system

Abstract

We provide the system. [Solution] A means for registering multiple artificial intelligence devices operating on a network and assigning a specific role to each device, A means for analyzing pre-processed input data to generate tasks necessary for processing, and for subdividing and assigning each task to the artificial intelligence device, A means for sharing and integrating results in an encrypted format to ensure the integrity and transparency of data generated between the artificial intelligence devices, and for making decisions. A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the scenarios that require big data processing and real-time decision-making have been increasing. However, in a centralized processing system, problems such as processing delays and bottlenecks caused by an increase in data flow, and failure to meet high security requirements have become prominent. These factors hinder the scalability and efficiency required to process large-scale data quickly and safely. Furthermore, when it is necessary to process a plurality of different tasks simultaneously, there is also a problem that it is difficult to adjust between tasks with conventional methods, and the overall processing efficiency decreases.

Means for Solving the Problems

[0005] This invention provides a system in which multiple artificial intelligence (AI) devices operate collaboratively over a network. By assigning specific roles to each device and performing data processing accordingly, scalable processing that can handle increasing data traffic is achieved. Furthermore, data integrity and security are ensured by encrypting and sharing each generated task using blockchain technology. As a result, each AI device can perform tasks independently yet collaboratively, enabling rapid and efficient decision-making. In addition, integrated processing results are provided to the user in the form of dashboards and reports, making it easy to utilize the obtained data.

[0006] A "network" is an infrastructure for communicating data between multiple devices and systems.

[0007] An "artificial intelligence device" is a program or device that has the ability to autonomously process specific tasks using machine learning and data analysis techniques.

[0008] A "task" is a specific unit of work set up to achieve a particular objective in data processing or analysis.

[0009] Blockchain technology is an encryption technology used to securely record transaction data on a decentralized network and prevent tampering.

[0010] "Scalability" refers to the ability of a system or application to expand in response to increasing load, enabling it to handle large-scale processing while maintaining efficiency.

[0011] "Coordination" refers to the process where multiple devices or programs share information and work together to achieve a common goal.

[0012] "Data integrity" refers to the property of ensuring that data remains consistent, unchanged, and accurate. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

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

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

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

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

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides specific methods for improving the efficiency of data processing and decision-making in distributed artificial intelligence systems. The configuration and operation of this system are outlined below.

[0035] This system consists of a series of artificial intelligence (AI) devices placed on a network, with each device assigned a specific task. A server functions as the central hub of the entire system, managing and controlling each terminal and AI device. The terminals are responsible for receiving data input from users and collecting data from external devices, then transmitting this data to the server.

[0036] 1. Task allocation

[0037] The server analyzes the data sent from the terminal and generates a task list based on it. This task list varies depending on the nature and purpose of the data. For example, in the case of text data, it may include sentiment analysis and summarization by a natural language processing unit.

[0038] 2. Data Sharing

[0039] Artificial intelligence devices share information with other devices through a decentralized blockchain network to perform their specialized tasks. This ensures data security and integrity.

[0040] 3. Decision-making process

[0041] Once all processing is complete, the server integrates the results from each artificial intelligence device and makes a final decision. During this process, users can monitor the progress in real time.

[0042] 4. Output of results

[0043] The server then outputs the integrated final results in a user-friendly format, such as a visual dashboard or a PDF report.

[0044] To give a specific example, in a financial data analysis scenario, a user inputs a large dataset of market trends via a terminal. This data is received by a server and assigned to different artificial intelligence (AI) devices to perform price trend predictions and risk assessments. After each device analyzes and shares its results, the server automatically generates an overall market strategy and reports it to the user.

[0045] In this way, the present invention enables the rapid and secure execution of large-scale and complex data processing, facilitating valuable decision-making for users.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The terminal receives input data provided by the user. The received data is preprocessed; for example, text data is tokenized and unnecessary information is removed. This preprocessed data is then optimized for subsequent processing.

[0049] Step 2:

[0050] The server analyzes the preprocessed data sent from the terminal and creates a processing request based on a specific task. This request includes the task on which the data should be processed (e.g., image classification, natural language processing, etc.).

[0051] Step 3:

[0052] The server assigns each processing request it creates to the appropriate artificial intelligence device. For example, image data is assigned to an image recognition device, and audio data is assigned to an audio analysis device.

[0053] Step 4:

[0054] Each artificial intelligence device processes data according to its assigned task, which includes running a model and generating results. The processed results are temporarily stored within the device.

[0055] Step 5:

[0056] Information necessary for artificial intelligence devices will be shared via blockchain. This sharing will utilize encryption technology to ensure the integrity and transparency of processing results.

[0057] Step 6:

[0058] The server integrates the processing results collected from each artificial intelligence device and performs the final decision-making process. The integrated data forms the basis for presenting the optimal solution to the user.

[0059] Step 7:

[0060] The user receives the final processing results from the server. These results are presented in a user-friendly format as dashboards and reports, providing information to help the user decide on the next course of action as needed.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] In the wide-ranging information processing using distributed intelligent devices, the challenge lies in providing results in an easily understandable format for users while ensuring improved processing efficiency and consistency of results. In particular, there is a need for real-time monitoring of data progress, secure data transmission, and highly transparent integration of results.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for registering a number of intelligent devices operating on a network and assigning specific functions to each device; means for analyzing input information to generate tasks necessary for processing, subdividing and assigning each task to the intelligent devices; and means for sharing and integrating the results in an encrypted format to perform decision evaluation. This makes it possible to provide transparent and consistent results while increasing the efficiency of information processing.

[0066] A "network" is a communication infrastructure in which multiple electronic devices are interconnected to send and receive information.

[0067] An "intelligent device" is a mechanical system that can perform specific tasks using artificial intelligence.

[0068] A "function" is a specific task or role that an intelligent device can perform.

[0069] "Information" refers to content that is stored as data and is subject to processing and analysis.

[0070] "Work" refers to specific tasks or processes in information processing.

[0071] "Consistency" refers to a state in which information is coherent and free from contradictions or errors.

[0072] "Transparency" refers to a state where data processing and decision-making processes are clear and traceable.

[0073] "Decision evaluation" refers to making the optimal decision based on results obtained from multiple intelligent devices.

[0074] A "visualization screen" is an interface for displaying processing results in real time.

[0075] "Document format" refers to a document that has been formally formatted as an output, such as a report or a written document.

[0076] "Distributed ledger technology" refers to a system that uses technologies like blockchain to distribute and store information in a distributed manner.

[0077] This invention is a distributed, advanced information processing system, primarily consisting of multiple intelligent devices operating on a network, and servers and terminals that integrate them. Its details are described below.

[0078] The server first receives and analyzes information entered from the terminal. The terminal provides an interface for the user to input market trends and other data. At this stage, intuitive operation is possible using a GUI. Furthermore, this system can use common computing devices such as smartphones and tablets as terminals.

[0079] The server analyzes the received information and assigns tasks to intelligent devices according to their specialized functions. In this process, the server performs advanced data calculations using generative AI models. For example, specific software for natural language processing or systems incorporating machine learning algorithms are used.

[0080] The analyzed information and tasks are securely shared among intelligent devices using distributed ledger technology, such as blockchain. This ensures transparency and integrity of the information. Each intelligent device independently performs its assigned task and sends the results back to the server.

[0081] Finally, the server performs an evaluation based on the aggregated data and provides the results to the user in the form of a visualization screen or document. The visual dashboard allows users to intuitively understand the information. A PDF report is also generated, providing more detailed information.

[0082] As a concrete example, when a user inputs financial market trend data into a terminal, that data is analyzed on a server, and tasks such as price trend analysis and risk assessment are assigned to different intelligent devices. Once each device completes its task, the results are aggregated on the server, and an overall market strategy is generated. This result is immediately displayed on a visual dashboard, making it easily accessible to the user.

[0083] An example of a prompt message might be, "Enter market trend data, analyze the current price trend, and generate a risk assessment report." The system operates according to this prompt message, enabling a quick response to user requests.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The terminal receives market trend data from users. Users can easily input data using the terminal's GUI. This input data includes, for example, information on past stock prices and trading volume. The terminal structures this information and prepares it for transmission to the server.

[0087] Step 2:

[0088] The terminal sends the received data to the server. During this process, the data is encrypted using the SSL / TLS protocol and sent securely to the server. The server receives this encrypted data, decrypts it, and prepares it for analysis.

[0089] Step 3:

[0090] The server analyzes the received data. This analysis process uses generative AI models to extract important patterns and trends from the data. If text data is included, natural language processing techniques are used for sentiment analysis and summarization. The analysis results are then assigned as tasks to intelligent devices.

[0091] Step 4:

[0092] Based on the analysis results, the server assigns specific tasks to intelligent devices. These tasks may include predicting price trends or assessing risks. The server then subdivides these tasks and distributes them to each intelligent device.

[0093] Step 5:

[0094] Intelligent devices perform assigned tasks. Each device processes data independently and shares intermediate results with other devices using distributed ledger technology. For example, one device applies a price prediction model, while other devices use that data to perform a risk assessment.

[0095] Step 6:

[0096] The server integrates the processing results from all intelligent devices. The server uses the aggregated data as the basis for the final decision evaluation. This integration process includes the removal of duplicates and verification of the consistency of the results.

[0097] Step 7:

[0098] The server outputs the integrated decision-making evaluation results in the form of a visualization screen or document. For example, it uses a visual dashboard to display information graphically, providing a format that is easy for users to understand. It also sends detailed analysis results to the terminal as a PDF report to notify the user.

[0099] (Application Example 1)

[0100] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0101] In factory manufacturing processes, it is necessary to optimize the operation of multiple intelligent devices and make quick and accurate decisions at each stage. However, existing systems have been inadequate in sharing information between intelligent devices and responding to anomaly detection, leaving challenges in improving production efficiency and safety.

[0102] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0103] In this invention, the server includes means for registering multiple intelligent devices operating on a network and assigning specific roles to each device; means for analyzing pre-processed input information to generate tasks necessary for processing, and subdividing and assigning each task to the intelligent devices; and means for displaying the output results on a display device or in a report format in order to present the judgment results in a format that workers can easily understand. This enables optimization of the entire production process and rapid response in the event of anomaly detection.

[0104] A "network" is a connectivity infrastructure that allows multiple intelligent devices to communicate with each other and share data.

[0105] An "intelligent device" is a device that has a specific role and autonomously processes and analyzes data.

