "Tokyo" into the form, and presses the upload button.

[0780] The server receives the data and detailed information, saves it in a temporary folder, analyzes it, assigns tags such as "weather data," "temperature," "humidity," "March 2023," and "Tokyo," and stores it in a database.

[0781] 2. User Operation

[0782] A weather forecaster requests "average temperature in Tokyo in March 2023." The emotion engine detects "tension" from the user's text.

[0783] The server receives the request and emotion data, and the generation AI searches the database to extract the relevant data.

[0784] The generating AI presents average temperature data in a format that gives a sense of relaxation suitable for users in a state of "tension."

[0785] The server provides the generated results to the user's device, which displays the message, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[0786] This will enable the realization of a system that improves the user experience by incorporating emotion recognition, increases the motivation of data providers, and enables users to quickly and accurately obtain the data they need.

[0787] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0788] Step 1:

[0789] Data provider login

[0790] User: The data provider logs into the system using a terminal. As input, they use their username and password. As output, they are granted access to the system and a dedicated upload form is displayed.

[0791] Step 2:

[0792] Uploading data

[0793] User: The data provider selects a data file in the upload form and enters detailed information (category, date, location, etc.). The input is the data file (e.g., "weatherdata.csv") and the detailed information. The output is the data file and the detailed information sent from the terminal to the server.

[0794] Step 3:

[0795] Receiving and temporarily storing data

[0796] Server: Receives data files and detailed information and saves them in a temporary folder. As input, it receives data files and detailed information sent from the device. As output, it saves the data files in a temporary folder.

[0797] Step 4:

[0798] Data analysis and tagging

[0799] Server: Analyzes data files saved in a temporary folder. The input is the saved data file. Specifically, the data file is analyzed using Python libraries such as pandas and NumPy. As output, categories and tags (e.g., "weather data," "temperature," "humidity," "March 2023," "Tokyo," etc.) are assigned based on the content of the data.

[0800] Step 5:

[0801] Storage in the database

[0802] Server: Stores the classified and tagged data in an RDBMS such as MySQL or PostgreSQL. The input is a tagged data file. The specific operation is to store the data in the database using SQL queries. The output is structured data stored in the database.

[0803] Step 6:

[0804] Sending a Data Request

[0805] User: The user inputs the conditions for the required data and submits a request. As input, there are the conditions for the required data (e.g., period, location, category, etc.). As output, the request is sent to the server. At the same time, emotion data is obtained.

[0806] Step 7:

[0807] Acquiring emotion data

[0808] Device: Obtain user emotional data using a text analysis library (e.g., TextBlob) or an emotion recognition sensor. Inputs include user text information and sensor data. Outputs include analyzed emotional data.

[0809] Step 8:

[0810] Receiving requests and emotion data

[0811] Server: Receives user request data and emotion data. As input, there is the request data and emotion data sent by the user. As output, the received data is passed to the generation AI.

[0812] Step 9:

[0813] Data retrieval and generation

[0814] Server: The generation AI searches the database based on the request conditions and emotion data, and extracts matching data. The inputs are the request conditions and emotion data. The specific operation is to search the database using the generation AI (e.g., OpenAI's GPT model). The output is the extraction of matching data.

[0815] Step 10:

[0816] Optimal Data Generation

[0817] Server: The generation AI generates the data in the optimal data format for the user based on the emotion data provided by the emotion engine. The inputs are emotion data and extracted data. Specifically, the generation AI creates a data format that matches the emotion. The output is optimized data.

[0818] Step 11:

[0819] Returning data

[0820] Server: Returns the generated data to the user's device. The input is the optimized data. The output is the data displayed on the user's device. The user's device displays, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[0821] Step 12:

[0822] Usage record

[0823] Server: Records details of the data used by the generating AI in a log. The input is the data used and its details. Specific operations include recording operations to log files and databases. The output is a record of usage.

[0824] Step 13:

[0825] Reward calculation and payment

[0826] Server: Aggregates the number of uses by each data provider for a specific period and calculates rewards. The input is usage data from the log. Specific operations include aggregation and reward calculation. The output is the calculated reward being deposited into the data provider's registered account.

[0827] (Application example 2)

[0828] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0829] Conventional data provision systems have the problem that the methods for utilizing data uploaded by data providers and the management of its usage status are complicated, and the data provided to users is simply in response to requests, making it difficult to provide optimal information according to the user's emotional state.In particular, autonomous vehicles are required to properly recognize the driver's emotions and provide optimal navigation and assistance based on those emotions, but conventional systems are not able to adequately address this issue either.

[0830] 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: a means for a data provider to upload data using a terminal; a means for the server to classify and tag the received data and store it in a database; a means for a user to send a data request from a terminal; a means for the server to generate data using a generation AI based on the user's request and send it to the user's terminal; a means for the server to log the usage status of the data used by the server; a means for the server to tally the number of uses for each data provider and calculate and pay a reward; a means for the server to analyze the driver's emotional data and apply it to the navigation system; and a means for the navigation system to generate an optimal route based on the emotional data and present it to the driver. This makes it easier for data providers to understand how their data is being used, and enables users to receive optimal data and navigation according to their emotional state.

[0831] "Data Provider" means an individual or entity that generates and uploads data to the system.

[0832] "Terminal" refers to a device used by data providers and users to upload data, send requests, etc.

[0833] A "server" is a computer that is the core of the system and is a device that performs various processes such as receiving, classifying, storing, generating, and logging data.

[0834] "Generative AI" is artificial intelligence that generates data based on user requests and sends it to the user's device.

[0835] A "database" is a storage device where the server stores classified and tagged data.

[0836] "Emotion data" is information indicating the emotional state of the driver analyzed based on data such as facial expressions, voice, and heart rate.

[0837] A "navigation system" is a system that generates an optimal route based on the driver's emotional data and presents it to the driver.

[0838] A "request" is a request made by a user to the system through a terminal for specific data or navigation information.

[0839] "Usage status" is information indicating the usage history and frequency of data generated by the server using the generated AI.

[0840] "Remuneration" means monetary compensation received by a data provider for the use of their data.

[0841] A "log" is data that records a series of operations and data usage status performed on a server.

[0842] A "prompt" is text data containing instructions or questions that are input into a generative AI model.

[0843] A "temporary folder" is an area where the server temporarily stores data received.

[0844] This invention provides an emotion-responsive navigation system for autonomous vehicles. This system provides optimal navigation according to the emotional state of the driver of the autonomous vehicle, realizing a comfortable driving environment.

[0845] First, the system uses the following hardware and software:

[0846] Hardware:

[0847] Internal camera (for driver facial expression recognition)

[0848] Various sensors (heart rate sensor, voice recognition microphone, etc.)

[0849] In-vehicle display

[0850] server

[0851] software:

[0852] Emotion recognition engine: Emotional AI SDK

[0853] Generative AI model: OpenAI GPT-4

[0854] Cloud database: AWS RDS

[0855] Navigation data: Google Maps API

[0856] When the driver gets into the vehicle, the internal camera and sensors collect emotional data such as facial expressions, voice, and heart rate in real time, which are then transmitted to the server via the terminal.

[0857] The server analyzes the received emotion data and identifies the driver's current emotional state using the Emotional AI SDK. Identified emotional states include "stress," "fatigue," and "relaxation."

[0858] The driver requests a destination and navigation conditions through the in-car display or voice assistant, and emotion data is sent to the server at the same time.

[0859] The server generates optimal navigation data based on the received request and emotion data using a generative AI model, OpenAI GPT-4, and Google Maps API to obtain the necessary navigation data.

[0860] For example, if a driver is in a "stressed" state, the generative AI model will select a scenic route that avoids crowds and encourages relaxation. If a driver is feeling "fatigued," the model will recommend a route with rest areas and toilets.

[0861] The generated navigation data is displayed on the driver's in-vehicle display, allowing the driver to receive navigation information optimized for their emotional state.

[0862] For example, a sample prompt for a generative AI model might look like this:

[0863] "My emotional state is 'stressed'. I will be driving from Tokyo to Yokohama at 1:00 PM on October 20, 2023. Please advise me of a relaxing route that avoids crowds."

[0864] This system allows drivers to relax even in stressful driving situations, resulting in an overall more comfortable driving experience.The invention also takes into consideration the interests of data providers, automatically calculating and paying compensation for data used based on how the data is used.

[0865] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0866] Step 1:

[0867] The server collects the driver's emotional data in real time from the internal camera and various sensors. Specifically, the camera recognizes facial expressions, and the sensors acquire heart rate and voice data. Based on this input data, the Emotional AI SDK is used to analyze the driver's emotional state and output it as emotional data.

[0868] Step 2:

[0869] The device receives a navigation request from the driver. The driver inputs the destination and navigation conditions through the display or voice assistant. For example, a condition such as "I want to avoid congestion" is specified. The request data is sent to the server.

[0870] Step 3:

[0871] The server receives the emotion data obtained in step 1 and the request data obtained in step 2. Based on this data, it processes the input using a generative AI model (OpenAI GPT-4) and generates appropriate navigation data. This generation process takes into account relaxing routes and routes that avoid congestion.

[0872] Step 4:

[0873] The server then connects the generated navigation data to the Google Maps API to obtain more detailed route information. This completes the final navigation data, which is then ready to be provided to the driver. The output data is optimized route information.

[0874] Step 5:

[0875] The generated navigation data is sent to the driver's device, and route information is displayed on the in-vehicle display for the driver to review. In addition, the voice assistant provides navigation guidance, improving driving safety and comfort.

[0876] Step 6:

[0877] The server records the usage of navigation data in a log. This logs information such as how much data from which data provider was used and details of the data used by the generating AI. This accumulates basic data for subsequent reward calculations.

[0878] Step 7:

[0879] The server tally up the number of times data providers have used the service for a specific period, such as the end of the month. It calculates how much data from each data provider has been used and calculates rewards based on that. The calculated reward data is then transferred to the data provider's registered account.

[0880] 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.

[0881] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0882] 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.

[0883] [Third embodiment]

[0884] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0885] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0886] 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).

[0887] 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.

[0888] 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.

[0889] 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).

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0896] This invention relates to a system in which data providers upload data via their devices, and a server receives, classifies, tags, and stores the data in a database. In this system, when a user sends a data request from their device, the server uses a generation AI to generate data according to the request and sends it to the user's device. Furthermore, the server records the usage status of the data in a log, tallying the number of times each data provider uses it, calculates rewards, and periodically pays them.

[0897] Program processing

[0898] 1. Data Collection:

[0899] Terminal: The data provider uses the terminal to access a dedicated upload form. The data provider selects the data file they own, enters detailed information about the data (e.g., data category, date, location, etc.), and presses the upload button to send the data to the server.

[0900] Server: The server receives the data file and its detailed information sent from the terminal.

[0901] 2. Data storage:

[0902] Server: The server saves the received data files in a temporary folder.

[0903] Server: Analyzes the contents of data files and classifies them by category and tag. Appropriate tags (e.g., "weather data," "temperature," "humidity," etc.) are assigned to each data.

[0904] Server: Stores the classified and tagged data in a database.

[0905] 3. Requesting and generating data:

[0906] User: The user accesses the system through a terminal, inputs the conditions for the data they require (e.g., a specific period, location, data category, etc.), and submits a request.

[0907] Server: The server receives the user's request, searches the database, and extracts data that matches the criteria.

[0908] Server: The AI ​​generates the requested data based on the search results. For example, if a user requests "average temperature in Tokyo in March 2023," the AI ​​aggregates the temperature data for the relevant period and calculates the average temperature.

[0909] Server: Returns the generated data to the user's terminal.

[0910] Terminal: The user's terminal displays the generated data.

[0911] 4. Usage Tracking:

[0912] Server: The server logs which data providers' data the generating AI used and how much of it was used, thereby accurately understanding the data usage status of each data provider.

[0913] 5. Calculation and Payment of Rewards:

[0914] Server: The server aggregates the amount of use of data from each data provider for each specific period (for example, at the end of the month).

[0915] Server: Based on the aggregated results, calculate the reward for each data provider. For example, calculate the amount based on the total number of uses, assuming "10 yen per use."

[0916] Server: Transfers the calculated reward to the registered account of the data provider.

[0917] Specific examples

[0918] 1. Data Collection:

[0919] Terminal: The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal. Detailed information such as "weather data," "March 2023," and "Tokyo" is entered into the form.

[0920] Server: Receives the data and details and stores them in a temporary folder.