[0106] A "role" refers to the specific task or work assigned to each intelligent device.

[0107] "Pre-processed input information" refers to data that has been formatted in advance for analysis or decision-making purposes.

[0108] "Work" refers to specific tasks or activities performed within a production process.

[0109] A "display device" is a device that visually represents the output results of an intelligent device.

[0110] A "report format" is a document format for organizing, summarizing, and presenting information.

[0111] "Judgment" is the process by which an intelligent system derives the optimal solution based on the results of its analysis.

[0112] "Sharing technology" refers to technologies for securely and efficiently exchanging data and information between multiple devices.

[0113] "Real-time" refers to processing or updating that occurs instantly without delay.

[0114] The system for realizing this invention aims to improve efficiency in factory manufacturing processes using intelligent devices. A server is connected to a network environment and registers multiple intelligent devices. Each intelligent device is assigned a specific role and performs tasks based on pre-processed input information.

[0115] The system's program is written in Python, and TENSORFLOW® is used for data analysis. Ethereum blockchain technology is utilized to maintain data security and integrity. A server collects data from these intelligent devices in real time and makes decisions to optimize the entire manufacturing process.

[0116] As a concrete example, consider a packaging process within a factory. Each intelligent device is responsible for packaging the product and periodically sends status data to a server. The server detects anomalies based on this data and adjusts the operation of the devices as needed, thereby improving process efficiency.

[0117] Users can check the status of the manufacturing line from a dashboard displayed on a smartphone or tablet. The program sends an alert to the user when an anomaly is detected, prompting a quick response.

[0118] A concrete example of a prompt using a generative AI model is: "Based on data from a specific factory, suggest the optimal way for each robot to optimize the packaging process. Also, consider rapid response measures in case of anomaly detection." Based on this prompt, the AI ​​generates an optimization strategy and provides advice to the user.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The server registers multiple intelligent devices over the network and assigns a specific role to each device. The input requires the ID and role information of each intelligent device, and the output returns a registration completion status. This process clarifies which tasks each device is responsible for.

[0122] Step 2:

[0123] The terminal sends pre-processed input information acquired by sensors from the manufacturing line to the server. The input includes production data from the factory floor, and the output is used for data analysis on the server. The server receives this data and performs data processing according to its role, or executes predictive models using TensorFlow.

[0124] Step 3:

[0125] The server makes decisions based on the data analysis results and subdivides and assigns the most suitable tasks to each intelligent device. Inputs include analysis results and device capability data, and output is the generation of specific work instructions. These instructions are encrypted and transmitted to each device.

[0126] Step 4:

[0127] The intelligent device autonomously processes assigned tasks and returns the results to the server. Inputs include work instructions and on-site situation data, while output is the processing results reported to the server. This processing involves real-time data calculations.

[0128] Step 5:

[0129] The server integrates results from intelligent devices and uses a generative AI model to form a strategy based on prompts. Input includes processing results and prompts from the generative AI model. Output is an optimized strategy proposal, which is visually presented to the user on a dashboard.

[0130] Step 6:

[0131] Users view strategic suggestions and alerts based on anomaly detection through their display devices. Output includes visual information and suggested countermeasures for the user. Based on this, users can take necessary improvements and actions.

[0132] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0133] This invention is a system that combines multiple artificial intelligence devices on a network with an emotion engine that recognizes user emotions. This makes it possible to change task priorities and optimize service delivery based on user input data and interactions. The configuration and operation of this system are outlined below.

[0134] The system primarily consists of a server, terminals, multiple artificial intelligence (AI) devices, and an emotion engine. The server manages and controls the entire system and distributes tasks to each AI device. Terminals receive data input from users and provide information for the emotion engine to analyze emotional states.

[0135] 1. Sentiment analysis

[0136] The device transmits emotions to the emotion engine in real time based on user input data and actions. For example, the tone of text, voice intonation, and changes in facial expressions can serve as input data. The emotion engine uses this information to evaluate the user's current emotional state and quantifies the result.

[0137] 2. Prioritizing tasks

[0138] The server dynamically adjusts task priorities among artificial intelligence devices based on emotional data from the emotion engine. For example, if a user is feeling stressed, the system will present information in a user-friendly way.

[0139] 3. Data Sharing and Processing

[0140] Artificial intelligence devices share data, including emotional data, via blockchain and perform tasks according to their respective roles. This ensures the integrity of processing and the transparency of results.

[0141] 4. Generating and displaying results

[0142] The server integrates the final decision-making results and presents them in a user-friendly format. For example, a visual dashboard might provide customized advice based on changes in emotions.

[0143] To give a concrete example, in an online shopping environment, users search for products they want to buy through their devices. The emotion engine analyzes the user's voice and text to determine their feelings of excitement and indecision. Based on this analysis, the server instructs the artificial intelligence device to prioritize displaying products and related advice that the user is most interested in. This improves the user experience and streamlines the purchasing process.

[0144] In this way, the present invention enables the provision of flexible services that respond to the user's emotions, thereby improving user satisfaction.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] The device receives voice and text input from the user. This data contains cues indicating the user's intentions and emotional state. This input data is sent to the emotion engine in real time.

[0148] Step 2:

[0149] The emotion engine analyzes data received from the device. Using natural language processing techniques to understand the text context and voice analysis to evaluate tone of voice, it determines the user's emotional state. The results are generated as quantified emotion data.

[0150] Step 3:

[0151] The server receives emotional data from the emotion engine and updates the information dashboard. It also takes this emotional data into consideration when assigning tasks to each artificial intelligence device. The order and content of information presentation are adjusted based on the user's emotional state.

[0152] Step 4:

[0153] Each artificial intelligence unit performs tasks received from the server. For example, a product recommendation system might take an approach that encourages users to consider new products if they seem happy, and prioritize providing promotional and support information if the user is unhappy.

[0154] Step 5:

[0155] Information sharing between artificial intelligence devices will be conducted using a blockchain network in an encrypted form. This will guarantee the integrity of the processing and the transparency of the results.

[0156] Step 6:

[0157] The server integrates the processing results from each artificial intelligence device and makes a final decision. This result is presented as customized information to help the user make better choices.

[0158] Step 7:

[0159] Users can visually confirm the final results through the dashboard. Receiving feedback and suggestions based on changes in emotions creates an interactive interface and provides a more enriching user experience.

[0160] (Example 2)

[0161] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0162] Traditional systems provided information without considering user emotions, resulting in limited user satisfaction and experience. Furthermore, insufficient data consistency and transparency of results undermined the overall reliability of the system. Additionally, the uniform nature of information provision made it difficult to flexibly respond to user needs.

[0163] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0164] In this invention, the server includes means for registering multiple information processing devices operating on a network and assigning specific functions to them; means for analyzing user input data to evaluate emotional states and dynamically adjusting processing priorities; and means for sharing and integrating results in encrypted form to ensure data integrity and transparency among information processing devices and to make decisions. This enables the provision of customized information based on user emotions, thereby improving user satisfaction and increasing system reliability.

[0165] A "network" is a system for exchanging information in which multiple terminals or devices are connected so that they can communicate with each other.

[0166] An "information processing device" is a device used for inputting, processing, and outputting data, and mainly refers to computers and servers.

[0167] "Function" refers to the type of operation or process that an information processing device performs to achieve a specific purpose.

[0168] A "user" is an entity that uses a system to input or manipulate data.

[0169] "Input data" refers to text, audio, and other forms of information that users provide to the system.

[0170] "Emotional state" refers to an evaluation result that indicates the user's psychological or emotional condition.

[0171] "Priority" is a ranking system that determines the importance and order in which multiple tasks or processes should be handled.

[0172] "Encrypted format" is a technology that protects privacy and security by converting data into a form that is not easily understood by third parties.

[0173] "Decision-making" is the process by which a system selects the optimal action or output based on the results of its processing.

[0174] "Visual display" refers to providing information graphically in a way that is easy for users to see and understand.

[0175] "Distributed ledger technology" is a technology that stores data on multiple distributed nodes, making it accessible while maintaining consistency, thereby improving reliability.

[0176] This invention realizes an information provision system in which the system dynamically responds while taking into account the user's emotional state. It primarily involves the coordinated operation of a server, terminals, multiple information processing devices, and an emotion evaluation engine.

[0177] Terminal role:

[0178] Users input data via a device in either voice or text format. This input data includes user questions, requests, and feedback. The device sends the input data to a sentiment evaluation engine in real time. This process can utilize speech recognition software or natural language processing technologies. Specific software examples include "Google® Speech-to-Text API" and "Microsoft® Azure® Cognitive Services."

[0179] The role of the emotion evaluation engine:

[0180] The emotion evaluation engine analyzes data received from users and quantifies their emotional state. It utilizes a generative AI model to evaluate emotional tendencies from the intonation of input text and speech. For example, it numerically represents whether the emotion is positive or negative and sends that data to the server.

[0181] Server role:

[0182] The server dynamically adjusts task priorities among the information processing devices used based on the sentiment data it receives. Furthermore, the server encrypts the sentiment data and uses distributed ledger technology (blockchain) to ensure data integrity and transparency among the information processing devices. Technologies such as Apache® Kafka and Hyperledger Fabric are utilized on the server.

[0183] The role of information processing equipment:

[0184] Information processing devices perform specific tasks according to their respective functions. They apply AI models and big data analysis techniques to analyze data and prepare information to be provided to users. Libraries such as TensorFlow and PyTorch can be used to improve the overall efficiency of the system.

[0185] Specific example:

[0186] When a user types "I want a new smartphone" into the device, the emotion evaluation engine recognizes emotions such as excitement and anticipation from the user's tone of voice and text content, and sends an emotion score to the server. The server then instructs the information processing unit to prioritize displaying smartphone options that are most likely to interest the user.

[0187] Example of a prompt:

[0188] When a user types, "I'm looking for running shoes. Do you have any recommendations?", the system responds to their expectations by prompting them with information such as, "Here is a list of running shoes we recommend for you." This prompt is generated using a generative AI model and optimized to the user's emotions.

[0189] In this way, the invented system can flexibly provide services in response to the user's emotions and improve the user experience.

[0190] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0191] Step 1:

[0192] The user provides input data through the device. Input can be in text or voice. The device temporarily stores the input data and prepares to send it to the sentiment evaluation engine in real time. Specifically, if voice input is received, it uses speech recognition technology to convert it into text data.

[0193] Step 2:

[0194] The device sends input data to the sentiment evaluation engine. The sentiment evaluation engine uses a generative AI model to analyze the content of the text and the intonation of the voice, and quantifies the emotional state. For example, if there are many positive words, a high sentiment score is generated. This score is output to the server.

[0195] Step 3:

[0196] The server determines the priority of tasks to be sent to the information processing unit based on the sentiment score received from the sentiment evaluation engine. Based on this score, the server infers what information the user needs next and sets a corresponding priority. Then, it sends the determined priority to the information processing unit.

[0197] Step 4:

[0198] The information processing device performs specific processing based on priority information received from the server. It selects a specific dataset and uses a generative AI model to generate information most relevant to the user. For example, when creating a related product list, it analyzes past purchase data and emotional states. The output results are returned to the server.

[0199] Step 5:

[0200] The server receives the results from the information processing device and verifies them using encryption technology to ensure data integrity and transparency. This process utilizes distributed ledger technology. If the results are deemed correct, the process proceeds to display the dashboard.

[0201] Step 6:

[0202] The terminal presents the user with verified information received from the server. This information is displayed in a visually easy-to-understand format. For example, a visual dashboard displays customized product suggestions and related information, which the user uses to decide on the next steps.

[0203] This series of processes enables the system to provide flexible and personalized information that responds to the user's emotions.

[0204] (Application Example 2)

[0205] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0206] Modern online systems struggle to personalize service delivery based on user emotions and behavior. In particular, the inability to dynamically adjust services based on user emotional states results in a limited user experience. Furthermore, there is a need for efficient data processing methods that maintain data integrity and transparency.

[0207] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0208] In this invention, the server includes means for registering multiple processing units operating on a network and assigning specific roles to each unit; means for analyzing pre-processed input information to generate tasks necessary for processing, and subdividing and allocating each task to the processing units; and means for analyzing the user's emotional state from the input information using an emotion evaluation engine and dynamically adjusting the display order of information based on the analysis results. This enables the provision of personalized services based on the user's emotional state, thereby improving the user experience.

[0209] A "processing device" is a device that operates on a network and has the function of performing a specific role.

[0210] A "role" refers to a specific function or task assigned to a processing unit.

[0211] "Input information" refers to data and actions provided by the user, and is the data necessary for system processing.

[0212] "Business operations" refer to specific tasks or tasks that a system performs using its processing unit.

[0213] An "emotion evaluation engine" is a machine learning-based engine that analyzes the emotional state from user input information and generates the results.

[0214] "To adjust dynamically" means to make flexible changes on the spot according to the situation.

[0215] "Consistency" means that the generated data maintains a consistent and non-contradictory state.

[0216] "Transparency" refers to a state where the operation of a system and data processing are clear and easy to understand.

[0217] A system implementing this invention includes a processing unit operating on a network, an emotion evaluation engine, and a server. The server processes user input information in real time and analyzes the user's emotional state using a generative AI model. The server has the function of dynamically adjusting the display order of information based on the analysis results.

[0218] Specifically, the server receives user voice and text input from a smartphone device and inputs it into an emotion evaluation engine. The emotion evaluation engine analyzes the input information, quantifies the user's emotional state, and provides it to the server. The server uses the analysis results to optimize the order in which information is presented through a generative AI model. In this process, the generated data is encrypted via blockchain technology and shared among processing units while maintaining the integrity and transparency of the results.

[0219] For example, if a user is shopping using an e-commerce app on their smartphone and the emotion evaluation engine detects that the user's voice tone indicates excitement, the server will prioritize displaying trending products and relevant advice based on that analysis. This is an example of a prompt generated by a generative AI model: "This user is currently excited. Please list travel-related products and display the most popular items on the screen."

[0220] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0221] Step 1:

[0222] The device receives voice and text input from the user and transmits that data to the server in real time. The input data includes the user's voice tone and the content of the text.

[0223] Step 2:

[0224] The server sends the received input data to the sentiment evaluation engine. The sentiment evaluation engine analyzes the intonation of the voice data and the content of the text to generate numerical data representing the user's emotional state. This analysis result is then returned to the server.

[0225] Step 3:

[0226] The server uses a generative AI model to optimize the order in which information is presented, based on numerical data of emotional states obtained from the emotion evaluation engine. It takes emotional data as input and outputs a list of products that will interest the user.

[0227] Step 4:

[0228] The optimized product list is transferred to the device in the order set by the server and displayed on the user's screen. This display allows the user to prioritize and see recommended products that match their emotional state.

[0229] Step 5:

[0230] The server encrypts and shares the generated data among other processing units, ensuring consistency and transparency while providing a reliable service. Blockchain technology is used to prevent data tampering during the sharing process.

[0231] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0232] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0233] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0234] [Second Embodiment]

[0235] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0236] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0237] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0238] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0239] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0240] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0241] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0242] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0243] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0244] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0245] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0246] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0247] This invention provides specific methods for improving the efficiency of data processing and decision-making in distributed artificial intelligence systems. The configuration and operation of this system are outlined below.

[0248] This system consists of a series of artificial intelligence (AI) devices placed on a network, with each device assigned a specific task. A server functions as the central hub of the entire system, managing and controlling each terminal and AI device. The terminals are responsible for receiving data input from users and collecting data from external devices, then transmitting this data to the server.

[0249] 1. Task allocation

[0250] The server analyzes the data sent from the terminal and generates a task list based on it. This task list varies depending on the nature and purpose of the data. For example, in the case of text data, it may include sentiment analysis and summarization by a natural language processing unit.

[0251] 2. Data Sharing

[0252] Artificial intelligence devices share information with other devices through a decentralized blockchain network to perform their specialized tasks. This ensures data security and integrity.

[0253] 3. Decision-making process

[0254] Once all processing is complete, the server integrates the results from each artificial intelligence device and makes a final decision. During this process, users can monitor the progress in real time.

[0255] 4. Output of results

[0256] The server generates output in a user-friendly format based on the integrated final results. For example, it may be provided in the form of a visual dashboard or a PDF report.

[0257] To give a specific example, in a financial data analysis scenario, a user inputs a large dataset of market trends via a terminal. This data is received by a server and assigned to different artificial intelligence (AI) devices to perform price trend predictions and risk assessments. After each device analyzes and shares its results, the server automatically generates an overall market strategy and reports it to the user.

[0258] In this way, the present invention enables the rapid and secure execution of large-scale and complex data processing, facilitating valuable decision-making for users.

[0259] The following describes the processing flow.

[0260] Step 1:

[0261] The terminal receives input data provided by the user. The received data is preprocessed; for example, text data is tokenized and unnecessary information is removed. This preprocessed data is then optimized for subsequent processing.

[0262] Step 2:

[0263] The server analyzes the preprocessed data sent from the terminal and creates a processing request based on a specific task. This request includes the task on which the data should be processed (e.g., image classification, natural language processing, etc.).

[0264] Step 3:

[0265] The server assigns each processing request it creates to the appropriate artificial intelligence device. For example, image data is assigned to an image recognition device, and audio data is assigned to an audio analysis device.

[0266] Step 4:

[0267] Each artificial intelligence device processes data according to its assigned task, which includes running a model and generating results. The processed results are temporarily stored within the device.

[0268] Step 5:

[0269] Information necessary for artificial intelligence devices will be shared via blockchain. This sharing will utilize encryption technology to ensure the integrity and transparency of processing results.

[0270] Step 6:

[0271] The server integrates the processing results collected from each artificial intelligence device and performs the final decision-making process. The integrated data forms the basis for presenting the optimal solution to the user.

[0272] Step 7:

[0273] The user receives the final processing results from the server. These results are presented in a user-friendly format as dashboards and reports, providing information to help the user decide on the next course of action as needed.

[0274] (Example 1)

[0275] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] In the wide-ranging information processing using distributed intelligent devices, the challenge lies in providing results in an easily understandable format for users while ensuring improved processing efficiency and consistency of results. In particular, there is a need for real-time monitoring of data progress, secure data transmission, and highly transparent integration of results.

[0277] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0278] In this invention, the server includes means for registering a number of intelligent devices operating on a network and assigning specific functions to each device; means for analyzing input information to generate tasks necessary for processing, subdividing and assigning each task to the intelligent devices; and means for sharing and integrating the results in an encrypted format to perform decision evaluation. This makes it possible to provide transparent and consistent results while increasing the efficiency of information processing.

[0279] A "network" is a communication infrastructure in which multiple electronic devices are interconnected to send and receive information.

[0280] An "intelligent device" is a mechanical system that can perform specific tasks using artificial intelligence.

[0281] A "function" is a specific task or role that an intelligent device can perform.

[0282] "Information" is the content stored as data and targeted for processing and analysis.

[0283] A "task" refers to a specific task or process in information processing.

[0284] "Consistency" refers to a state where the integrity of information is maintained without contradictions or errors.

[0285] "Transparency" refers to a state where data processing and decision-making processes are clear and traceable.

[0286] "Decision evaluation" means making an optimal judgment based on the results obtained from multiple intelligent devices.

[0287] A "visualization screen" is an interface for displaying processing results in real time.

[0288] "Document format" refers to an output formatted as a document, such as a report or a form.

[0289] "Distributed ledger technology" is a system that uses technologies such as blockchain to perform distributed storage of information.

[0290] This invention is a distributed advanced information processing system, mainly composed of multiple intelligent devices operating on a network, a server for integrating them, and terminals. The details will be described below.

[0291] The server first receives and analyzes information entered from the terminal. The terminal provides an interface for the user to input market trends and other data. At this stage, intuitive operation is possible using a GUI. Furthermore, this system can use common computing devices such as smartphones and tablets as terminals.

[0292] The server analyzes the received information and assigns tasks to intelligent devices according to their specialized functions. In this process, the server performs advanced data calculations using generative AI models. For example, specific software for natural language processing or systems incorporating machine learning algorithms are used.

[0293] The analyzed information and tasks are securely shared among intelligent devices using distributed ledger technology, such as blockchain. This ensures transparency and integrity of the information. Each intelligent device independently performs its assigned task and sends the results back to the server.

[0294] Finally, the server performs an evaluation based on the aggregated data and provides the results to the user in the form of a visualization screen or document. The visual dashboard allows users to intuitively understand the information. A PDF report is also generated, providing more detailed information.