[0921] 2. Data storage:

[0922] Server: Analyzes the received data and tags it with information such as "weather data," "temperature," "humidity," "March 2023," and "Tokyo."

[0923] Server: Stores the tagged data in a database.

[0924] 3. Requesting and generating data:

[0925] User: A weather forecaster requests "average temperature in Tokyo in March 2023" on their device.

[0926] Server: Receives the request and searches the database. The relevant data is extracted and the generating AI calculates the average temperature.

[0927] Server: Provides the calculation results to the user's terminal.

[0928] Terminal: Display "The average temperature in Tokyo in March 2023 will be 15 degrees" on the user terminal.

[0929] 4. Usage Tracking:

[0930] Server: Logs which data is used and how much. For example, it records how many times a researcher's weather data is used.

[0931] 5. Calculation and Payment of Rewards:

[0932] Server: At the end of the collection period, the server counts the 100 times the researcher's data has been used.

[0933] Server: Calculates a reward of 10 yen per 100 uses and transfers 1,000 yen to the researcher.

[0934] In this way, the system and program of the present invention enable users to quickly and accurately obtain the data they need while increasing the motivation of data providers.

[0935] The processing flow will be explained below.

[0936] Step 1:

[0937] Terminal: The data provider logs into the system using a terminal.

[0938] Terminal: The data provider accesses the upload form, selects the data file, and enters data details (category, date, location, etc.).

[0939] Terminal: When the data provider presses the upload button, the terminal sends the data file and detailed information to the server.

[0940] Step 2:

[0941] Server: The server receives the data file and detailed information sent from the device.

[0942] Server: Save the received data in a temporary folder.

[0943] Step 3:

[0944] Server: Analyzes the stored data files and assigns categories and tags based on the data content. For example, tags such as "weather data," "temperature," and "humidity" are assigned.

[0945] Server: Stores the classified and tagged data in a database.

[0946] Step 4:

[0947] User: The user accesses the system through a terminal and navigates to the data request form.

[0948] User: Enters the criteria for the data they need (e.g., a specific time period, location, data category, etc.) and submits a request.

[0949] Step 5:

[0950] Server: The server receives the user's request and searches the database to extract data that matches the criteria.

[0951] Step 6:

[0952] Server: The generation AI generates the requested data based on the search results. For example, if a request is made for "average temperature in Tokyo in March 2023," the generation AI aggregates the relevant data and calculates the average temperature.

[0953] Server: Returns the generated data to the user's terminal.

[0954] Step 7:

[0955] Terminal: The user's terminal receives the generated data and displays it on the screen.

[0956] Step 8:

[0957] Server: Records details of the data used by the generating AI in a log, including which data provider's data was used and the number of times it was used.

[0958] Step 9:

[0959] Server: For each specific period (e.g., at the end of the month), the number of times data is used for each data provider is tallied.

[0960] Server: Calculates the reward based on the aggregated results. For example, 10 yen per use.

[0961] Step 10:

[0962] Server: Transfers the calculated reward to the registered account of the data provider.

[0963] In this way, data providers, users, and servers cooperate to operate the system, ensuring efficient and fair provision and use of data.

[0964] Example 1

[0965] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0966] In recent years, data collection, management, and provision have become important issues in many industries, but there is a lack of a system that can efficiently manage data uploaded by various data providers, quickly provide necessary data to users, and distribute appropriate rewards to data providers.In addition, there is a challenge in accurately understanding the usage status of the data uploaded by data providers and paying rewards in a transparent manner.

[0967] 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.

[0968] In this invention, the server includes: a means for a data provider to upload data using a terminal; a means for the server to classify and tag the received data and store it in an information storage device; a means for a user to send a data request from the terminal; a means for the server to generate data using a generative AI model based on the user request and send it to the user terminal; a means for the server to record the usage status of the data used by the server on a recording medium; and a means for the server to tally the number of times each data provider uses the data and calculate and pay rewards. This enables efficient management of data uploaded by data providers, fast and accurate provision of data to users, and appropriate distribution of rewards to data providers.

[0969] A "data provider" is a person or organization that uses a terminal to upload data to the system.

[0970] "Terminal" means a computing device used by a user or data provider to access the system and to send and receive data.

[0971] A "server" is a central computing system that receives, stores, analyzes, and generates data.

[0972] An "information storage device" is a database or storage system that stores data persistently after it has been classified and tagged.

[0973] A "generative AI model" is an artificial intelligence model used to generate necessary data based on a user request.

[0974] A "recording medium" is a log system or database that records the usage of data used by a generative AI model.

[0975] "Remuneration" is the consideration paid to the data provider based on the number of times the data is used.

[0976] "Use count" refers to the number of times a particular piece of data is requested and used by a user.

[0977] A "data request" is a request for data of specific conditions that a user requests from the system via a terminal.

[0978] MODE FOR CARRYING OUT THE INVENTION

[0979] The present invention relates to a system in which data providers upload data via their terminals, and a server receives, classifies, tags, and stores the data in an information storage device. In this system, when a user sends a data request from their terminal, the server generates data according to the request using a generative AI model and sends it to the user's terminal. Furthermore, the server records the usage status of the data used on a recording medium, tallying the number of times each data provider has used it, calculates rewards, and periodically pays them.

[0980] Data collection

[0981] Terminal: The data provider accesses the dedicated web application and clicks the "Upload Data" button. They select the file to upload from their PC (e.g., "Tokyo weather data for March 2023.csv"), enter detailed information such as the data category (e.g., weather data), date (e.g., March 2023), and location (e.g., Tokyo) in the form, and click the upload button to send the data to the server.

[0982] Server: Receives uploaded data files and their details and safely stores them in a temporary folder. The HTTP protocol is used for receiving, and uploaded files are temporarily stored on the server's disk.

[0983] Data storage

[0984] Server: Analyze the data files saved in the temporary folder using an automatic analysis program (e.g., Python's pandas library). During analysis, the data items and formats are checked and the contents are structured.

[0985] Server: Based on the analyzed data, it assigns categories (e.g., weather data) and tags (e.g., temperature, humidity, location, date, etc.). For tagging, it may use a predefined schema file.

[0986] Server: Stores tagged data in an information storage device such as MongoDB, allowing for quick search when retrieving data.

[0987] Requesting and Generating Data

[0988] User: The user accesses a data request form through the web application, where they enter specific criteria (e.g., average temperature in Tokyo in March 2023) and press the submit button.

[0989] Server: Receives user requests and queries the database to find data that matches the criteria. This is done using MongoDB's query functionality.

[0990] Server: Based on the search results, a generative AI model (e.g., TensorFlow or GPT-3) is used to generate the requested data. For example, if a user requests "average temperature in Tokyo in March 2023," the generative AI model aggregates temperature data for the relevant period and calculates the average temperature.

[0991] Server: Converts the generated data into JSON format and returns it to the user's device.

[0992] Device: The user's device analyzes the returned data and displays a message on a web page, such as "The average temperature in Tokyo in March 2023 will be 15 degrees."

[0993] Usage Tracking

[0994] Server: Records detailed logs of which data providers' data the generative AI model used and to which users it provided it to, for example, by creating log entries containing information such as data provider ID, data item, and number of uses.

[0995] Server: This information is stored on a recording medium using a log management system such as MySQL or Elasticsearch.

[0996] Reward calculation and payment

[0997] Server: Periodically (e.g., at the end of each month), aggregate how much data from each data provider has been used. Scan the log data and record the number of times each data provider has been used.

[0998] Server: Calculates rewards based on the number of uses. For example, it is calculated as "10 yen per use."

[0999] Server: Based on the statistical results, the reward is transferred to the data provider's registered bank account. This can be done using a financial API (e.g., Stripe or PayPal).

[1000] Examples of concrete examples and prompts

[1001] 1. Data collection

[1002] Terminal: The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal. He enters "weather data," "March 2023," and "Tokyo" into the form.

[1003] Server: Receives this data and details and stores them in a temporary folder.

[1004] 2. Data Storage

[1005] Server: Analyzes the stored data using the pandas library, adds categories and tags, and stores them in a MongoDB database.

[1006] 3. Requesting and Generating Data

[1007] User: A weather forecaster requests "Average temperature in Tokyo in March 2023."

[1008] Server: Performs queries and calculates average temperatures using a generative AI model.

[1009] Server: Returns the result in JSON and displays "The average temperature in Tokyo in March 2023 will be 15 degrees" on the user's device.

[1010] 4. Usage Tracking

[1011] Server: Logs data usage. Example: A researcher's weather data is used 100 times.

[1012] 5. Calculation and Payment of Rewards

[1013] Server: At the end of the collection period, calculate the number of uses and the total reward. For example, if there are 100 uses, the total reward will be calculated as 10 yen per use, for a total of 1,000 yen.

[1014] Server: Transfers rewards to the data provider's bank account using Stripe API.

[1015] Prompt Sentence Examples

[1016] "What is the average temperature in Tokyo in March 2023?"

[1017] "Please provide a list of temperature and humidity data for Tokyo in March 2023."

[1018] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1019] Step 1: Collect data

[1020] Terminal: The data provider accesses a dedicated web application. They log in with their user ID and password and click the "Upload Data" button. They select the file to upload from their PC (e.g., "Tokyo weather data for March 2023.csv"), enter detailed information in the form, such as the data category (e.g., weather data), date (e.g., March 2023), and location (e.g., Tokyo), and then press the submit button. Input: Data file and detailed information. Output: Upload request.

[1021] Server: Receives upload requests and saves data files and details in a temporary folder. Input: Upload request. Output: File saved in the temporary folder.

[1022] Step 2: Store the data

[1023] Server: Reads data files saved in a temporary folder and analyzes them using an automatic analysis program (e.g., Python's pandas library). During analysis, the data items and format are checked and the contents are structured. Input: Data files read from the temporary folder. Output: Structured data.

[1024] Server: Based on the structured data, assigns categories (e.g., weather data) and tags (e.g., temperature, humidity, location, date, etc.). A predefined schema is used for tagging. Input: Structured data. Output: Tagged data.

[1025] Server: Stores tagged data in an information storage device such as MongoDB. Input: Tagged data. Output: Data stored in the database.

[1026] Step 3: Request the data

[1027] User: The user accesses the data request form through the web application, enters specific criteria (e.g., average temperature in Tokyo in March 2023), and presses the submit button. Input: Data request. Output: Request submitted.

[1028] Server: Receives data requests and queries the database to find data that matches the criteria. Input: Data request. Output: Search results.

[1029] Step 4: Generate data

[1030] Server: Based on the search results, a generative AI model (e.g., TensorFlow or GPT-3) is used to generate the requested data. For example, if a user requests "average temperature in Tokyo in March 2023," the generative AI model aggregates temperature data for the relevant period and calculates the average temperature. Input: Search results. Output: Generated data.

[1031] Server: Converts the generated data into JSON format and returns it to the user's device. Input: Generated data. Output: Returned data.

[1032] Step 5: View the generated data

[1033] Terminal: The user's terminal receives the returned data, analyzes it, and displays it. For example, it displays "The average temperature in Tokyo in March 2023 is 15 degrees" on a web page. Input: The returned data. Output: The displayed data.

[1034] Step 6: Track usage

[1035] Server: Records detailed logs of which data providers' data the generative AI model used and to which users it provided it. For example, it creates log entries containing information such as data provider ID, data item, and number of uses. Input: Data usage information. Output: Recorded log.

[1036] Server: This information is stored on a recording medium using a log management system such as MySQL or Elasticsearch. Input: Recorded logs. Output: Stored logs.

[1037] Step 7: Calculating and paying rewards

[1038] Server: Periodically (e.g., at the end of each month), aggregate how much data from each data provider has been used. Scan the log data and count the number of uses for each data provider. Input: Stored logs. Output: Aggregated results of the number of uses.

[1039] Server: Calculates rewards based on the number of uses. For example, it is calculated as "10 yen per use." Input: Aggregated results of number of uses. Output: Reward calculation results.

[1040] Server: Based on the statistical results, the reward is transferred to the data provider's registered bank account. This is done using a financial API (e.g., Stripe or PayPal). Input: Reward calculation results. Output: Transfer completion notification.

[1041] (Application example 1)

[1042] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1043] In the manufacturing field, while the real-time collection and use of various data is important, data collection, management, and optimization are complex and there is a lack of efficient systems. Furthermore, data providers often do not receive fair compensation, which reduces their motivation to provide data. In such an environment, optimizing the manufacturing process is difficult, and as a result, production efficiency is likely to decline.