[0295] As a concrete example, when a user inputs financial market trend data into a terminal, that data is analyzed on a server, and tasks such as price trend analysis and risk assessment are assigned to different intelligent devices. Once each device completes its task, the results are aggregated on the server, and an overall market strategy is generated. This result is immediately displayed on a visual dashboard, making it easily accessible to the user.

[0296] An example of a prompt message might be, "Enter market trend data, analyze the current price trend, and generate a risk assessment report." The system operates according to this prompt message, enabling a rapid response to user requests.

[0297] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0298] Step 1:

[0299] The terminal receives market trend data from users. Users can easily input data using the terminal's GUI. This input data includes, for example, information on past stock prices and trading volume. The terminal structures this information and prepares it for transmission to the server.

[0300] Step 2:

[0301] The terminal sends the received data to the server. During this process, the data is encrypted using the SSL / TLS protocol and sent securely to the server. The server receives this encrypted data, decrypts it, and prepares it for analysis.

[0302] Step 3:

[0303] The server analyzes the received data. This analysis process uses generative AI models to extract important patterns and trends from the data. If text data is included, natural language processing techniques are used for sentiment analysis and summarization. The analysis results are then assigned as tasks to intelligent devices.

[0304] Step 4:

[0305] Based on the analysis results, the server assigns specific tasks to intelligent devices. These tasks may include predicting price trends or assessing risks. The server then subdivides these tasks and distributes them to each intelligent device.

[0306] Step 5:

[0307] The intelligent device executes the assigned tasks. Each device processes data independently and uses distributed ledger technology to share intermediate results with other devices. For example, one device applies a price prediction model, and another device uses the data to perform risk assessment.

[0308] Step 6:

[0309] The server integrates the processing results from all intelligent devices. The server uses the aggregated data as material for the final decision evaluation. In this integration process, duplicate removal and result consistency verification are performed.

[0310] Step 7:

[0311] The server outputs the integrated decision evaluation results in a visualization screen or document format. For example, use a visual dashboard to display information graphically and provide a format that is easy for users to understand. Also, send the detailed analysis results to the terminal as a PDF report and notify the user.

[0312] (Application Example 1)

[0313] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0314] In the manufacturing process of a factory, it is necessary to optimize the operations of multiple intelligent devices and make quick and accurate decisions at each step. However, in the previous systems, the sharing of information between intelligent devices and the response when detecting abnormalities were insufficient, leaving problems in improving production efficiency and safety.

[0315] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0316] In this invention, the server includes means for registering multiple intelligent devices operating on a network and assigning specific roles to each device; means for analyzing pre-processed input information to generate tasks necessary for processing, and subdividing and assigning each task to the intelligent devices; and means for displaying the output results on a display device or in a report format in order to present the judgment results in a format that workers can easily understand. This enables optimization of the entire production process and rapid response in the event of anomaly detection.

[0317] A "network" is a connectivity infrastructure that allows multiple intelligent devices to communicate with each other and share data.

[0318] An "intelligent device" is a device that has a specific role and autonomously processes and analyzes data.

[0319] A "role" refers to the specific task or work assigned to each intelligent device.

[0320] "Pre-processed input information" refers to data that has been formatted in advance for analysis or decision-making purposes.

[0321] "Work" refers to specific tasks or activities performed within a production process.

[0322] A "display device" is a device that visually represents the output results of an intelligent device.

[0323] A "report format" is a document format for organizing, summarizing, and presenting information.

[0324] "Judgment" is the process by which an intelligent system derives the optimal solution based on the results of its analysis.

[0325] "Sharing technology" refers to technologies for securely and efficiently exchanging data and information between multiple devices.

[0326] "Real-time" refers to processing or updating that occurs instantly without delay.

[0327] The system for realizing this invention aims to improve efficiency in factory manufacturing processes using intelligent devices. A server is connected to a network environment and registers multiple intelligent devices. Each intelligent device is assigned a specific role and performs tasks based on pre-processed input information.

[0328] The system's program is written in Python, and TensorFlow is used for data analysis. Ethereum blockchain technology is utilized to maintain data security and integrity. A server collects data from these intelligent devices in real time and makes decisions to optimize the entire manufacturing process.

[0329] As a concrete example, consider a packaging process within a factory. Each intelligent device is responsible for packaging the product and periodically sends status data to a server. The server detects anomalies based on this data and adjusts the operation of the devices as needed, thereby improving process efficiency.

[0330] Users can check the status of the manufacturing line from a dashboard displayed on a smartphone or tablet. The program sends an alert to the user when an anomaly is detected, prompting a quick response.

[0331] A concrete example of a prompt using a generative AI model is: "Based on data from a specific factory, suggest the optimal way for each robot to optimize the packaging process. Also, consider rapid response measures in case of anomaly detection." Based on this prompt, the AI ​​generates an optimization strategy and provides advice to the user.

[0332] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0333] Step 1:

[0334] The server registers multiple intelligent devices over the network and assigns a specific role to each device. The input requires the ID and role information of each intelligent device, and the output returns a registration completion status. This process clarifies which tasks each device is responsible for.

[0335] Step 2:

[0336] The terminal sends pre-processed input information acquired by sensors from the manufacturing line to the server. The input includes production data from the factory floor, and the output is used for data analysis on the server. The server receives this data and performs data processing according to its role, or executes predictive models using TensorFlow.

[0337] Step 3:

[0338] The server makes decisions based on the data analysis results and subdivides and assigns the most suitable tasks to each intelligent device. Inputs include analysis results and device capability data, and output is the generation of specific work instructions. These instructions are encrypted and transmitted to each device.

[0339] Step 4:

[0340] The intelligent device autonomously processes assigned tasks and returns the results to the server. Inputs include work instructions and on-site situation data, while output is the processing results reported to the server. This processing involves real-time data calculations.

[0341] Step 5:

[0342] The server integrates results from intelligent devices and uses a generative AI model to form a strategy based on prompts. Input includes processing results and prompts from the generative AI model. Output is an optimized strategy proposal, which is visually presented to the user on a dashboard.

[0343] Step 6:

[0344] Users view strategic suggestions and alerts based on anomaly detection through their display devices. Output includes visual information and suggested countermeasures for the user. Based on this, users can take necessary improvements and actions.

[0345] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0346] This invention is a system that combines multiple artificial intelligence devices on a network with an emotion engine that recognizes user emotions. This makes it possible to change task priorities and optimize service delivery based on user input data and interactions. The configuration and operation of this system are outlined below.

[0347] The system primarily consists of a server, terminals, multiple artificial intelligence (AI) devices, and an emotion engine. The server manages and controls the entire system and distributes tasks to each AI device. Terminals receive data input from users and provide information for the emotion engine to analyze emotional states.

[0348] 1. Sentiment analysis

[0349] The device transmits emotions to the emotion engine in real time based on user input data and actions. For example, the tone of text, voice intonation, and changes in facial expressions can serve as input data. The emotion engine uses this information to evaluate the user's current emotional state and quantifies the result.

[0350] 2. Prioritizing tasks

[0351] The server dynamically adjusts task priorities among artificial intelligence devices based on emotional data from the emotion engine. For example, if a user is feeling stressed, the system will present information in a user-friendly way.

[0352] 3. Data Sharing and Processing

[0353] Artificial intelligence devices share data, including emotional data, via blockchain and perform tasks according to their respective roles. This ensures the integrity of processing and the transparency of results.

[0354] 4. Generating and displaying results

[0355] The server integrates the final decision-making results and presents them in a user-friendly format. For example, a visual dashboard might provide customized advice based on changes in emotions.

[0356] To give a concrete example, in an online shopping environment, users search for products they want to buy through their devices. The emotion engine analyzes the user's voice and text to determine their feelings of excitement and indecision. Based on this analysis, the server instructs the artificial intelligence device to prioritize displaying products and related advice that the user is most interested in. This improves the user experience and streamlines the purchasing process.

[0357] In this way, the present invention enables the provision of flexible services that respond to the user's emotions, thereby improving user satisfaction.

[0358] The following describes the processing flow.

[0359] Step 1:

[0360] The device receives voice and text input from the user. This data contains cues indicating the user's intentions and emotional state. This input data is sent to the emotion engine in real time.

[0361] Step 2:

[0362] The emotion engine analyzes data received from the device. Using natural language processing techniques to understand the text context and voice analysis to evaluate tone of voice, it determines the user's emotional state. The results are generated as quantified emotion data.

[0363] Step 3:

[0364] The server receives emotional data from the emotion engine and updates the information dashboard. It also takes this emotional data into consideration when assigning tasks to each artificial intelligence device. The order and content of information presentation are adjusted based on the user's emotional state.

[0365] Step 4:

[0366] Each artificial intelligence unit performs tasks received from the server. For example, a product recommendation system might take an approach that encourages users to consider new products if they seem happy, and prioritize providing promotional and support information if the user is unhappy.

[0367] Step 5:

[0368] Information sharing between artificial intelligence devices will be conducted using a blockchain network in an encrypted form. This will guarantee the integrity of the processing and the transparency of the results.

[0369] Step 6:

[0370] The server integrates the processing results from each artificial intelligence device and makes a final decision. This result is presented as customized information to help the user make better choices.

[0371] Step 7:

[0372] Users can visually confirm the final results through the dashboard. Receiving feedback and suggestions based on changes in emotions creates an interactive interface and provides a more enriching user experience.

[0373] (Example 2)

[0374] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0375] Traditional systems provided information without considering user emotions, resulting in limited user satisfaction and experience. Furthermore, insufficient data consistency and transparency of results undermined the overall reliability of the system. Additionally, the uniform nature of information provision made it difficult to flexibly respond to user needs.

[0376] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0377] In this invention, the server includes means for registering multiple information processing devices operating on a network and assigning specific functions to them; means for analyzing user input data to evaluate emotional states and dynamically adjusting processing priorities; and means for sharing and integrating results in encrypted form to ensure data integrity and transparency among information processing devices and to make decisions. This enables the provision of customized information based on user emotions, thereby improving user satisfaction and increasing system reliability.

[0378] A "network" is a system for exchanging information in which multiple terminals or devices are connected so that they can communicate with each other.

[0379] An "information processing device" is a device used for inputting, processing, and outputting data, and mainly refers to computers and servers.

[0380] "Function" refers to the type of operation or process that an information processing device performs to achieve a specific purpose.

[0381] A "user" is an entity that uses a system to input or manipulate data.

[0382] "Input data" refers to text, audio, and other forms of information that users provide to the system.