[1044] 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.

[1045] In this invention, the server

[1046] A means for a data provider to upload data using a terminal;

[1047] means for classifying and tagging the data received by the server and storing it in a database;

[1048] A means for a user to send a data request from a terminal;

[1049] A means for the server to generate data using a generation AI based on a user request and transmit the data to the user terminal;

[1050] a means for logging usage of the data used by the server;

[1051] The server counts the number of uses for each data provider, calculates the reward, and pays it;

[1052] A means for the robot to collect data in real time according to the manufacturing process and upload it to a server;

[1053] a means for the robot to request data required for a particular manufacturing process from the server;

[1054] Includes:

[1055] This will enable efficient data collection, management, and optimization at manufacturing sites, and will also increase motivation for data provision by ensuring appropriate compensation for data providers. Furthermore, the use of generative AI will enable the rapid and accurate generation of requested data, facilitating optimization of manufacturing processes.

[1056] A "data provider" is an entity that generates valuable data related to manufacturing processes or other systems and provides it to the system.

[1057] "Terminal" means an electronic device used by Data Contributors and Users to access the System and to upload or request Data.

[1058] "Server" means a computer system that receives, categorizes, tags, stores data in a database, generates data based on requests, records logs, calculates rewards, and pays out.

[1059] A "user" is an entity that utilizes the system to request specific data and receive the results.

[1060] "Generative AI" is artificial intelligence used to generate necessary data based on user requests.

[1061] A "data request" is an act in which a user requests data required based on specific conditions from the system.

[1062] A "database" is an information system for systematically storing categorized and tagged data.

[1063] A "log" is a digital record generated by a server to record data usage.

[1064] The "number of times data is used" is an index that indicates the frequency with which a user accesses and uses specific data.

[1065] "Remuneration" means monetary or other consideration paid to a Data Provider based on the use of the Data provided by that Data Provider.

[1066] "Robots" are automated machines that collect and request data during the manufacturing process and work in conjunction with a server to optimize operations.

[1067] "Manufacturing process" refers to the series of steps used to produce a product from raw materials.

[1068] The present invention provides a system for collecting data in a factory and optimizing the data. Specific embodiments of the present invention will be described below.

[1069] System Configuration

[1070] This system consists of a data provider terminal, a user terminal, a server, and a factory robot.

[1071] Data provider terminal

[1072] Data providers upload data using terminals. Terminals include devices that can input and send data, such as PCs, smartphones, and tablets. The provided data includes valuable information related to the manufacturing process (e.g., sensor data, work history, quality check data, etc.).

[1073] server

[1074] The server analyzes the received data and categorizes and tags it. When storing it in a database, it organizes it by assigning specific categories and tags (e.g., "weather data," "temperature," "humidity," etc.). It also receives data requests from users and generates appropriate data using generation AI. It also tracks usage and records the number of times each data provider has used the service in a log. It also calculates compensation to data providers for specific periods and transfers it to their registered accounts.

[1075] Factory robots

[1076] Factory robots collect data in real time according to the manufacturing process and upload it to the server. If the robot needs data, it sends a data request to the server and optimizes the manufacturing process based on the data provided by the server.

[1077] Processing flow

[1078] 1. Data Collection and Upload

[1079] Data providers upload data through terminals, and factory robots also use sensors to collect data in real time and send it to a server.

[1080] 2. Data Storage and Classification

[1081] The server analyzes the received data, categorizes and tags it appropriately, and stores it in a database using a database management system (e.g., MySQL).

[1082] 3. Data Request and Generation

[1083] When users and factory robots request specific data, they send conditional requests to the server. The server uses generative AI (e.g., natural language processing models) to generate the required data. For example, a prompt might be entered: "Calculate the average temperature of production line A in October 2023."

[1084] 4. Usage Tracking and Reward Calculation

[1085] The server logs the usage of the generated data and tally the number of times each data provider has used it for a specific period (e.g., the end of the month). Based on this, the reward is calculated and transferred to the data provider's registered account. The software used includes log management tools (e.g., the Python logging module) and data analysis tools (e.g., Pandas).

[1086] Specific examples

[1087] Specific examples of data collection and uploading

[1088] The data provider uploads "Production Line A's October temperature data.csv" from their terminal. The factory robot sends "Production Line A's real-time temperature data for October 2023" to the server.

[1089] Examples of data request and generation

[1090] The user requests "the average temperature of production line A in October 2023." The server uses the generative AI and the "generative AI model" to generate data based on the prompt, "Please calculate the average temperature of production line A in October 2023," and sends it to the user's device.

[1091] In this way, the system can achieve effective data management and optimization of the manufacturing process, while also ensuring fair compensation for data providers.

[1092] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1093] Step 1:

[1094] The data provider uploads data using a terminal. On the terminal, the provider selects a data file and also enters detailed data information (e.g., category, date, location, etc.) through a dedicated upload form. When executed, the data and its details are sent to the server. The input data is, for example, "Temperature data.csv for production line A" and detailed information such as "October 2023, production line A, temperature data," and the output is an HTTP request containing the uploaded data file and the detailed information.

[1095] Step 2:

[1096] The server classifies and tags the received data and stores it in a database. First, it saves the transmitted data file in a temporary folder. Next, it analyzes the content of the data and assigns appropriate categories and tags (e.g., "Temperature," "Humidity," "Production Line A," "October 2023," etc.). After that, it uses a database management system (e.g., MySQL) to store the classified and tagged data in a database. The input is the data file and detailed information received by the server, and the output is the classified and tagged data stored in the database.

[1097] Step 3:

[1098] Users and factory robots request specific data. The user terminal or factory robot accesses the system, inputs the conditions for the required data (e.g., specific period, location, data category, etc.), and sends a request. The input is the conditions requested by the user or robot, and the output is the data request sent to the server.

[1099] Step 4:

[1100] The server receives a user or robot request and searches the database. Based on the search results, a generative AI is used to generate the requested data. For example, a prompt statement such as "Please calculate the average temperature of production line A in October 2023" is input into the generative AI model. The generative AI analyzes the relevant data and calculates the average temperature. The inputs are the search conditions and the prompt statement input into the generative AI, and the output is the calculated average temperature data.

[1101] Step 5:

[1102] The server sends the generated data to the user terminal or factory robot. The generation AI formats the generated data and sends it to the user or robot terminal in a form that can be displayed. The input is the data generated by the generation AI, and the output is the data sent to the user or robot terminal.

[1103] Step 6:

[1104] The server logs the usage of the data used. By recording which data provider's information the generative AI model used and the number of times it was used, the system tracks the details of usage for each data provider. The input is the usage information for the generated data, and the output is the usage data recorded in the log file.

[1105] Step 7:

[1106] The server tally the number of uses for each data provider, calculates the reward, and pays it. At specific intervals, the server tally the number of uses and calculates the reward for the data provider based on a predetermined reward unit price. The reward is then transferred to the provider's registered account. The input is the number of uses for each data provider, and the output is the reward calculation result and a transfer completion notification.

[1107] 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.

[1108] This invention relates to a system in which data providers upload data via their devices, and a server receives, classifies, tags, and stores the data in a database. The system also incorporates an emotion engine that recognizes user emotions and reflects them in data generation. When a user sends a data request from their device, the system's generation AI and emotion engine generate data according to the request and send it to the user's device. Furthermore, the server records the usage status of the data in a log, tallying the number of times each data provider uses the data, and calculates and periodically pays rewards.

[1109] Program processing

[1110] 1. Data Collection:

[1111] Terminal: The data provider logs in to the system using a terminal. The data provider accesses a dedicated upload form, selects a data file, enters detailed data information (e.g., category, date, location, etc.), and presses the upload button, which sends the data and detailed information to the server.

[1112] 2. Data storage:

[1113] Server: The server receives the data files and detailed information sent from the device and stores them in a temporary folder. It then analyzes the data files and assigns categories and tags based on their content (e.g., "weather data," "temperature," "humidity," etc.). The categorized and tagged data is then stored in a database.

[1114] 3. Requesting and generating data:

[1115] User: The user accesses the system through a terminal, inputs the conditions for the required data (e.g., a specific period, location, category, etc.), and submits a request.

[1116] Terminal: At the same time, the user's emotional data (e.g., emotional state from text analysis or data from emotion recognition sensors) is acquired by the emotion engine.

[1117] Server: The server receives the user's request and emotion data, and the generation AI searches the database to extract data that matches the conditions.

[1118] Server: The generation AI generates data that is optimal for the user based on the emotional data provided by the emotion engine. For example, if a user requests the "average temperature in Tokyo in March 2023" and the "feeling relaxed" is detected based on the emotional data, the generation AI will present the data in a format that gives a feeling of relaxation.

[1119] Server: Returns the generated data to the user's terminal.

[1120] 4. Usage Tracking:

[1121] Server: Logs details of the data used by the generating AI (such as which data provider's data was used and how many times it was used).

[1122] 5. Calculation and Payment of Rewards:

[1123] Server: The server tally the number of times each data provider uses data for a specific period (e.g., the end of the month) and calculates the reward (e.g., "10 yen per use").

[1124] Server: Transfers the calculated reward to the registered account of the data provider.

[1125] Specific examples

[1126] 1. Data Collection:

[1127] Terminal: The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal and enters detailed information such as "weather data," "March 2023," and "Tokyo" into the form. When the researcher presses the upload button, the data is sent to the server.

[1128] Server: Receives data and detailed information and saves it in a temporary folder. Analyzes it, assigns tags such as "weather data," "temperature," "humidity," "March 2023," and "Tokyo," and stores it in a database.

[1129] 2. Requesting and generating data:

[1130] User: A weather forecaster requests "Average temperature in Tokyo in March 2023" on their device. The emotion engine detects "tension" in the user's text.

[1131] Server: Receives the request and emotion data, and the generation AI searches the database to extract the relevant data.

[1132] Server: The generation AI calculates and presents average temperature data in a format that gives a sense of relaxation suitable for users in a "tense" state.

[1133] Server: Provides the generated results to the user's device. The device displays, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[1134] 3. Usage Tracking:

[1135] Server: Logs which data is used and how much. For example, it records how many times a researcher's weather data is used.

[1136] 4. Calculation and payment of rewards:

[1137] Server: At the end of the collection period, the server counts the 100 times the researcher's data has been used.

[1138] Server: Calculates a reward of 10 yen per 100 uses and transfers 1,000 yen to the researcher.

[1139] In this way, the system and program of the present invention improve the user experience by incorporating emotion recognition, increasing the motivation of data providers while enabling users to quickly and accurately obtain the data they need.

[1140] The processing flow will be explained below.

[1141] Step 1:

[1142] Terminal: The data provider logs into the system using a terminal.

[1143] Terminal: The data provider accesses the dedicated upload form, selects the data file (e.g., "Tokyo weather data for March 2023.csv"), enters the data details (category, date, location), and presses the upload button.

[1144] Step 2:

[1145] Server: The server receives the data file and detailed information sent from the device.

[1146] Server: Save the received data in a temporary folder.

[1147] Step 3:

[1148] Server: Analyzes the stored data files and assigns categories and tags based on their content ("weather data," "temperature," "humidity," "March 2023," "Tokyo").

[1149] Server: Stores the classified and tagged data in a database.

[1150] Step 4:

[1151] User: The user accesses the system through a terminal and navigates to the data request form.

[1152] User: Enters the criteria for the data they need (specific time period, location, category, etc.) and submits a request.

[1153] Step 5:

[1154] Device: The emotion engine collects the user's emotional data. For example, it analyzes the user's emotional state, such as "tension," from the user's text input and data from the facial recognition camera.

[1155] Terminal: The emotion engine sends the user's emotion data to the server.

[1156] Step 6:

[1157] Server: The server receives the user's request and emotion data.

[1158] Server: Searches the database and extracts data that matches the conditions.

[1159] Step 7:

[1160] Server: The generation AI generates data based on the search results. For example, if a user requests "average temperature in Tokyo in March 2023" and the emotional data indicates "tension," the generation AI generates data in a format that gives a sense of relaxation.

[1161] Server: Sends the generated data to the user's terminal.

[1162] Step 8:

[1163] Terminal: The user's terminal receives the generated data and displays it on the screen. For example, it displays "The average temperature in Tokyo in March 2023 will be 15 degrees."

[1164] Step 9:

[1165] Server: Records details of the data used by the generating AI in a log, including which data provider's data was used and the number of times it was used.

[1166] Step 10:

[1167] Server: For each specific period (e.g., at the end of the month), the number of times data is used for each data provider is tallied.

[1168] Server: Calculates the reward based on the aggregated results. For example, 10 yen per use.

[1169] Step 11:

[1170] Server: Transfers the calculated reward to the registered account of the data provider.

[1171] In this way, data providers, users, and servers can cooperate to operate a comprehensive data provision and utilization system, thereby ensuring efficient and fair data provision and an improved user experience.

[1172] Example 2

[1173] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1174] Existing data management systems not only lack the ability to upload, classify, tag, and store data in a database, but also lack the ability to provide optimal data by utilizing user emotion recognition. As a result, it is difficult to provide data that meets user needs, and the user experience cannot be improved. In addition, the process of calculating and paying rewards to data providers is often manual and inefficient.

[1175] 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.

[1176] In this invention, the server includes: means for a data provider to upload data using a terminal; means for analyzing, classifying, and tagging the received data and storing it in a database; means for a user to send a data request from the terminal; means for the terminal to acquire the user's emotional data; means for the server to generate data using a generation AI based on the user's request and emotional data and send it to the user's terminal; means for the server to record in a log how the data has been used; and means for the server to tally the number of times each data provider has used the data and calculate and pay a reward. This makes it possible to provide optimal data according to the user's emotions, improving the user experience and the motivation of data providers at the same time.

[1177] "Data Provider" means any person or entity that uploads and provides data through the System.

[1178] "Terminal" means a computer or mobile device used by a User or Data Contributor to access and operate the System.

[1179] "Server" refers to a central computer system that receives, processes, stores, and distributes data.

[1180] A "database" refers to a structured collection of information that allows a server to classify, tag, and efficiently store and manage data.

[1181] "Generative AI" refers to artificial intelligence that extracts necessary data from a database based on a user request and then generates the data in the optimal format based on the user's emotional data.

[1182] An "emotion engine" refers to software or hardware that has the ability to analyze a user's emotional data and provide that emotional state to a generative AI.

[1183] "Log" refers to a file or database that records system activity, such as data usage.

[1184] "Remuneration" refers to monetary compensation paid according to the number of times the data provided by the data provider is used.

[1185] The "temporary folder" refers to a folder for temporarily storing data received by the server.

[1186] "User" means any person or entity accessing the System and making a request for data.

[1187] The present invention relates to a system in which users and data providers process data mutually through terminals, and a server manages and optimizes this. Specific embodiments are described below.

[1188] Data upload by data contributors

[1189] Terminal

[1190] Data providers log in to the system using a terminal. To log in, they use a username and password. After logging in, they access a dedicated upload form. Using this form, they select a data file (e.g., "weatherdata.csv") and enter detailed data information (e.g., category, date, location, etc.). When they press the upload button, the data file and its details are sent to the server.

[1191] Server processing and storage of data

[1192] server

[1193] The server saves the received data files and detailed information in a temporary folder. The server then analyzes the data files and assigns categories and tags based on their content (e.g., "Weather Data," "Temperature," "Humidity," "March 2023," "Tokyo," etc.). Python libraries such as pandas and NumPy can be used for analysis. The classified and tagged data is stored in a database using an RDBMS such as MySQL or PostgreSQL.

[1194] User data requests and emotion data acquisition

[1195] Terminal

[1196] The user accesses the system from their device, inputs the conditions for the required data (e.g., period, location, category, etc.), and sends a request. At the same time, the user's emotional data is acquired. To acquire the emotional data, a text analysis library (e.g., TextBlob) or an emotion recognition sensor is used.

[1197] Server-generated data

[1198] server

[1199] The server receives the user's request and emotion data. The generation AI then searches the database based on the request conditions and extracts matching data. The generation AI can use OpenAI's GPT model, for example. Furthermore, it generates data in a format that is optimal for the user based on the emotion data provided by the emotion engine. For example, if a user requests "average temperature in Tokyo in March 2023" and "relaxed feeling" is detected based on the emotion data, the generation AI will present the data in a format that gives a sense of relaxation.

[1200] Returning data

[1201] server

[1202] The server sends the generated data back to the user's device, which displays, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[1203] Usage tracking and reward calculation

[1204] server

[1205] The server logs details of the data used by the generating AI (such as which data provider's data was used and the number of times it was used). In addition, the server aggregates the number of times each data provider used it for a specific period (e.g., the end of the month) and calculates the reward (e.g., "10 yen per use"). The calculated reward is transferred to the data provider's registered account.

[1206] Specific examples

[1207] 1. Data provider operations

[1208] The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal, enters detailed information such as "weather data," "March 2023," and "Tokyo" into the form, and presses the upload button.

[1209] The server receives the data and detailed information, saves it in a temporary folder, analyzes it, assigns tags such as "weather data," "temperature," "humidity," "March 2023," and "Tokyo," and stores it in a database.

[1210] 2. User Operation

[1211] A weather forecaster requests "average temperature in Tokyo in March 2023." The emotion engine detects "tension" from the user's text.

[1212] The server receives the request and emotion data, and the generation AI searches the database to extract the relevant data.

[1213] The generating AI presents average temperature data in a format that gives a sense of relaxation suitable for users in a state of "tension."

[1214] The server provides the generated results to the user's device, which displays the message, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[1215] This will enable the realization of a system that improves the user experience by incorporating emotion recognition, increases the motivation of data providers, and enables users to quickly and accurately obtain the data they need.

[1216] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1217] Step 1:

[1218] Data provider login

[1219] User: The data provider logs into the system using a terminal. As input, they use their username and password. As output, they are granted access to the system and a dedicated upload form is displayed.

[1220] Step 2:

[1221] Uploading data

[1222] User: The data provider selects a data file in the upload form and enters detailed information (category, date, location, etc.). The input is the data file (e.g., "weatherdata.csv") and the detailed information. The output is the data file and the detailed information sent from the terminal to the server.

[1223] Step 3:

[1224] Receiving and temporarily storing data

[1225] Server: Receives data files and detailed information and saves them in a temporary folder. As input, it receives data files and detailed information sent from the device. As output, it saves the data files in a temporary folder.

[1226] Step 4:

[1227] Data analysis and tagging

[1228] Server: Analyzes data files saved in a temporary folder. The input is the saved data file. Specifically, the data file is analyzed using Python libraries such as pandas and NumPy. As output, categories and tags (e.g., "weather data," "temperature," "humidity," "March 2023," "Tokyo," etc.) are assigned based on the content of the data.

[1229] Step 5:

[1230] Storage in the database

[1231] Server: Stores the classified and tagged data in an RDBMS such as MySQL or PostgreSQL. The input is a tagged data file. The specific operation is to store the data in the database using SQL queries. The output is structured data stored in the database.

[1232] Step 6:

[1233] Sending a Data Request

[1234] User: The user inputs the conditions for the required data and submits a request. As input, there are the conditions for the required data (e.g., period, location, category, etc.). As output, the request is sent to the server. At the same time, emotion data is obtained.

[1235] Step 7:

[1236] Acquiring emotion data

[1237] Device: Obtain user emotional data using a text analysis library (e.g., TextBlob) or an emotion recognition sensor. Inputs include user text information and sensor data. Outputs include analyzed emotional data.

[1238] Step 8:

[1239] Receiving requests and emotion data

[1240] Server: Receives user request data and emotion data. As input, there is the request data and emotion data sent by the user. As output, the received data is passed to the generation AI.

[1241] Step 9:

[1242] Data retrieval and generation

[1243] Server: The generation AI searches the database based on the request conditions and emotion data, and extracts matching data. The inputs are the request conditions and emotion data. The specific operation is to search the database using the generation AI (e.g., OpenAI's GPT model). The output is the extraction of matching data.

[1244] Step 10:

[1245] Optimal Data Generation

[1246] Server: The generation AI generates the data in the optimal data format for the user based on the emotion data provided by the emotion engine. The inputs are emotion data and extracted data. Specifically, the generation AI creates a data format that matches the emotion. The output is optimized data.

[1247] Step 11:

[1248] Returning data

[1249] Server: Returns the generated data to the user's device. The input is the optimized data. The output is the data displayed on the user's device. The user's device displays, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[1250] Step 12:

[1251] Usage record

[1252] Server: Records details of the data used by the generating AI in a log. The input is the data used and its details. Specific operations include recording operations to log files and databases. The output is a record of usage.

[1253] Step 13:

[1254] Reward calculation and payment

[1255] Server: Aggregates the number of uses by each data provider for a specific period and calculates rewards. The input is usage data from the log. Specific operations include aggregation and reward calculation. The output is the calculated reward being deposited into the data provider's registered account.

[1256] (Application example 2)

[1257] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1258] Conventional data provision systems have the problem that the methods for utilizing data uploaded by data providers and the management of its usage status are complicated, and the data provided to users is simply in response to requests, making it difficult to provide optimal information according to the user's emotional state.In particular, autonomous vehicles are required to properly recognize the driver's emotions and provide optimal navigation and assistance based on those emotions, but conventional systems are not able to adequately address this issue either.

[1259] 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: a means for a data provider to upload data using a terminal; a means for the server to classify and tag the received data and store it in a database; a means for a user to send a data request from a terminal; a means for the server to generate data using a generation AI based on the user's request and send it to the user's terminal; a means for the server to log the usage status of the data used by the server; a means for the server to tally the number of uses for each data provider and calculate and pay a reward; a means for the server to analyze the driver's emotional data and apply it to the navigation system; and a means for the navigation system to generate an optimal route based on the emotional data and present it to the driver. This makes it easier for data providers to understand how their data is being used, and enables users to receive optimal data and navigation according to their emotional state.

[1260] "Data Provider" means an individual or entity that generates and uploads data to the system.

[1261] "Terminal" refers to a device used by data providers and users to upload data, send requests, etc.

[1262] A "server" is a computer that is the core of the system and is a device that performs various processes such as receiving, classifying, storing, generating, and logging data.

[1263] "Generative AI" is artificial intelligence that generates data based on user requests and sends it to the user's device.

[1264] A "database" is a storage device where the server stores classified and tagged data.

[1265] "Emotion data" is information indicating the emotional state of the driver analyzed based on data such as facial expressions, voice, and heart rate.

[1266] A "navigation system" is a system that generates an optimal route based on the driver's emotional data and presents it to the driver.

[1267] A "request" is a request made by a user to the system through a terminal for specific data or navigation information.

[1268] "Usage status" is information indicating the usage history and frequency of data generated by the server using the generated AI.

[1269] "Remuneration" means monetary compensation received by a data provider for the use of their data.

[1270] A "log" is data that records a series of operations and data usage status performed on a server.

[1271] A "prompt" is text data containing instructions or questions that are input into a generative AI model.

[1272] A "temporary folder" is an area where the server temporarily stores data received.

[1273] This invention provides an emotion-responsive navigation system for autonomous vehicles. This system provides optimal navigation according to the emotional state of the driver of the autonomous vehicle, realizing a comfortable driving environment.

[1274] First, the system uses the following hardware and software:

[1275] Hardware:

[1276] Internal camera (for driver facial expression recognition)

[1277] Various sensors (heart rate sensor, voice recognition microphone, etc.)

[1278] In-vehicle display

[1279] server

[1280] software:

[1281] Emotion recognition engine: Emotional AI SDK

[1282] Generative AI model: OpenAI GPT-4

[1283] Cloud database: AWS RDS

[1284] Navigation data: Google Maps API

[1285] When the driver gets into the vehicle, the internal camera and sensors collect emotional data such as facial expressions, voice, and heart rate in real time, which are then transmitted to the server via the terminal.

[1286] The server analyzes the received emotion data and identifies the driver's current emotional state using the Emotional AI SDK. Identified emotional states include "stress," "fatigue," and "relaxation."

[1287] The driver requests a destination and navigation conditions through the in-car display or voice assistant, and emotion data is sent to the server at the same time.

[1288] The server generates optimal navigation data based on the received request and emotion data using a generative AI model, OpenAI GPT-4, and Google Maps API to obtain the necessary navigation data.

[1289] For example, if a driver is in a "stressed" state, the generative AI model will select a scenic route that avoids crowds and encourages relaxation. If a driver is feeling "fatigued," the model will recommend a route with rest areas and toilets.

[1290] The generated navigation data is displayed on the driver's in-vehicle display, allowing the driver to receive navigation information optimized for their emotional state.