[0383] "Emotional state" refers to an evaluation result that indicates the user's psychological or emotional condition.

[0384] "Priority" is a ranking system that determines the importance and order in which multiple tasks or processes should be handled.

[0385] "Encrypted format" is a technology that protects privacy and security by converting data into a form that is not easily understood by third parties.

[0386] "Decision-making" is the process by which a system selects the optimal action or output based on the results of its processing.

[0387] "Visual display" refers to providing information graphically in a way that is easy for users to see and understand.

[0388] "Distributed ledger technology" is a technology that stores data on multiple distributed nodes, making it accessible while maintaining consistency, thereby improving reliability.

[0389] This invention realizes an information provision system in which the system dynamically responds while taking into account the user's emotional state. It primarily involves the coordinated operation of a server, terminals, multiple information processing devices, and an emotion evaluation engine.

[0390] Terminal role:

[0391] Users input data via their device in either voice or text format. This input data includes user questions, requests, and feedback. The device sends the input data to a sentiment evaluation engine in real time. This process can utilize speech recognition software or natural language processing technologies. Specific software examples include "Google Speech-to-Text API" and "Microsoft Azure Cognitive Services."

[0392] The role of the emotion evaluation engine:

[0393] The emotion evaluation engine analyzes data received from users and quantifies their emotional state. It utilizes a generative AI model to evaluate emotional tendencies from the intonation of input text and speech. For example, it numerically represents whether the emotion is positive or negative and sends that data to the server.

[0394] Server role:

[0395] The server dynamically adjusts task priorities among the information processing devices used based on the sentiment data it receives. Furthermore, the server encrypts the sentiment data and uses distributed ledger technology (blockchain) to ensure data integrity and transparency among the information processing devices. Technologies such as Apache Kafka and Hyperledger Fabric are utilized on the server.

[0396] The role of information processing equipment:

[0397] Information processing devices perform specific tasks according to their respective functions. They apply AI models and big data analysis techniques to analyze data and prepare information to be provided to users. Libraries such as TensorFlow and PyTorch can be used to improve the overall efficiency of the system.

[0398] Specific example:

[0399] When a user types "I want a new smartphone" into the device, the emotion evaluation engine recognizes emotions such as excitement and anticipation from the user's tone of voice and text content, and sends an emotion score to the server. The server then instructs the information processing unit to prioritize displaying smartphone options that are most likely to interest the user.

[0400] Example of a prompt:

[0401] When a user types, "I'm looking for running shoes. Do you have any recommendations?", the system responds to their expectations by prompting them with information such as, "Here is a list of running shoes we recommend for you." This prompt is generated using a generative AI model and optimized to the user's emotions.

[0402] In this way, the invented system can flexibly provide services in response to the user's emotions and improve the user experience.

[0403] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0404] Step 1:

[0405] The user provides input data through the device. Input can be in text or voice. The device temporarily stores the input data and prepares to send it to the sentiment evaluation engine in real time. Specifically, if voice input is received, it uses speech recognition technology to convert it into text data.

[0406] Step 2:

[0407] The device sends input data to the sentiment evaluation engine. The sentiment evaluation engine uses a generative AI model to analyze the content of the text and the intonation of the voice, and quantifies the emotional state. For example, if there are many positive words, a high sentiment score is generated. This score is output to the server.

[0408] Step 3:

[0409] The server determines the priority of tasks to be sent to the information processing unit based on the sentiment score received from the sentiment evaluation engine. Based on this score, the server infers what information the user needs next and sets a corresponding priority. Then, it sends the determined priority to the information processing unit.

[0410] Step 4:

[0411] The information processing device performs specific processing based on priority information received from the server. It selects a specific dataset and uses a generative AI model to generate information most relevant to the user. For example, when creating a related product list, it analyzes past purchase data and emotional states. The output results are returned to the server.

[0412] Step 5:

[0413] The server receives the results from the information processing device and verifies them using encryption technology to ensure data integrity and transparency. This process utilizes distributed ledger technology. If the results are deemed correct, the process proceeds to display the dashboard.

[0414] Step 6:

[0415] The terminal presents the user with verified information received from the server. This information is displayed in a visually easy-to-understand format. For example, a visual dashboard displays customized product suggestions and related information, which the user uses to decide on the next steps.

[0416] This series of processes enables the system to provide flexible and personalized information that responds to the user's emotions.

[0417] (Application Example 2)

[0418] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0419] Modern online systems struggle to personalize service delivery based on user emotions and behavior. In particular, the inability to dynamically adjust services based on user emotional states results in a limited user experience. Furthermore, there is a need for efficient data processing methods that maintain data integrity and transparency.

[0420] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0421] In this invention, the server includes means for registering multiple processing units operating on a network and assigning specific roles to each unit; means for analyzing pre-processed input information to generate tasks necessary for processing, and subdividing and allocating each task to the processing units; and means for analyzing the user's emotional state from the input information using an emotion evaluation engine and dynamically adjusting the display order of information based on the analysis results. This enables the provision of personalized services based on the user's emotional state, thereby improving the user experience.

[0422] A "processing device" is a device that operates on a network and has the function of performing a specific role.

[0423] A "role" refers to a specific function or task assigned to a processing unit.

[0424] "Input information" refers to data and actions provided by the user, and is the data necessary for system processing.

[0425] "Business operations" refer to specific tasks or tasks that a system performs using its processing unit.

[0426] An "emotion evaluation engine" is a machine learning-based engine that analyzes the emotional state from user input information and generates the results.

[0427] "To adjust dynamically" means to make flexible changes on the spot according to the situation.

[0428] "Consistency" means that the generated data maintains a consistent and non-contradictory state.

[0429] "Transparency" refers to a state where the operation of a system and data processing are clear and easy to understand.

[0430] A system implementing this invention includes a processing unit operating on a network, an emotion evaluation engine, and a server. The server processes user input information in real time and analyzes the user's emotional state using a generative AI model. The server has the function of dynamically adjusting the display order of information based on the analysis results.

[0431] Specifically, the server receives user voice and text input from a smartphone device and inputs it into an emotion evaluation engine. The emotion evaluation engine analyzes the input information, quantifies the user's emotional state, and provides it to the server. The server uses the analysis results to optimize the order in which information is presented through a generative AI model. In this process, the generated data is encrypted via blockchain technology and shared among processing units while maintaining the integrity and transparency of the results.

[0432] For example, if a user is shopping using an e-commerce app on their smartphone and the emotion evaluation engine detects that the user's voice tone indicates excitement, the server will prioritize displaying trending products and relevant advice based on that analysis. This is an example of a prompt generated by a generative AI model: "This user is currently excited. Please list travel-related products and display the most popular items on the screen."

[0433] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0434] Step 1:

[0435] The device receives voice and text input from the user and transmits that data to the server in real time. The input data includes the user's voice tone and the content of the text.

[0436] Step 2:

[0437] The server sends the received input data to the sentiment evaluation engine. The sentiment evaluation engine analyzes the intonation of the voice data and the content of the text to generate numerical data representing the user's emotional state. This analysis result is then returned to the server.

[0438] Step 3:

[0439] The server uses a generative AI model to optimize the order in which information is presented, based on numerical data of emotional states obtained from the emotion evaluation engine. It takes emotional data as input and outputs a list of products that will interest the user.

[0440] Step 4:

[0441] The optimized product list is transferred to the device in the order set by the server and displayed on the user's screen. This display allows the user to prioritize and see recommended products that match their emotional state.

[0442] Step 5:

[0443] The server encrypts and shares the generated data among other processing units, ensuring consistency and transparency while providing a reliable service. Blockchain technology is used to prevent data tampering during the sharing process.

[0444] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0445] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0446] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0447] [Third Embodiment]

[0448] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0449] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0450] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0451] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0452] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0453] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0454] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0455] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0456] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0457] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0458] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0459] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0460] This invention provides specific methods for improving the efficiency of data processing and decision-making in distributed artificial intelligence systems. The configuration and operation of this system are outlined below.

[0461] This system consists of a series of artificial intelligence (AI) devices placed on a network, with each device assigned a specific task. A server functions as the central hub of the entire system, managing and controlling each terminal and AI device. The terminals are responsible for receiving data input from users and collecting data from external devices, then transmitting this data to the server.

[0462] 1. Task allocation

[0463] The server analyzes the data sent from the terminal and generates a task list based on it. This task list varies depending on the nature and purpose of the data. For example, in the case of text data, it may include sentiment analysis and summarization by a natural language processing unit.

[0464] 2. Data Sharing

[0465] Artificial intelligence devices share information with other devices through a decentralized blockchain network to perform their specialized tasks. This ensures data security and integrity.

[0466] 3. Decision-making process

[0467] Once all processing is complete, the server integrates the results from each artificial intelligence device and makes a final decision. During this process, users can monitor the progress in real time.

[0468] 4. Output of results

[0469] The server generates output in a user-friendly format based on the integrated final results. For example, it may be provided in the form of a visual dashboard or a PDF report.

[0470] To give a specific example, in a financial data analysis scenario, a user inputs a large dataset of market trends via a terminal. This data is received by a server and assigned to different artificial intelligence (AI) devices to perform price trend predictions and risk assessments. After each device analyzes and shares its results, the server automatically generates an overall market strategy and reports it to the user.

[0471] In this way, the present invention enables the rapid and secure execution of large-scale and complex data processing, facilitating valuable decision-making for users.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] The terminal receives input data provided by the user. The received data is preprocessed; for example, text data is tokenized and unnecessary information is removed. This preprocessed data is then optimized for subsequent processing.

[0475] Step 2:

[0476] The server analyzes the preprocessed data sent from the terminal and creates a processing request based on a specific task. This request includes the task on which the data should be processed (e.g., image classification, natural language processing, etc.).

[0477] Step 3:

[0478] The server assigns each processing request it creates to the appropriate artificial intelligence device. For example, image data is assigned to an image recognition device, and audio data is assigned to an audio analysis device.

[0479] Step 4:

[0480] Each artificial intelligence device processes data according to its assigned task, which includes running a model and generating results. The processed results are temporarily stored within the device.

[0481] Step 5:

[0482] Information necessary for artificial intelligence devices will be shared via blockchain. This sharing will utilize encryption technology to ensure the integrity and transparency of processing results.

[0483] Step 6:

[0484] The server integrates the processing results collected from each artificial intelligence device and performs the final decision-making process. The integrated data forms the basis for presenting the optimal solution to the user.