[1291] For example, a sample prompt for a generative AI model might look like this:

[1292] "My emotional state is 'stressed'. I will be driving from Tokyo to Yokohama at 1:00 PM on October 20, 2023. Please advise me of a relaxing route that avoids crowds."

[1293] This system allows drivers to relax even in stressful driving situations, resulting in an overall more comfortable driving experience.The invention also takes into consideration the interests of data providers, automatically calculating and paying compensation for data used based on how the data is used.

[1294] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1295] Step 1:

[1296] The server collects the driver's emotional data in real time from the internal camera and various sensors. Specifically, the camera recognizes facial expressions, and the sensors acquire heart rate and voice data. Based on this input data, the Emotional AI SDK is used to analyze the driver's emotional state and output it as emotional data.

[1297] Step 2:

[1298] The device receives a navigation request from the driver. The driver inputs the destination and navigation conditions through the display or voice assistant. For example, a condition such as "I want to avoid congestion" is specified. The request data is sent to the server.

[1299] Step 3:

[1300] The server receives the emotion data obtained in step 1 and the request data obtained in step 2. Based on this data, it processes the input using a generative AI model (OpenAI GPT-4) and generates appropriate navigation data. This generation process takes into account relaxing routes and routes that avoid congestion.

[1301] Step 4:

[1302] The server then connects the generated navigation data to the Google Maps API to obtain more detailed route information. This completes the final navigation data, which is then ready to be provided to the driver. The output data is optimized route information.

[1303] Step 5:

[1304] The generated navigation data is sent to the driver's device, and route information is displayed on the in-vehicle display for the driver to review. In addition, the voice assistant provides navigation guidance, improving driving safety and comfort.

[1305] Step 6:

[1306] The server records the usage of navigation data in a log. This logs information such as how much data from which data provider was used and details of the data used by the generating AI. This accumulates basic data for subsequent reward calculations.

[1307] Step 7:

[1308] The server tally up the number of times data providers have used the service for a specific period, such as the end of the month. It calculates how much data from each data provider has been used and calculates rewards based on that. The calculated reward data is then transferred to the data provider's registered account.

[1309] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1310] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1311] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1312] [Fourth embodiment]

[1313] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1314] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1315] 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).

[1316] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1317] 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.

[1318] 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).

[1319] 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.

[1320] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1321] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1322] 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.

[1323] 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.

[1324] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1325] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1326] This invention relates to a system in which data providers upload data via their devices, and a server receives, classifies, tags, and stores the data in a database. In this system, when a user sends a data request from their device, the server uses a generation AI to generate data according to the request and sends it to the user's device. Furthermore, the server records the usage status of the data in a log, tallying the number of times each data provider uses it, calculates rewards, and periodically pays them.

[1327] Program processing

[1328] 1. Data Collection:

[1329] Terminal: The data provider uses the terminal to access a dedicated upload form. The data provider selects the data file they own, enters detailed information about the data (e.g., data category, date, location, etc.), and presses the upload button to send the data to the server.

[1330] Server: The server receives the data file and its detailed information sent from the terminal.

[1331] 2. Data storage:

[1332] Server: The server saves the received data files in a temporary folder.

[1333] Server: Analyzes the contents of data files and classifies them by category and tag. Appropriate tags (e.g., "weather data," "temperature," "humidity," etc.) are assigned to each data.

[1334] Server: Stores the classified and tagged data in a database.

[1335] 3. Requesting and generating data:

[1336] User: The user accesses the system through a terminal, inputs the conditions for the data they require (e.g., a specific period, location, data category, etc.), and submits a request.

[1337] Server: The server receives the user's request, searches the database, and extracts data that matches the criteria.

[1338] Server: The AI ​​generates the requested data based on the search results. For example, if a user requests "average temperature in Tokyo in March 2023," the AI ​​aggregates the temperature data for the relevant period and calculates the average temperature.

[1339] Server: Returns the generated data to the user's terminal.

[1340] Terminal: The user's terminal displays the generated data.

[1341] 4. Usage Tracking:

[1342] Server: The server logs which data providers' data the generating AI used and how much of it was used, thereby accurately understanding the data usage status of each data provider.

[1343] 5. Calculation and Payment of Rewards:

[1344] Server: The server aggregates the amount of use of data from each data provider for each specific period (for example, at the end of the month).

[1345] Server: Based on the aggregated results, calculate the reward for each data provider. For example, calculate the amount based on the total number of uses, assuming "10 yen per use."

[1346] Server: Transfers the calculated reward to the registered account of the data provider.

[1347] Specific examples

[1348] 1. Data Collection:

[1349] Terminal: The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal. Detailed information such as "weather data," "March 2023," and "Tokyo" is entered into the form.

[1350] Server: Receives the data and details and stores them in a temporary folder.

[1351] 2. Data storage:

[1352] Server: Analyzes the received data and tags it with information such as "weather data," "temperature," "humidity," "March 2023," and "Tokyo."

[1353] Server: Stores the tagged data in a database.

[1354] 3. Requesting and generating data:

[1355] User: A weather forecaster requests "average temperature in Tokyo in March 2023" on their device.

[1356] Server: Receives the request and searches the database. The relevant data is extracted and the generating AI calculates the average temperature.

[1357] Server: Provides the calculation results to the user's terminal.

[1358] Terminal: Display "The average temperature in Tokyo in March 2023 will be 15 degrees" on the user terminal.

[1359] 4. Usage Tracking:

[1360] Server: Logs which data is used and how much. For example, it records how many times a researcher's weather data is used.

[1361] 5. Calculation and Payment of Rewards:

[1362] Server: At the end of the collection period, the server counts the 100 times the researcher's data has been used.

[1363] Server: Calculates a reward of 10 yen per 100 uses and transfers 1,000 yen to the researcher.

[1364] In this way, the system and program of the present invention enable users to quickly and accurately obtain the data they need while increasing the motivation of data providers.

[1365] The processing flow will be explained below.

[1366] Step 1:

[1367] Terminal: The data provider logs into the system using a terminal.

[1368] Terminal: The data provider accesses the upload form, selects the data file, and enters data details (category, date, location, etc.).

[1369] Terminal: When the data provider presses the upload button, the terminal sends the data file and detailed information to the server.

[1370] Step 2:

[1371] Server: The server receives the data file and detailed information sent from the device.

[1372] Server: Save the received data in a temporary folder.

[1373] Step 3:

[1374] Server: Analyzes the stored data files and assigns categories and tags based on the data content. For example, tags such as "weather data," "temperature," and "humidity" are assigned.

[1375] Server: Stores the classified and tagged data in a database.

[1376] Step 4:

[1377] User: The user accesses the system through a terminal and navigates to the data request form.

[1378] User: Enters the criteria for the data they need (e.g., a specific time period, location, data category, etc.) and submits a request.

[1379] Step 5:

[1380] Server: The server receives the user's request and searches the database to extract data that matches the criteria.

[1381] Step 6:

[1382] Server: The generation AI generates the requested data based on the search results. For example, if a request is made for "average temperature in Tokyo in March 2023," the generation AI aggregates the relevant data and calculates the average temperature.

[1383] Server: Returns the generated data to the user's terminal.

[1384] Step 7:

[1385] Terminal: The user's terminal receives the generated data and displays it on the screen.

[1386] Step 8:

[1387] Server: Records details of the data used by the generating AI in a log, including which data provider's data was used and the number of times it was used.

[1388] Step 9:

[1389] Server: For each specific period (e.g., at the end of the month), the number of times data is used for each data provider is tallied.

[1390] Server: Calculates the reward based on the aggregated results. For example, 10 yen per use.

[1391] Step 10:

[1392] Server: Transfers the calculated reward to the registered account of the data provider.

[1393] In this way, data providers, users, and servers cooperate to operate the system, ensuring efficient and fair provision and use of data.

[1394] Example 1

[1395] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1396] In recent years, data collection, management, and provision have become important issues in many industries, but there is a lack of a system that can efficiently manage data uploaded by various data providers, quickly provide necessary data to users, and distribute appropriate rewards to data providers.In addition, there is a challenge in accurately understanding the usage status of the data uploaded by data providers and paying rewards in a transparent manner.

[1397] 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.

[1398] In this invention, the server includes: a means for a data provider to upload data using a terminal; a means for the server to classify and tag the received data and store it in an information storage device; a means for a user to send a data request from the terminal; a means for the server to generate data using a generative AI model based on the user request and send it to the user terminal; a means for the server to record the usage status of the data used by the server on a recording medium; and a means for the server to tally the number of times each data provider uses the data and calculate and pay rewards. This enables efficient management of data uploaded by data providers, fast and accurate provision of data to users, and appropriate distribution of rewards to data providers.

[1399] A "data provider" is a person or organization that uses a terminal to upload data to the system.

[1400] "Terminal" means a computing device used by a user or data provider to access the system and to send and receive data.

[1401] A "server" is a central computing system that receives, stores, analyzes, and generates data.

[1402] An "information storage device" is a database or storage system that stores data persistently after it has been classified and tagged.

[1403] A "generative AI model" is an artificial intelligence model used to generate necessary data based on a user request.

[1404] A "recording medium" is a log system or database that records the usage of data used by a generative AI model.

[1405] "Remuneration" is the consideration paid to the data provider based on the number of times the data is used.

[1406] "Use count" refers to the number of times a particular piece of data is requested and used by a user.

[1407] A "data request" is a request for data of specific conditions that a user requests from the system via a terminal.

[1408] MODE FOR CARRYING OUT THE INVENTION

[1409] The present invention relates to a system in which data providers upload data via their terminals, and a server receives, classifies, tags, and stores the data in an information storage device. In this system, when a user sends a data request from their terminal, the server generates data according to the request using a generative AI model and sends it to the user's terminal. Furthermore, the server records the usage status of the data used on a recording medium, tallying the number of times each data provider has used it, calculates rewards, and periodically pays them.

[1410] Data collection

[1411] Terminal: The data provider accesses the dedicated web application and clicks the "Upload Data" button. They select the file to upload from their PC (e.g., "Tokyo weather data for March 2023.csv"), enter detailed information such as the data category (e.g., weather data), date (e.g., March 2023), and location (e.g., Tokyo) in the form, and click the upload button to send the data to the server.

[1412] Server: Receives uploaded data files and their details and safely stores them in a temporary folder. The HTTP protocol is used for receiving, and uploaded files are temporarily stored on the server's disk.

[1413] Data storage

[1414] Server: Analyze the data files saved in the temporary folder using an automatic analysis program (e.g., Python's pandas library). During analysis, the data items and formats are checked and the contents are structured.

[1415] Server: Based on the analyzed data, it assigns categories (e.g., weather data) and tags (e.g., temperature, humidity, location, date, etc.). For tagging, it may use a predefined schema file.

[1416] Server: Stores tagged data in an information storage device such as MongoDB, allowing for quick search when retrieving data.

[1417] Requesting and Generating Data

[1418] User: The user accesses a data request form through the web application, where they enter specific criteria (e.g., average temperature in Tokyo in March 2023) and press the submit button.

[1419] Server: Receives user requests and queries the database to find data that matches the criteria. This is done using MongoDB's query functionality.

[1420] Server: Based on the search results, a generative AI model (e.g., TensorFlow or GPT-3) is used to generate the requested data. For example, if a user requests "average temperature in Tokyo in March 2023," the generative AI model aggregates temperature data for the relevant period and calculates the average temperature.

[1421] Server: Converts the generated data into JSON format and returns it to the user's device.

[1422] Device: The user's device analyzes the returned data and displays a message on a web page, such as "The average temperature in Tokyo in March 2023 will be 15 degrees."

[1423] Usage Tracking

[1424] Server: Records detailed logs of which data providers' data the generative AI model used and to which users it provided it to, for example, by creating log entries containing information such as data provider ID, data item, and number of uses.

[1425] Server: This information is stored on a recording medium using a log management system such as MySQL or Elasticsearch.

[1426] Reward calculation and payment

[1427] Server: Periodically (e.g., at the end of each month), aggregate how much data from each data provider has been used. Scan the log data and record the number of times each data provider has been used.

[1428] Server: Calculates rewards based on the number of uses. For example, it is calculated as "10 yen per use."

[1429] Server: Based on the statistical results, the reward is transferred to the data provider's registered bank account. This can be done using a financial API (e.g., Stripe or PayPal).

[1430] Examples of concrete examples and prompts

[1431] 1. Data collection

[1432] Terminal: The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal. He enters "weather data," "March 2023," and "Tokyo" into the form.

[1433] Server: Receives this data and details and stores them in a temporary folder.

[1434] 2. Data Storage

[1435] Server: Analyzes the stored data using the pandas library, adds categories and tags, and stores them in a MongoDB database.

[1436] 3. Requesting and Generating Data

[1437] User: A weather forecaster requests "Average temperature in Tokyo in March 2023."

[1438] Server: Performs queries and calculates average temperatures using a generative AI model.

[1439] Server: Returns the result in JSON and displays "The average temperature in Tokyo in March 2023 will be 15 degrees" on the user's device.

[1440] 4. Usage Tracking

[1441] Server: Logs data usage. Example: A researcher's weather data is used 100 times.

[1442] 5. Calculation and Payment of Rewards

[1443] Server: At the end of the collection period, calculate the number of uses and the total reward. For example, if there are 100 uses, the total reward will be calculated as 10 yen per use, for a total of 1,000 yen.

[1444] Server: Transfers rewards to the data provider's bank account using Stripe API.

[1445] Prompt Sentence Examples

[1446] "What is the average temperature in Tokyo in March 2023?"

[1447] "Please provide a list of temperature and humidity data for Tokyo in March 2023."

[1448] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1449] Step 1: Collect data

[1450] Terminal: The data provider accesses a dedicated web application. They log in with their user ID and password and click the "Upload Data" button. They select the file to upload from their PC (e.g., "Tokyo weather data for March 2023.csv"), enter detailed information in the form, such as the data category (e.g., weather data), date (e.g., March 2023), and location (e.g., Tokyo), and then press the submit button. Input: Data file and detailed information. Output: Upload request.

[1451] Server: Receives upload requests and saves data files and details in a temporary folder. Input: Upload request. Output: File saved in the temporary folder.

[1452] Step 2: Store the data

[1453] Server: Reads data files saved in a temporary folder and analyzes them using an automatic analysis program (e.g., Python's pandas library). During analysis, the data items and format are checked and the contents are structured. Input: Data files read from the temporary folder. Output: Structured data.

[1454] Server: Based on the structured data, assigns categories (e.g., weather data) and tags (e.g., temperature, humidity, location, date, etc.). A predefined schema is used for tagging. Input: Structured data. Output: Tagged data.

[1455] Server: Stores tagged data in an information storage device such as MongoDB. Input: Tagged data. Output: Data stored in the database.

[1456] Step 3: Request the data

[1457] User: The user accesses the data request form through the web application, enters specific criteria (e.g., average temperature in Tokyo in March 2023), and presses the submit button. Input: Data request. Output: Request submitted.

[1458] Server: Receives data requests and queries the database to find data that matches the criteria. Input: Data request. Output: Search results.

[1459] Step 4: Generate data

[1460] Server: Based on the search results, a generative AI model (e.g., TensorFlow or GPT-3) is used to generate the requested data. For example, if a user requests "average temperature in Tokyo in March 2023," the generative AI model aggregates temperature data for the relevant period and calculates the average temperature. Input: Search results. Output: Generated data.

[1461] Server: Converts the generated data into JSON format and returns it to the user's device. Input: Generated data. Output: Returned data.

[1462] Step 5: View the generated data

[1463] Terminal: The user's terminal receives the returned data, analyzes it, and displays it. For example, it displays "The average temperature in Tokyo in March 2023 is 15 degrees" on a web page. Input: The returned data. Output: The displayed data.

[1464] Step 6: Track usage

[1465] Server: Records detailed logs of which data providers' data the generative AI model used and to which users it provided it. For example, it creates log entries containing information such as data provider ID, data item, and number of uses. Input: Data usage information. Output: Recorded log.

[1466] Server: This information is stored on a recording medium using a log management system such as MySQL or Elasticsearch. Input: Recorded logs. Output: Stored logs.

[1467] Step 7: Calculating and paying rewards

[1468] Server: Periodically (e.g., at the end of each month), aggregate how much data from each data provider has been used. Scan the log data and count the number of uses for each data provider. Input: Stored logs. Output: Aggregated results of the number of uses.

[1469] Server: Calculates rewards based on the number of uses. For example, it is calculated as "10 yen per use." Input: Aggregated results of number of uses. Output: Reward calculation results.

[1470] Server: Based on the statistical results, the reward is transferred to the data provider's registered bank account. This is done using a financial API (e.g., Stripe or PayPal). Input: Reward calculation results. Output: Transfer completion notification.

[1471] (Application example 1)

[1472] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1473] In the manufacturing field, while the real-time collection and use of various data is important, data collection, management, and optimization are complex and there is a lack of efficient systems. Furthermore, data providers often do not receive fair compensation, which reduces their motivation to provide data. In such an environment, optimizing the manufacturing process is difficult, and as a result, production efficiency is likely to decline.

[1474] 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.

[1475] In this invention, the server

[1476] A means for a data provider to upload data using a terminal;

[1477] means for classifying and tagging the data received by the server and storing it in a database;

[1478] A means for a user to send a data request from a terminal;

[1479] A means for the server to generate data using a generation AI based on a user request and transmit the data to the user terminal;

[1480] a means for logging usage of the data used by the server;

[1481] The server counts the number of uses for each data provider, calculates the reward, and pays it;

[1482] A means for the robot to collect data in real time according to the manufacturing process and upload it to a server;

[1483] a means for the robot to request data required for a particular manufacturing process from the server;

[1484] Includes:

[1485] This will enable efficient data collection, management, and optimization at manufacturing sites, and will also increase motivation for data provision by ensuring appropriate compensation for data providers. Furthermore, the use of generative AI will enable the rapid and accurate generation of requested data, facilitating optimization of manufacturing processes.

[1486] A "data provider" is an entity that generates valuable data related to manufacturing processes or other systems and provides it to the system.

[1487] "Terminal" means an electronic device used by Data Contributors and Users to access the System and to upload or request Data.

[1488] "Server" means a computer system that receives, categorizes, tags, stores data in a database, generates data based on requests, records logs, calculates rewards, and pays out.

[1489] A "user" is an entity that utilizes the system to request specific data and receive the results.

[1490] "Generative AI" is artificial intelligence used to generate necessary data based on user requests.

[1491] A "data request" is an act in which a user requests data required based on specific conditions from the system.

[1492] A "database" is an information system for systematically storing categorized and tagged data.

[1493] A "log" is a digital record generated by a server to record data usage.

[1494] The "number of times data is used" is an index that indicates the frequency with which a user accesses and uses specific data.

[1495] "Remuneration" means monetary or other consideration paid to a Data Provider based on the use of the Data provided by that Data Provider.

[1496] "Robots" are automated machines that collect and request data during the manufacturing process and work in conjunction with a server to optimize operations.

[1497] "Manufacturing process" refers to the series of steps used to produce a product from raw materials.

[1498] The present invention provides a system for collecting data in a factory and optimizing the data. Specific embodiments of the present invention will be described below.

[1499] System Configuration

[1500] This system consists of a data provider terminal, a user terminal, a server, and a factory robot.

[1501] Data provider terminal

[1502] Data providers upload data using terminals. Terminals include devices that can input and send data, such as PCs, smartphones, and tablets. The provided data includes valuable information related to the manufacturing process (e.g., sensor data, work history, quality check data, etc.).

[1503] server

[1504] The server analyzes the received data and categorizes and tags it. When storing it in a database, it organizes it by assigning specific categories and tags (e.g., "weather data," "temperature," "humidity," etc.). It also receives data requests from users and generates appropriate data using generation AI. It also tracks usage and records the number of times each data provider has used the service in a log. It also calculates compensation to data providers for specific periods and transfers it to their registered accounts.

[1505] Factory robots

[1506] Factory robots collect data in real time according to the manufacturing process and upload it to the server. If the robot needs data, it sends a data request to the server and optimizes the manufacturing process based on the data provided by the server.

[1507] Processing flow

[1508] 1. Data Collection and Upload

[1509] Data providers upload data through terminals, and factory robots also use sensors to collect data in real time and send it to a server.

[1510] 2. Data Storage and Classification

[1511] The server analyzes the received data, categorizes and tags it appropriately, and stores it in a database using a database management system (e.g., MySQL).

[1512] 3. Data Request and Generation

[1513] When users and factory robots request specific data, they send conditional requests to the server. The server uses generative AI (e.g., natural language processing models) to generate the required data. For example, a prompt might be entered: "Calculate the average temperature of production line A in October 2023."

[1514] 4. Usage Tracking and Reward Calculation

[1515] The server logs the usage of the generated data and tally the number of times each data provider has used it for a specific period (e.g., the end of the month). Based on this, the reward is calculated and transferred to the data provider's registered account. The software used includes log management tools (e.g., the Python logging module) and data analysis tools (e.g., Pandas).

[1516] Specific examples

[1517] Specific examples of data collection and uploading

[1518] The data provider uploads "Production Line A's October temperature data.csv" from their terminal. The factory robot sends "Production Line A's real-time temperature data for October 2023" to the server.

[1519] Examples of data request and generation

[1520] The user requests "the average temperature of production line A in October 2023." The server uses the generative AI and the "generative AI model" to generate data based on the prompt, "Please calculate the average temperature of production line A in October 2023," and sends it to the user's device.

[1521] In this way, the system can achieve effective data management and optimization of the manufacturing process, while also ensuring fair compensation for data providers.

[1522] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1523] Step 1:

[1524] The data provider uploads data using a terminal. On the terminal, the provider selects a data file and also enters detailed data information (e.g., category, date, location, etc.) through a dedicated upload form. When executed, the data and its details are sent to the server. The input data is, for example, "Temperature data.csv for production line A" and detailed information such as "October 2023, production line A, temperature data," and the output is an HTTP request containing the uploaded data file and the detailed information.

[1525] Step 2:

[1526] The server classifies and tags the received data and stores it in a database. First, it saves the transmitted data file in a temporary folder. Next, it analyzes the content of the data and assigns appropriate categories and tags (e.g., "Temperature," "Humidity," "Production Line A," "October 2023," etc.). After that, it uses a database management system (e.g., MySQL) to store the classified and tagged data in a database. The input is the data file and detailed information received by the server, and the output is the classified and tagged data stored in the database.

[1527] Step 3:

[1528] Users and factory robots request specific data. The user terminal or factory robot accesses the system, inputs the conditions for the required data (e.g., specific period, location, data category, etc.), and sends a request. The input is the conditions requested by the user or robot, and the output is the data request sent to the server.

[1529] Step 4:

[1530] The server receives a user or robot request and searches the database. Based on the search results, a generative AI is used to generate the requested data. For example, a prompt statement such as "Please calculate the average temperature of production line A in October 2023" is input into the generative AI model. The generative AI analyzes the relevant data and calculates the average temperature. The inputs are the search conditions and the prompt statement input into the generative AI, and the output is the calculated average temperature data.

[1531] Step 5:

[1532] The server sends the generated data to the user terminal or factory robot. The generation AI formats the generated data and sends it to the user or robot terminal in a form that can be displayed. The input is the data generated by the generation AI, and the output is the data sent to the user or robot terminal.

[1533] Step 6:

[1534] The server logs the usage of the data used. By recording which data provider's information the generative AI model used and the number of times it was used, the system tracks the details of usage for each data provider. The input is the usage information for the generated data, and the output is the usage data recorded in the log file.

[1535] Step 7:

[1536] The server tally the number of uses for each data provider, calculates the reward, and pays it. At specific intervals, the server tally the number of uses and calculates the reward for the data provider based on a predetermined reward unit price. The reward is then transferred to the provider's registered account. The input is the number of uses for each data provider, and the output is the reward calculation result and a transfer completion notification.

[1537] 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.

[1538] This invention relates to a system in which data providers upload data via their devices, and a server receives, classifies, tags, and stores the data in a database. The system also incorporates an emotion engine that recognizes user emotions and reflects them in data generation. When a user sends a data request from their device, the system's generation AI and emotion engine generate data according to the request and send it to the user's device. Furthermore, the server records the usage status of the data in a log, tallying the number of times each data provider uses the data, and calculates and periodically pays rewards.

[1539] Program processing

[1540] 1. Data Collection:

[1541] Terminal: The data provider logs in to the system using a terminal. The data provider accesses a dedicated upload form, selects a data file, enters detailed data information (e.g., category, date, location, etc.), and presses the upload button, which sends the data and detailed information to the server.