[0485] Step 7:

[0486] The user receives the final processing results from the server. These results are presented in a user-friendly format as dashboards and reports, providing information to help the user decide on the next course of action as needed.

[0487] (Example 1)

[0488] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0489] In the wide-ranging information processing using distributed intelligent devices, the challenge lies in providing results in an easily understandable format for users while ensuring improved processing efficiency and consistency of results. In particular, there is a need for real-time monitoring of data progress, secure data transmission, and highly transparent integration of results.

[0490] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0491] In this invention, the server includes means for registering a number of intelligent devices operating on a network and assigning specific functions to each device; means for analyzing input information to generate tasks necessary for processing, subdividing and assigning each task to the intelligent devices; and means for sharing and integrating the results in an encrypted format to perform decision evaluation. This makes it possible to provide transparent and consistent results while increasing the efficiency of information processing.

[0492] A "network" is a communication infrastructure in which multiple electronic devices are interconnected to send and receive information.

[0493] An "intelligent device" is a mechanical system that can perform specific tasks using artificial intelligence.

[0494] A "function" is a specific task or role that an intelligent device can perform.

[0495] "Information" refers to content that is stored as data and is subject to processing and analysis.

[0496] "Work" refers to specific tasks or processes in information processing.

[0497] "Consistency" refers to a state in which information is coherent and free from contradictions or errors.

[0498] "Transparency" refers to a state where data processing and decision-making processes are clear and traceable.

[0499] "Decision evaluation" refers to making the optimal decision based on results obtained from multiple intelligent devices.

[0500] A "visualization screen" is an interface for displaying processing results in real time.

[0501] "Document format" refers to a document that has been formally formatted as an output, such as a report or a written document.

[0502] "Distributed ledger technology" refers to a system that uses technologies like blockchain to distribute and store information in a distributed manner.

[0503] This invention is a distributed, advanced information processing system, primarily consisting of multiple intelligent devices operating on a network, and servers and terminals that integrate them. Its details are described below.

[0504] The server first receives and analyzes information entered from the terminal. The terminal provides an interface for the user to input market trends and other data. At this stage, intuitive operation is possible using a GUI. Furthermore, this system can use common computing devices such as smartphones and tablets as terminals.

[0505] The server analyzes the received information and assigns tasks to intelligent devices according to their specialized functions. In this process, the server performs advanced data calculations using generative AI models. For example, specific software for natural language processing or systems incorporating machine learning algorithms are used.

[0506] The analyzed information and tasks are securely shared among intelligent devices using distributed ledger technology, such as blockchain. This ensures transparency and integrity of the information. Each intelligent device independently performs its assigned task and sends the results back to the server.

[0507] Finally, the server performs an evaluation based on the aggregated data and provides the results to the user in the form of a visualization screen or document. The visual dashboard allows users to intuitively understand the information. A PDF report is also generated, providing more detailed information.

[0508] As a concrete example, when a user inputs financial market trend data into a terminal, that data is analyzed on a server, and tasks such as price trend analysis and risk assessment are assigned to different intelligent devices. Once each device completes its task, the results are aggregated on the server, and an overall market strategy is generated. This result is immediately displayed on a visual dashboard, making it easily accessible to the user.

[0509] An example of a prompt message might be, "Enter market trend data, analyze the current price trend, and generate a risk assessment report." The system operates according to this prompt message, enabling a quick response to user requests.

[0510] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0511] Step 1:

[0512] The terminal receives market trend data from users. Users can easily input data using the terminal's GUI. This input data includes, for example, information on past stock prices and trading volume. The terminal structures this information and prepares it for transmission to the server.

[0513] Step 2:

[0514] The terminal sends the received data to the server. During this process, the data is encrypted using the SSL / TLS protocol and sent securely to the server. The server receives this encrypted data, decrypts it, and prepares it for analysis.

[0515] Step 3:

[0516] The server analyzes the received data. This analysis process uses generative AI models to extract important patterns and trends from the data. If text data is included, natural language processing techniques are used for sentiment analysis and summarization. The analysis results are then assigned as tasks to intelligent devices.

[0517] Step 4:

[0518] Based on the analysis results, the server assigns specific tasks to intelligent devices. These tasks may include predicting price trends or assessing risks. The server then subdivides these tasks and distributes them to each intelligent device.

[0519] Step 5:

[0520] Intelligent devices perform assigned tasks. Each device processes data independently and shares intermediate results with other devices using distributed ledger technology. For example, one device applies a price prediction model, while other devices use that data to perform a risk assessment.

[0521] Step 6:

[0522] The server integrates the processing results from all intelligent devices. The server uses the aggregated data as the basis for the final decision evaluation. This integration process includes the removal of duplicates and verification of the consistency of the results.

[0523] Step 7:

[0524] The server outputs the integrated decision-making evaluation results in the form of a visualization screen or document. For example, it uses a visual dashboard to display information graphically, providing a format that is easy for users to understand. It also sends detailed analysis results to the terminal as a PDF report to notify the user.

[0525] (Application Example 1)

[0526] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0527] In factory manufacturing processes, it is necessary to optimize the operation of multiple intelligent devices and make quick and accurate decisions at each stage. However, existing systems have been inadequate in sharing information between intelligent devices and responding to anomaly detection, leaving challenges in improving production efficiency and safety.

[0528] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0529] In this invention, the server includes means for registering multiple intelligent devices operating on a network and assigning specific roles to each device; means for analyzing pre-processed input information to generate tasks necessary for processing, and subdividing and assigning each task to the intelligent devices; and means for displaying the output results on a display device or in a report format in order to present the judgment results in a format that workers can easily understand. This enables optimization of the entire production process and rapid response in the event of anomaly detection.

[0530] A "network" is a connectivity infrastructure that allows multiple intelligent devices to communicate with each other and share data.

[0531] An "intelligent device" is a device that has a specific role and autonomously processes and analyzes data.

[0532] A "role" refers to the specific task or work assigned to each intelligent device.

[0533] "Pre-processed input information" refers to data that has been formatted in advance for analysis or decision-making purposes.

[0534] "Work" refers to specific tasks or activities performed within a production process.

[0535] A "display device" is a device that visually represents the output results of an intelligent device.

[0536] A "report format" is a document format for organizing, summarizing, and presenting information.

[0537] "Judgment" is the process by which an intelligent system derives the optimal solution based on the results of its analysis.

[0538] "Sharing technology" refers to technologies for securely and efficiently exchanging data and information between multiple devices.

[0539] "Real-time" refers to processing or updating that occurs instantly without delay.

[0540] The system for realizing this invention aims to improve efficiency in factory manufacturing processes using intelligent devices. A server is connected to a network environment and registers multiple intelligent devices. Each intelligent device is assigned a specific role and performs tasks based on pre-processed input information.

[0541] The system's program is written in Python, and TensorFlow is used for data analysis. Ethereum blockchain technology is utilized to maintain data security and integrity. A server collects data from these intelligent devices in real time and makes decisions to optimize the entire manufacturing process.

[0542] As a concrete example, consider a packaging process within a factory. Each intelligent device is responsible for packaging the product and periodically sends status data to a server. The server detects anomalies based on this data and adjusts the operation of the devices as needed, thereby improving process efficiency.

[0543] Users can check the status of the manufacturing line from a dashboard displayed on a display device using a smartphone or tablet. The program sends an alert to the user when an anomaly is detected, prompting a quick response.

[0544] A concrete example of a prompt using a generative AI model is: "Based on data from a specific factory, suggest the optimal way for each robot to optimize the packaging process. Also, consider rapid response measures in case of anomaly detection." Based on this prompt, the AI ​​generates an optimization strategy and provides advice to the user.

[0545] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0546] Step 1:

[0547] The server registers multiple intelligent devices over the network and assigns a specific role to each device. The input requires the ID and role information of each intelligent device, and the output returns a registration completion status. This process clarifies which tasks each device is responsible for.

[0548] Step 2:

[0549] The terminal sends pre-processed input information acquired by sensors from the manufacturing line to the server. The input includes production data from the factory floor, and the output is used for data analysis on the server. The server receives this data and performs data processing according to its role, or executes predictive models using TensorFlow.

[0550] Step 3:

[0551] The server makes decisions based on the data analysis results and subdivides and assigns the most suitable tasks to each intelligent device. Inputs include analysis results and device capability data, and output is the generation of specific work instructions. These instructions are encrypted and transmitted to each device.

[0552] Step 4:

[0553] The intelligent device autonomously processes assigned tasks and returns the results to the server. Inputs include work instructions and on-site situation data, while output is the processing results reported to the server. This processing involves real-time data calculations.

[0554] Step 5:

[0555] The server integrates results from intelligent devices and uses a generative AI model to form a strategy based on prompts. Input includes processing results and prompts from the generative AI model. Output is an optimized strategy proposal, which is visually presented to the user on a dashboard.

[0556] Step 6:

[0557] Users view strategic suggestions and alerts based on anomaly detection through their display devices. Output includes visual information and suggested countermeasures for the user. Based on this, users can take necessary improvements and actions.

[0558] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0559] This invention is a system that combines multiple artificial intelligence devices on a network with an emotion engine that recognizes user emotions. This makes it possible to change task priorities and optimize service delivery based on user input data and interactions. The configuration and operation of this system are outlined below.

[0560] The system primarily consists of a server, terminals, multiple artificial intelligence (AI) devices, and an emotion engine. The server manages and controls the entire system and distributes tasks to each AI device. Terminals receive data input from users and provide information for the emotion engine to analyze emotional states.

[0561] 1. Sentiment analysis

[0562] The device transmits emotions to the emotion engine in real time based on user input data and actions. For example, the tone of text, voice intonation, and changes in facial expressions can serve as input data. The emotion engine uses this information to evaluate the user's current emotional state and quantifies the result.

[0563] 2. Prioritizing tasks

[0564] The server dynamically adjusts task priorities among artificial intelligence devices based on emotional data from the emotion engine. For example, if a user is feeling stressed, the system will present information in a user-friendly way.

[0565] 3. Data Sharing and Processing

[0566] Artificial intelligence devices share data, including emotional data, via blockchain and perform tasks according to their respective roles. This ensures the integrity of processing and the transparency of results.

[0567] 4. Generating and displaying results

[0568] The server integrates the final decision-making results and presents them in a user-friendly format. For example, a visual dashboard might provide customized advice based on changes in emotions.