[1542] 2. Data storage:

[1543] Server: The server receives the data files and detailed information sent from the device and stores them in a temporary folder. It then analyzes the data files and assigns categories and tags based on their content (e.g., "weather data," "temperature," "humidity," etc.). The categorized and tagged data is then stored in a database.

[1544] 3. Requesting and generating data:

[1545] User: The user accesses the system through a terminal, inputs the conditions for the required data (e.g., a specific period, location, category, etc.), and submits a request.

[1546] Terminal: At the same time, the user's emotional data (e.g., emotional state from text analysis or data from emotion recognition sensors) is acquired by the emotion engine.

[1547] Server: The server receives the user's request and emotion data, and the generation AI searches the database to extract data that matches the conditions.

[1548] Server: The generation AI generates data that is optimal for the user based on the emotional data provided by the emotion engine. For example, if a user requests the "average temperature in Tokyo in March 2023" and the "feeling relaxed" is detected based on the emotional data, the generation AI will present the data in a format that gives a feeling of relaxation.

[1549] Server: Returns the generated data to the user's terminal.

[1550] 4. Usage Tracking:

[1551] Server: Logs details of the data used by the generating AI (such as which data provider's data was used and how many times it was used).

[1552] 5. Calculation and Payment of Rewards:

[1553] Server: The server tally the number of times each data provider uses data for a specific period (e.g., the end of the month) and calculates the reward (e.g., "10 yen per use").

[1554] Server: Transfers the calculated reward to the registered account of the data provider.

[1555] Specific examples

[1556] 1. Data Collection:

[1557] Terminal: The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal and enters detailed information such as "weather data," "March 2023," and "Tokyo" into the form. When the researcher presses the upload button, the data is sent to the server.

[1558] Server: Receives data and detailed information and saves it in a temporary folder. Analyzes it, assigns tags such as "weather data," "temperature," "humidity," "March 2023," and "Tokyo," and stores it in a database.

[1559] 2. Requesting and generating data:

[1560] User: A weather forecaster requests "Average temperature in Tokyo in March 2023" on their device. The emotion engine detects "tension" in the user's text.

[1561] Server: Receives the request and emotion data, and the generation AI searches the database to extract the relevant data.

[1562] Server: The generation AI calculates and presents average temperature data in a format that gives a sense of relaxation suitable for users in a "tense" state.

[1563] Server: Provides the generated results to the user's device. The device displays, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[1564] 3. Usage Tracking:

[1565] Server: Logs which data is used and how much. For example, it records how many times a researcher's weather data is used.

[1566] 4. Calculation and payment of rewards:

[1567] Server: At the end of the collection period, the server counts the 100 times the researcher's data has been used.

[1568] Server: Calculates a reward of 10 yen per 100 uses and transfers 1,000 yen to the researcher.

[1569] In this way, the system and program of the present invention improve the user experience by incorporating emotion recognition, increasing the motivation of data providers while enabling users to quickly and accurately obtain the data they need.

[1570] The processing flow will be explained below.

[1571] Step 1:

[1572] Terminal: The data provider logs into the system using a terminal.

[1573] Terminal: The data provider accesses the dedicated upload form, selects the data file (e.g., "Tokyo weather data for March 2023.csv"), enters the data details (category, date, location), and presses the upload button.

[1574] Step 2:

[1575] Server: The server receives the data file and detailed information sent from the device.

[1576] Server: Save the received data in a temporary folder.

[1577] Step 3:

[1578] Server: Analyzes the stored data files and assigns categories and tags based on their content ("weather data," "temperature," "humidity," "March 2023," "Tokyo").

[1579] Server: Stores the classified and tagged data in a database.

[1580] Step 4:

[1581] User: The user accesses the system through a terminal and navigates to the data request form.

[1582] User: Enters the criteria for the data they need (specific time period, location, category, etc.) and submits a request.

[1583] Step 5:

[1584] Device: The emotion engine collects the user's emotional data. For example, it analyzes the user's emotional state, such as "tension," from the user's text input and data from the facial recognition camera.

[1585] Terminal: The emotion engine sends the user's emotion data to the server.

[1586] Step 6:

[1587] Server: The server receives the user's request and emotion data.

[1588] Server: Searches the database and extracts data that matches the conditions.

[1589] Step 7:

[1590] Server: The generation AI generates data based on the search results. For example, if a user requests "average temperature in Tokyo in March 2023" and the emotional data indicates "tension," the generation AI generates data in a format that gives a sense of relaxation.

[1591] Server: Sends the generated data to the user's terminal.

[1592] Step 8:

[1593] Terminal: The user's terminal receives the generated data and displays it on the screen. For example, it displays "The average temperature in Tokyo in March 2023 will be 15 degrees."

[1594] Step 9:

[1595] Server: Records details of the data used by the generating AI in a log, including which data provider's data was used and the number of times it was used.

[1596] Step 10:

[1597] Server: For each specific period (e.g., at the end of the month), the number of times data is used for each data provider is tallied.

[1598] Server: Calculates the reward based on the aggregated results. For example, 10 yen per use.

[1599] Step 11:

[1600] Server: Transfers the calculated reward to the registered account of the data provider.

[1601] In this way, data providers, users, and servers can cooperate to operate a comprehensive data provision and utilization system, thereby ensuring efficient and fair data provision and an improved user experience.

[1602] Example 2

[1603] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1604] Existing data management systems not only lack the ability to upload, classify, tag, and store data in a database, but also lack the ability to provide optimal data by utilizing user emotion recognition. As a result, it is difficult to provide data that meets user needs, and the user experience cannot be improved. In addition, the process of calculating and paying rewards to data providers is often manual and inefficient.

[1605] 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.

[1606] In this invention, the server includes: means for a data provider to upload data using a terminal; means for analyzing, classifying, and tagging the received data and storing it in a database; means for a user to send a data request from the terminal; means for the terminal to acquire the user's emotional data; means for the server to generate data using a generation AI based on the user's request and emotional data and send it to the user's terminal; means for the server to record in a log how the data has been used; and means for the server to tally the number of times each data provider has used the data and calculate and pay a reward. This makes it possible to provide optimal data according to the user's emotions, improving the user experience and the motivation of data providers at the same time.

[1607] "Data Provider" means any person or entity that uploads and provides data through the System.

[1608] "Terminal" means a computer or mobile device used by a User or Data Contributor to access and operate the System.

[1609] "Server" refers to a central computer system that receives, processes, stores, and distributes data.

[1610] A "database" refers to a structured collection of information that allows a server to classify, tag, and efficiently store and manage data.

[1611] "Generative AI" refers to artificial intelligence that extracts necessary data from a database based on a user request and then generates the data in the optimal format based on the user's emotional data.

[1612] An "emotion engine" refers to software or hardware that has the ability to analyze a user's emotional data and provide that emotional state to a generative AI.

[1613] "Log" refers to a file or database that records system activity, such as data usage.

[1614] "Remuneration" refers to monetary compensation paid according to the number of times the data provided by the data provider is used.

[1615] The "temporary folder" refers to a folder for temporarily storing data received by the server.

[1616] "User" means any person or entity accessing the System and making a request for data.

[1617] The present invention relates to a system in which users and data providers process data mutually through terminals, and a server manages and optimizes this. Specific embodiments are described below.

[1618] Data upload by data contributors

[1619] Terminal

[1620] Data providers log in to the system using a terminal. To log in, they use a username and password. After logging in, they access a dedicated upload form. Using this form, they select a data file (e.g., "weatherdata.csv") and enter detailed data information (e.g., category, date, location, etc.). When they press the upload button, the data file and its details are sent to the server.

[1621] Server processing and storage of data

[1622] server

[1623] The server saves the received data files and detailed information in a temporary folder. The server then analyzes the data files and assigns categories and tags based on their content (e.g., "Weather Data," "Temperature," "Humidity," "March 2023," "Tokyo," etc.). Python libraries such as pandas and NumPy can be used for analysis. The classified and tagged data is stored in a database using an RDBMS such as MySQL or PostgreSQL.

[1624] User data requests and emotion data acquisition

[1625] Terminal

[1626] The user accesses the system from their device, inputs the conditions for the required data (e.g., period, location, category, etc.), and sends a request. At the same time, the user's emotional data is acquired. To acquire the emotional data, a text analysis library (e.g., TextBlob) or an emotion recognition sensor is used.

[1627] Server-generated data

[1628] server

[1629] The server receives the user's request and emotion data. The generation AI then searches the database based on the request conditions and extracts matching data. The generation AI can use OpenAI's GPT model, for example. Furthermore, it generates data in a format that is optimal for the user based on the emotion data provided by the emotion engine. For example, if a user requests "average temperature in Tokyo in March 2023" and "relaxed feeling" is detected based on the emotion data, the generation AI will present the data in a format that gives a sense of relaxation.

[1630] Returning data

[1631] server

[1632] The server sends the generated data back to the user's device, which displays, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[1633] Usage tracking and reward calculation

[1634] server

[1635] The server logs details of the data used by the generating AI (such as which data provider's data was used and the number of times it was used). In addition, the server aggregates the number of times each data provider used it for a specific period (e.g., the end of the month) and calculates the reward (e.g., "10 yen per use"). The calculated reward is transferred to the data provider's registered account.

[1636] Specific examples

[1637] 1. Data provider operations

[1638] The researcher uploads "Tokyo weather data for March 2023.csv" from the terminal, enters detailed information such as "weather data," "March 2023," and "Tokyo" into the form, and presses the upload button.

[1639] The server receives the data and detailed information, saves it in a temporary folder, analyzes it, assigns tags such as "weather data," "temperature," "humidity," "March 2023," and "Tokyo," and stores it in a database.

[1640] 2. User Operation

[1641] A weather forecaster requests "average temperature in Tokyo in March 2023." The emotion engine detects "tension" from the user's text.

[1642] The server receives the request and emotion data, and the generation AI searches the database to extract the relevant data.

[1643] The generating AI presents average temperature data in a format that gives a sense of relaxation suitable for users in a state of "tension."

[1644] The server provides the generated results to the user's device, which displays the message, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[1645] This will enable the realization of a system that improves the user experience by incorporating emotion recognition, increases the motivation of data providers, and enables users to quickly and accurately obtain the data they need.

[1646] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1647] Step 1:

[1648] Data provider login

[1649] User: The data provider logs into the system using a terminal. As input, they use their username and password. As output, they are granted access to the system and a dedicated upload form is displayed.

[1650] Step 2:

[1651] Uploading data

[1652] User: The data provider selects a data file in the upload form and enters detailed information (category, date, location, etc.). The input is the data file (e.g., "weatherdata.csv") and the detailed information. The output is the data file and the detailed information sent from the terminal to the server.

[1653] Step 3:

[1654] Receiving and temporarily storing data

[1655] Server: Receives data files and detailed information and saves them in a temporary folder. As input, it receives data files and detailed information sent from the device. As output, it saves the data files in a temporary folder.

[1656] Step 4:

[1657] Data analysis and tagging

[1658] Server: Analyzes data files saved in a temporary folder. The input is the saved data file. Specifically, the data file is analyzed using Python libraries such as pandas and NumPy. As output, categories and tags (e.g., "weather data," "temperature," "humidity," "March 2023," "Tokyo," etc.) are assigned based on the content of the data.

[1659] Step 5:

[1660] Storage in the database

[1661] Server: Stores the classified and tagged data in an RDBMS such as MySQL or PostgreSQL. The input is a tagged data file. The specific operation is to store the data in the database using SQL queries. The output is structured data stored in the database.

[1662] Step 6:

[1663] Sending a Data Request

[1664] User: The user inputs the conditions for the required data and submits a request. As input, there are the conditions for the required data (e.g., period, location, category, etc.). As output, the request is sent to the server. At the same time, emotion data is obtained.

[1665] Step 7:

[1666] Acquiring emotion data

[1667] Device: Obtain user emotional data using a text analysis library (e.g., TextBlob) or an emotion recognition sensor. Inputs include user text information and sensor data. Outputs include analyzed emotional data.

[1668] Step 8:

[1669] Receiving requests and emotion data

[1670] Server: Receives user request data and emotion data. As input, there is the request data and emotion data sent by the user. As output, the received data is passed to the generation AI.

[1671] Step 9:

[1672] Data retrieval and generation

[1673] Server: The generation AI searches the database based on the request conditions and emotion data, and extracts matching data. The inputs are the request conditions and emotion data. The specific operation is to search the database using the generation AI (e.g., OpenAI's GPT model). The output is the extraction of matching data.