[0569] To give a concrete example, in an online shopping environment, users search for products they want to buy through their devices. The emotion engine analyzes the user's voice and text to determine their feelings of excitement and indecision. Based on this analysis, the server instructs the artificial intelligence device to prioritize displaying products and related advice that the user is most interested in. This improves the user experience and streamlines the purchasing process.

[0570] In this way, the present invention enables the provision of flexible services that respond to the user's emotions, thereby improving user satisfaction.

[0571] The following describes the processing flow.

[0572] Step 1:

[0573] The device receives voice and text input from the user. This data contains cues indicating the user's intentions and emotional state. This input data is sent to the emotion engine in real time.

[0574] Step 2:

[0575] The emotion engine analyzes data received from the device. Using natural language processing techniques to understand the text context and voice analysis to evaluate tone of voice, it determines the user's emotional state. The results are generated as quantified emotion data.

[0576] Step 3:

[0577] The server receives emotional data from the emotion engine and updates the information dashboard. It also takes this emotional data into consideration when assigning tasks to each artificial intelligence device. The order and content of information presentation are adjusted based on the user's emotional state.

[0578] Step 4:

[0579] Each artificial intelligence unit performs tasks received from the server. For example, a product recommendation system might take an approach that encourages users to consider new products if they seem happy, and prioritize providing promotional and support information if the user is unhappy.

[0580] Step 5:

[0581] Information sharing between artificial intelligence devices will be conducted using a blockchain network in an encrypted form. This will guarantee the integrity of the processing and the transparency of the results.

[0582] Step 6:

[0583] The server integrates the processing results from each artificial intelligence device and makes a final decision. This result is presented as customized information to help the user make better choices.

[0584] Step 7:

[0585] Users can visually confirm the final results through the dashboard. Receiving feedback and suggestions based on changes in emotions creates an interactive interface and provides a more enriching user experience.

[0586] (Example 2)

[0587] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0588] Traditional systems provided information without considering user emotions, resulting in limited user satisfaction and experience. Furthermore, insufficient data consistency and transparency of results undermined the overall reliability of the system. Additionally, the uniform nature of information provision made it difficult to flexibly respond to user needs.

[0589] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0590] In this invention, the server includes means for registering multiple information processing devices operating on a network and assigning specific functions to them; means for analyzing user input data to evaluate emotional states and dynamically adjusting processing priorities; and means for sharing and integrating results in encrypted form to ensure data integrity and transparency among information processing devices and to make decisions. This enables the provision of customized information based on user emotions, thereby improving user satisfaction and increasing system reliability.

[0591] A "network" is a system for exchanging information in which multiple terminals or devices are connected so that they can communicate with each other.

[0592] An "information processing device" is a device used for inputting, processing, and outputting data, and mainly refers to computers and servers.

[0593] "Function" refers to the type of operation or process that an information processing device performs to achieve a specific purpose.

[0594] A "user" is an entity that uses a system to input or manipulate data.

[0595] "Input data" refers to text, audio, and other forms of information that users provide to the system.

[0596] "Emotional state" refers to an evaluation result that indicates the user's psychological or emotional condition.

[0597] "Priority" is a ranking system that determines the importance and order in which multiple tasks or processes should be handled.

[0598] "Encrypted format" is a technology that protects privacy and security by converting data into a form that is not easily understood by third parties.

[0599] "Decision-making" is the process by which a system selects the optimal action or output based on the results of its processing.

[0600] "Visual display" refers to providing information graphically in a way that is easy for users to see and understand.

[0601] "Distributed ledger technology" is a technology that stores data on multiple distributed nodes, making it accessible while maintaining consistency, thereby improving reliability.

[0602] This invention realizes an information provision system in which the system dynamically responds while taking into account the user's emotional state. It primarily involves the coordinated operation of a server, terminals, multiple information processing devices, and an emotion evaluation engine.

[0603] Terminal role:

[0604] Users input data via their device in either voice or text format. This input data includes user questions, requests, and feedback. The device sends the input data to a sentiment evaluation engine in real time. This process can utilize speech recognition software or natural language processing technologies. Specific software examples include "Google Speech-to-Text API" and "Microsoft Azure Cognitive Services."

[0605] The role of the emotion evaluation engine:

[0606] The emotion evaluation engine analyzes data received from users and quantifies their emotional state. It utilizes a generative AI model to evaluate emotional tendencies from the intonation of input text and speech. For example, it numerically represents whether the emotion is positive or negative and sends that data to the server.

[0607] Server role:

[0608] The server dynamically adjusts task priorities among the information processing devices used based on the sentiment data it receives. Furthermore, the server encrypts the sentiment data and uses distributed ledger technology (blockchain) to ensure data integrity and transparency among the information processing devices. Technologies such as Apache Kafka and Hyperledger Fabric are utilized on the server.

[0609] The role of information processing equipment:

[0610] Information processing devices perform specific tasks according to their respective functions. They apply AI models and big data analysis techniques to analyze data and prepare information to be provided to users. Libraries such as TensorFlow and PyTorch can be used to improve the overall efficiency of the system.

[0611] Specific example:

[0612] When a user types "I want a new smartphone" into the device, the emotion evaluation engine recognizes emotions such as excitement and anticipation from the user's tone of voice and text content, and sends an emotion score to the server. The server then instructs the information processing unit to prioritize displaying smartphone options that are most likely to interest the user.

[0613] Example of a prompt:

[0614] When a user types, "I'm looking for running shoes. Do you have any recommendations?", the system responds to their expectations by prompting them with information such as, "Here is a list of running shoes we recommend for you." This prompt is generated using a generative AI model and optimized to the user's emotions.

[0615] In this way, the invented system can flexibly provide services in response to the user's emotions and improve the user experience.

[0616] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0617] Step 1:

[0618] The user provides input data through the device. Input can be in text or voice. The device temporarily stores the input data and prepares to send it to the sentiment evaluation engine in real time. Specifically, if voice input is received, it uses speech recognition technology to convert it into text data.

[0619] Step 2:

[0620] The device sends input data to the sentiment evaluation engine. The sentiment evaluation engine uses a generative AI model to analyze the content of the text and the intonation of the voice, and quantifies the emotional state. For example, if there are many positive words, a high sentiment score is generated. This score is output to the server.

[0621] Step 3:

[0622] The server determines the priority of tasks to be sent to the information processing unit based on the sentiment score received from the sentiment evaluation engine. Based on this score, the server infers what information the user needs next and sets a corresponding priority. Then, it sends the determined priority to the information processing unit.

[0623] Step 4:

[0624] The information processing device performs specific processing based on priority information received from the server. It selects a specific dataset and uses a generative AI model to generate information most relevant to the user. For example, when creating a related product list, it analyzes past purchase data and emotional states. The output results are returned to the server.

[0625] Step 5:

[0626] The server receives the results from the information processing device and verifies them using encryption technology to ensure data integrity and transparency. This process utilizes distributed ledger technology. If the results are deemed correct, the process proceeds to display the dashboard.

[0627] Step 6:

[0628] The terminal presents the user with verified information received from the server. This information is displayed in a visually easy-to-understand format. For example, a visual dashboard displays customized product suggestions and related information, which the user uses to decide on the next steps.

[0629] This series of processes enables the system to provide flexible and personalized information that responds to the user's emotions.

[0630] (Application Example 2)

[0631] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0632] Modern online systems struggle to personalize service delivery based on user emotions and behavior. In particular, the inability to dynamically adjust services based on user emotional states results in a limited user experience. Furthermore, there is a need for efficient data processing methods that maintain data integrity and transparency.

[0633] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0634] In this invention, the server includes means for registering multiple processing units operating on a network and assigning specific roles to each unit; means for analyzing pre-processed input information to generate tasks necessary for processing, and subdividing and allocating each task to the processing units; and means for analyzing the user's emotional state from the input information using an emotion evaluation engine and dynamically adjusting the display order of information based on the analysis results. This enables the provision of personalized services based on the user's emotional state, thereby improving the user experience.

[0635] A "processing device" is a device that operates on a network and has the function of performing a specific role.

[0636] A "role" refers to a specific function or task assigned to a processing unit.

[0637] "Input information" refers to data and actions provided by the user, and is the data necessary for system processing.

[0638] "Business operations" refer to specific tasks or tasks that a system performs using its processing unit.

[0639] An "emotion evaluation engine" is a machine learning-based engine that analyzes the emotional state from user input information and generates the results.

[0640] "To adjust dynamically" means to make flexible changes on the spot according to the situation.

[0641] "Consistency" means that the generated data maintains a consistent and non-contradictory state.

[0642] "Transparency" refers to a state where the operation of a system and data processing are clear and easy to understand.

[0643] A system implementing this invention includes a processing unit operating on a network, an emotion evaluation engine, and a server. The server processes user input information in real time and analyzes the user's emotional state using a generative AI model. The server has the function of dynamically adjusting the display order of information based on the analysis results.

[0644] Specifically, the server receives user voice and text input from a smartphone device and inputs it into an emotion evaluation engine. The emotion evaluation engine analyzes the input information, quantifies the user's emotional state, and provides it to the server. The server uses the analysis results to optimize the order in which information is presented through a generative AI model. In this process, the generated data is encrypted via blockchain technology and shared among processing units while maintaining the integrity and transparency of the results.

[0645] For example, if a user is shopping using an e-commerce app on their smartphone and the emotion evaluation engine detects that the user's voice tone indicates excitement, the server will prioritize displaying trending products and relevant advice based on that analysis. This is an example of a prompt generated by a generative AI model: "This user is currently excited. Please list travel-related products and display the most popular items on the screen."

[0646] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0647] Step 1:

[0648] The device receives voice and text input from the user and transmits that data to the server in real time. The input data includes the user's voice tone and the content of the text.

[0649] Step 2:

[0650] The server sends the received input data to the sentiment evaluation engine. The sentiment evaluation engine analyzes the intonation of the voice data and the content of the text to generate numerical data representing the user's emotional state. This analysis result is then returned to the server.

[0651] Step 3:

[0652] The server uses a generative AI model to optimize the order in which information is presented, based on numerical data of emotional states obtained from the emotion evaluation engine. It takes emotional data as input and outputs a list of products that will interest the user.

[0653] Step 4:

[0654] The optimized product list is transferred to the device in the order set by the server and displayed on the user's screen. This display allows the user to prioritize and see recommended products that match their emotional state.