[1674] Step 10:

[1675] Optimal Data Generation

[1676] Server: The generation AI generates the data in the optimal data format for the user based on the emotion data provided by the emotion engine. The inputs are emotion data and extracted data. Specifically, the generation AI creates a data format that matches the emotion. The output is optimized data.

[1677] Step 11:

[1678] Returning data

[1679] Server: Returns the generated data to the user's device. The input is the optimized data. The output is the data displayed on the user's device. The user's device displays, "The average temperature in Tokyo in March 2023 will be 15 degrees."

[1680] Step 12:

[1681] Usage record

[1682] Server: Records details of the data used by the generating AI in a log. The input is the data used and its details. Specific operations include recording operations to log files and databases. The output is a record of usage.

[1683] Step 13:

[1684] Reward calculation and payment

[1685] Server: Aggregates the number of uses by each data provider for a specific period and calculates rewards. The input is usage data from the log. Specific operations include aggregation and reward calculation. The output is the calculated reward being deposited into the data provider's registered account.

[1686] (Application example 2)

[1687] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1688] Conventional data provision systems have the problem that the methods for utilizing data uploaded by data providers and the management of its usage status are complicated, and the data provided to users is simply in response to requests, making it difficult to provide optimal information according to the user's emotional state.In particular, autonomous vehicles are required to properly recognize the driver's emotions and provide optimal navigation and assistance based on those emotions, but conventional systems are not able to adequately address this issue either.

[1689] 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: a means for a data provider to upload data using a terminal; a means for the server to classify and tag the received data and store it in a database; a means for a user to send a data request from a terminal; a means for the server to generate data using a generation AI based on the user's request and send it to the user's terminal; a means for the server to log the usage status of the data used by the server; a means for the server to tally the number of uses for each data provider and calculate and pay a reward; a means for the server to analyze the driver's emotional data and apply it to the navigation system; and a means for the navigation system to generate an optimal route based on the emotional data and present it to the driver. This makes it easier for data providers to understand how their data is being used, and enables users to receive optimal data and navigation according to their emotional state.

[1690] "Data Provider" means an individual or entity that generates and uploads data to the system.

[1691] "Terminal" refers to a device used by data providers and users to upload data, send requests, etc.

[1692] A "server" is a computer that is the core of the system and is a device that performs various processes such as receiving, classifying, storing, generating, and logging data.

[1693] "Generative AI" is artificial intelligence that generates data based on user requests and sends it to the user's device.

[1694] A "database" is a storage device where the server stores classified and tagged data.

[1695] "Emotion data" is information indicating the emotional state of the driver analyzed based on data such as facial expressions, voice, and heart rate.

[1696] A "navigation system" is a system that generates an optimal route based on the driver's emotional data and presents it to the driver.

[1697] A "request" is a request made by a user to the system through a terminal for specific data or navigation information.

[1698] "Usage status" is information indicating the usage history and frequency of data generated by the server using the generated AI.

[1699] "Remuneration" means monetary compensation received by a data provider for the use of their data.

[1700] A "log" is data that records a series of operations and data usage status performed on a server.

[1701] A "prompt" is text data containing instructions or questions that are input into a generative AI model.

[1702] A "temporary folder" is an area where the server temporarily stores data received.

[1703] This invention provides an emotion-responsive navigation system for autonomous vehicles. This system provides optimal navigation according to the emotional state of the driver of the autonomous vehicle, realizing a comfortable driving environment.

[1704] First, the system uses the following hardware and software:

[1705] Hardware:

[1706] Internal camera (for driver facial expression recognition)

[1707] Various sensors (heart rate sensor, voice recognition microphone, etc.)

[1708] In-vehicle display

[1709] server

[1710] software:

[1711] Emotion recognition engine: Emotional AI SDK

[1712] Generative AI model: OpenAI GPT-4

[1713] Cloud database: AWS RDS

[1714] Navigation data: Google Maps API

[1715] When the driver gets into the vehicle, the internal camera and sensors collect emotional data such as facial expressions, voice, and heart rate in real time, which are then transmitted to the server via the terminal.

[1716] The server analyzes the received emotion data and identifies the driver's current emotional state using the Emotional AI SDK. Identified emotional states include "stress," "fatigue," and "relaxation."

[1717] The driver requests a destination and navigation conditions through the in-car display or voice assistant, and emotion data is sent to the server at the same time.

[1718] The server generates optimal navigation data based on the received request and emotion data using a generative AI model, OpenAI GPT-4, and Google Maps API to obtain the necessary navigation data.

[1719] For example, if a driver is in a "stressed" state, the generative AI model will select a scenic route that avoids crowds and encourages relaxation. If a driver is feeling "fatigued," the model will recommend a route with rest areas and toilets.

[1720] The generated navigation data is displayed on the driver's in-vehicle display, allowing the driver to receive navigation information optimized for their emotional state.

[1721] For example, a sample prompt for a generative AI model might look like this:

[1722] "My emotional state is 'stressed'. I will be driving from Tokyo to Yokohama at 1:00 PM on October 20, 2023. Please advise me of a relaxing route that avoids crowds."

[1723] This system allows drivers to relax even in stressful driving situations, resulting in an overall more comfortable driving experience.The invention also takes into consideration the interests of data providers, automatically calculating and paying compensation for data used based on how the data is used.

[1724] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1725] Step 1:

[1726] The server collects the driver's emotional data in real time from the internal camera and various sensors. Specifically, the camera recognizes facial expressions, and the sensors acquire heart rate and voice data. Based on this input data, the Emotional AI SDK is used to analyze the driver's emotional state and output it as emotional data.

[1727] Step 2:

[1728] The device receives a navigation request from the driver. The driver inputs the destination and navigation conditions through the display or voice assistant. For example, a condition such as "I want to avoid congestion" is specified. The request data is sent to the server.

[1729] Step 3:

[1730] The server receives the emotion data obtained in step 1 and the request data obtained in step 2. Based on this data, it processes the input using a generative AI model (OpenAI GPT-4) and generates appropriate navigation data. This generation process takes into account relaxing routes and routes that avoid congestion.

[1731] Step 4:

[1732] The server then connects the generated navigation data to the Google Maps API to obtain more detailed route information. This completes the final navigation data, which is then ready to be provided to the driver. The output data is optimized route information.

[1733] Step 5:

[1734] The generated navigation data is sent to the driver's device, and route information is displayed on the in-vehicle display for the driver to review. In addition, the voice assistant provides navigation guidance, improving driving safety and comfort.

[1735] Step 6:

[1736] The server records the usage of navigation data in a log. This logs information such as how much data from which data provider was used and details of the data used by the generating AI. This accumulates basic data for subsequent reward calculations.

[1737] Step 7:

[1738] The server tally up the number of times data providers have used the service for a specific period, such as the end of the month. It calculates how much data from each data provider has been used and calculates rewards based on that. The calculated reward data is then transferred to the data provider's registered account.

[1739] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1740] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1741] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1742] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1743] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1744] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1745] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1746] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1747] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1748] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1749] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1750] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1751] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1752] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1753] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1754] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1755] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1756] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1757] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1758] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1759] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1760] The following is further disclosed regarding the above embodiment.

[1761] (Claim 1)

[1762] A means for a data provider to upload data using a terminal;

[1763] means for classifying and tagging the data received by the server and storing it in a database;

[1764] A means for a user to send a data request from a terminal;

[1765] A means for the server to generate data using a generation AI based on a user request and transmit the data to the user terminal;

[1766] a means for logging usage of the data used by the server;

[1767] The server counts the number of uses for each data provider, calculates the reward, and pays it;

[1768] A system including:

[1769] (Claim 2)

[1770] The system of claim 1, further comprising means for the server to search for data in the database, and for the generation AI to analyze the data based on the search results and generate the required data.

[1771] (Claim 3)

[1772] 2. The system according to claim 1, wherein the server includes means for transferring a reward to the registered account of the data provider at specific intervals.

[1773] "Example 1"

[1774] (Claim 1)

[1775] A means for a data provider to upload data using a terminal;

[1776] A means for classifying and tagging the data received by the server and storing the data in an information storage device;

[1777] A means for a user to send a data request from a terminal;

[1778] A means for the server to generate data using a generative AI model based on a user request and transmit the data to the user terminal;

[1779] a means for recording the usage status of the data used by the server on a recording medium;

[1780] The server counts the number of uses for each data provider, calculates the reward, and pays it;

[1781] A system including:

[1782] (Claim 2)

[1783] The system of claim 1, further comprising means for the server to search for data in an information storage device, and for the generative AI model to analyze the data based on the search results and generate the required data.

[1784] (Claim 3)

[1785] 2. The system according to claim 1, wherein the server includes means for transferring a reward to the registered account of the data provider at specific intervals.

[1786] "Application Example 1"

[1787] (Claim 1)

[1788] A means for a data provider to upload data using a terminal;

[1789] means for classifying and tagging the data received by the server and storing it in a database;

[1790] A means for a user to send a data request from a terminal;

[1791] A means for the server to generate data using a generation AI based on a user request and transmit the data to the user terminal;

[1792] a means for logging usage of the data used by the server;

[1793] The server counts the number of uses for each data provider, calculates the reward, and pays it;

[1794] A means for the robot to collect data in real time according to the manufacturing process and upload it to a server;

[1795] a means for the robot to request data required for a particular manufacturing process from the server;

[1796] A system including:

[1797] (Claim 2)

[1798] The system of claim 1, further comprising means for the server to search for data in the database, and for the generation AI to analyze the data based on the search results and generate the required data.

[1799] (Claim 3)

[1800] 2. The system according to claim 1, wherein the server includes means for transferring a reward to the registered account of the data provider at specific intervals.

[1801] "Example 2: Combining Emotion Engines"

[1802] (Claim 1)

[1803] A means for a data provider to upload data using a terminal;

[1804] A means for analyzing, classifying, and tagging the data received by the server and storing the data in a database;

[1805] A means for a user to send a data request from a terminal;

[1806] A means for the terminal to acquire user emotion data;

[1807] A means for the server to generate data using a generation AI based on the user's request and emotion data and transmit the data to the user's terminal;

[1808] a means for logging usage of the data used by the server;

[1809] The server counts the number of uses for each data provider, calculates the reward, and pays it;

[1810] A system including:

[1811] (Claim 2)

[1812] The system of claim 1, further comprising means for the server to search for data in the database, and for the generation AI to analyze the data based on the search results and emotion data, and generate the required data in the optimal data format.

[1813] (Claim 3)

[1814] 2. The system according to claim 1, wherein the server includes means for transferring a reward to the registered account of the data provider at specific intervals.

[1815] "Application example 2 when combining emotion engines"

[1816] (Claim 1)

[1817] A means for a data provider to upload data using a terminal;

[1818] means for classifying and tagging the data received by the server and storing it in a database;

[1819] A means for a user to send a data request from a terminal;

[1820] A means for the server to generate data using a generation AI based on a user request and transmit the data to the user terminal;

[1821] a means for logging usage of the data used by the server;

[1822] The server counts the number of uses for each data provider, calculates the reward, and pays it;

[1823] A means for the server to analyze the driver's emotion data and apply it to a navigation system;

[1824] a means for the navigation system to generate an optimal route based on the emotion data and present the route to the driver;

[1825] A system including:

[1826] (Claim 2)

[1827] The server searches for data in the database, and based on the search results, the AI ​​analyzes the data and generates the necessary data.

[1828] A means for the navigation system to generate a route that provides a driver with a sense of relaxation and stress reduction in accordance with the emotional data;

[1829] 10. The system of claim 1, comprising:

[1830] (Claim 3)

[1831] A means for the server to transfer the reward to the registered account of the data provider at specific intervals;

[1832] a means for the navigation system to input a prompt sentence to the generative AI model;

[1833] 10. The system of claim 1, comprising: [Explanation of symbols]

[1834] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for a data provider to upload data using a terminal; means for classifying and tagging the data received by the server and storing it in a database; A means for a user to send a data request from a terminal; A means for the server to generate data using a generation AI based on a user request and transmit the data to the user terminal; a means for logging usage of the data used by the server; The server counts the number of uses for each data provider, calculates the reward, and pays it; A system including:

2. 2. The system according to claim 1, further comprising means for the server to search for data in the database, and for the generation AI to analyze the data based on the search results and generate the required data.

3. 2. The system according to claim 1, wherein the server includes means for transferring the reward to the registered account of the data provider at specific intervals.

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