[0655] Step 5:

[0656] The server encrypts and shares the generated data among other processing units, ensuring consistency and transparency while providing a reliable service. Blockchain technology is used to prevent data tampering during the sharing process.

[0657] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0658] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0659] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0660] [Fourth Embodiment]

[0661] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0662] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0663] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0664] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0665] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0666] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0667] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0668] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0669] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0670] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0671] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0672] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0673] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0674] This invention provides specific methods for improving the efficiency of data processing and decision-making in distributed artificial intelligence systems. The configuration and operation of this system are outlined below.

[0675] This system consists of a series of artificial intelligence (AI) devices placed on a network, with each device assigned a specific task. A server functions as the central hub of the entire system, managing and controlling each terminal and AI device. The terminals are responsible for receiving data input from users and collecting data from external devices, then transmitting this data to the server.

[0676] 1. Task allocation

[0677] The server analyzes the data sent from the terminal and generates a task list based on it. This task list varies depending on the nature and purpose of the data. For example, in the case of text data, it may include sentiment analysis and summarization by a natural language processing unit.

[0678] 2. Data Sharing

[0679] Artificial intelligence devices share information with other devices through a decentralized blockchain network to perform their specialized tasks. This ensures data security and integrity.

[0680] 3. Decision-making process

[0681] Once all processing is complete, the server integrates the results from each artificial intelligence device and makes a final decision. During this process, users can monitor the progress in real time.

[0682] 4. Output of results

[0683] The server then outputs the integrated final results in a user-friendly format, such as a visual dashboard or a PDF report.

[0684] To give a specific example, in a financial data analysis scenario, a user inputs a large dataset of market trends via a terminal. This data is received by a server and assigned to different artificial intelligence (AI) devices to perform price trend predictions and risk assessments. After each device analyzes and shares its results, the server automatically generates an overall market strategy and reports it to the user.

[0685] In this way, the present invention enables the rapid and secure execution of large-scale and complex data processing, facilitating valuable decision-making for users.

[0686] The following describes the processing flow.

[0687] Step 1:

[0688] The terminal receives input data provided by the user. The received data is preprocessed; for example, text data is tokenized and unnecessary information is removed. This preprocessed data is then optimized for subsequent processing.

[0689] Step 2:

[0690] The server analyzes the preprocessed data sent from the terminal and creates a processing request based on a specific task. This request includes the task on which the data should be processed (e.g., image classification, natural language processing, etc.).

[0691] Step 3:

[0692] The server assigns each processing request it creates to the appropriate artificial intelligence device. For example, image data is assigned to an image recognition device, and audio data is assigned to an audio analysis device.

[0693] Step 4:

[0694] Each artificial intelligence device processes data according to its assigned task, which includes running a model and generating results. The processed results are temporarily stored within the device.

[0695] Step 5:

[0696] Information necessary for artificial intelligence devices will be shared via blockchain. This sharing will utilize encryption technology to ensure the integrity and transparency of processing results.

[0697] Step 6:

[0698] The server integrates the processing results collected from each artificial intelligence device and performs the final decision-making process. The integrated data forms the basis for presenting the optimal solution to the user.

[0699] Step 7:

[0700] The user receives the final processing results from the server. These results are presented in a user-friendly format as dashboards and reports, providing information to help the user decide on the next course of action as needed.

[0701] (Example 1)

[0702] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0703] In the wide-ranging information processing using distributed intelligent devices, the challenge lies in providing results in an easily understandable format for users while ensuring improved processing efficiency and consistency of results. In particular, there is a need for real-time monitoring of data progress, secure data transmission, and highly transparent integration of results.

[0704] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0705] In this invention, the server includes means for registering a number of intelligent devices operating on a network and assigning specific functions to each device; means for analyzing input information to generate tasks necessary for processing, subdividing and assigning each task to the intelligent devices; and means for sharing and integrating the results in an encrypted format to perform decision evaluation. This makes it possible to provide transparent and consistent results while increasing the efficiency of information processing.

[0706] A "network" is a communication infrastructure in which multiple electronic devices are interconnected to send and receive information.

[0707] An "intelligent device" is a mechanical system that can perform specific tasks using artificial intelligence.

[0708] A "function" is a specific task or role that an intelligent device can perform.

[0709] "Information" refers to content that is stored as data and is subject to processing and analysis.

[0710] "Work" refers to specific tasks or processes in information processing.

[0711] "Consistency" refers to a state in which information is coherent and free from contradictions or errors.

[0712] "Transparency" refers to a state where data processing and decision-making processes are clear and traceable.

[0713] "Decision evaluation" refers to making the optimal decision based on results obtained from multiple intelligent devices.

[0714] A "visualization screen" is an interface for displaying processing results in real time.

[0715] "Document format" refers to a document that has been formally formatted as an output, such as a report or a written document.

[0716] "Distributed ledger technology" refers to a system that uses technologies like blockchain to distribute and store information in a distributed manner.

[0717] This invention is a distributed, advanced information processing system, primarily consisting of multiple intelligent devices operating on a network, and servers and terminals that integrate them. Its details are described below.

[0718] The server first receives and analyzes information entered from the terminal. The terminal provides an interface for the user to input market trends and other data. At this stage, intuitive operation is possible using a GUI. Furthermore, this system can use common computing devices such as smartphones and tablets as terminals.

[0719] The server analyzes the received information and assigns tasks to intelligent devices according to their specialized functions. In this process, the server performs advanced data calculations using generative AI models. For example, specific software for natural language processing or systems incorporating machine learning algorithms are used.

[0720] The analyzed information and tasks are securely shared among intelligent devices using distributed ledger technology, such as blockchain. This ensures transparency and integrity of the information. Each intelligent device independently performs its assigned task and sends the results back to the server.

[0721] Finally, the server performs an evaluation based on the aggregated data and provides the results to the user in the form of a visualization screen or document. The visual dashboard allows users to intuitively understand the information. A PDF report is also generated, providing more detailed information.

[0722] As a concrete example, when a user inputs financial market trend data into a terminal, that data is analyzed on a server, and tasks such as price trend analysis and risk assessment are assigned to different intelligent devices. Once each device completes its task, the results are aggregated on the server, and an overall market strategy is generated. This result is immediately displayed on a visual dashboard, making it easily accessible to the user.

[0723] An example of a prompt message might be, "Enter market trend data, analyze the current price trend, and generate a risk assessment report." The system operates according to this prompt message, enabling a rapid response to user requests.

[0724] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0725] Step 1:

[0726] The terminal receives market trend data from users. Users can easily input data using the terminal's GUI. This input data includes, for example, information on past stock prices and trading volume. The terminal structures this information and prepares it for transmission to the server.

[0727] Step 2:

[0728] The terminal sends the received data to the server. During this process, the data is encrypted using the SSL / TLS protocol and sent securely to the server. The server receives this encrypted data, decrypts it, and prepares it for analysis.

[0729] Step 3:

[0730] The server analyzes the received data. This analysis process uses generative AI models to extract important patterns and trends from the data. If text data is included, natural language processing techniques are used for sentiment analysis and summarization. The analysis results are then assigned as tasks to intelligent devices.

[0731] Step 4:

[0732] Based on the analysis results, the server assigns specific tasks to intelligent devices. These tasks may include predicting price trends or assessing risks. The server then subdivides these tasks and distributes them to each intelligent device.

[0733] Step 5:

[0734] Intelligent devices perform assigned tasks. Each device processes data independently and shares intermediate results with other devices using distributed ledger technology. For example, one device applies a price prediction model, while other devices use that data to perform a risk assessment.

[0735] Step 6:

[0736] The server integrates the processing results from all intelligent devices. The server uses the aggregated data as the basis for the final decision evaluation. This integration process includes the removal of duplicates and verification of the consistency of the results.

[0737] Step 7:

[0738] The server outputs the integrated decision-making evaluation results in the form of a visualization screen or document. For example, it uses a visual dashboard to display information graphically, providing a format that is easy for users to understand. It also sends detailed analysis results to the terminal as a PDF report to notify the user.

[0739] (Application Example 1)

[0740] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0741] In factory manufacturing processes, it is necessary to optimize the operation of multiple intelligent devices and make quick and accurate decisions at each stage. However, existing systems have been inadequate in sharing information between intelligent devices and responding to anomaly detection, leaving challenges in improving production efficiency and safety.

[0742] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0743] In this invention, the server includes means for registering multiple intelligent devices operating on a network and assigning specific roles to each device; means for analyzing pre-processed input information to generate tasks necessary for processing, and subdividing and assigning each task to the intelligent devices; and means for displaying the output results on a display device or in a report format in order to present the judgment results in a format that workers can easily understand. This enables optimization of the entire production process and rapid response in the event of anomaly detection.

[0744] A "network" is a connectivity infrastructure that allows multiple intelligent devices to communicate with each other and share data.

[0745] An "intelligent device" is a device that has a specific role and autonomously processes and analyzes data.

[0746] A "role" refers to the specific task or work assigned to each intelligent device.

[0747] "Pre-processed input information" refers to data that has been formatted in advance for analysis or decision-making purposes.

[0748] "Work" refers to specific tasks or activities performed within a production process.

[0749] A "display device" is a device that visually represents the output results of an intelligent device.

[0750] A "report format" is a document format for organizing, summarizing, and presenting information.

[0751] "Judgment" is the process by which an intelligent system derives the optimal solution based on the results of its analysis.

[0752] "Sharing technology" refers to technologies for securely and efficiently exchanging data and information between multiple devices.

[0753] "Real-time" refers to processing or updating that occurs instantly without delay.

[0754] The system for realizing this invention aims to improve efficiency in factory manufacturing processes using intelligent devices. A server is connected to a network environment and registers multiple intelligent devices. Each intelligent device is assigned a specific role and performs tasks based on pre-processed input information.

[0755] The system's program is written in Python, and TensorFlow is used for data analysis. Ethereum blockchain technology is utilized to maintain data security and integrity. A server collects data from these intelligent devices in real time and makes decisions to optimize the entire manufacturing process.

[0756] As a concrete example, consider a packaging process within a factory. Each intelligent device is responsible for packaging the product and periodically sends status data to a server. The server detects anomalies based on this data and adjusts the operation of the devices as needed, thereby improving process efficiency.

[0757] Users can check the status of the manufacturing line from a dashboard displayed on a smartphone or tablet. The program sends an alert to the user when an anomaly is detected, prompting a quick response.

[0758] A concrete example of a prompt using a generative AI model is: "Based on data from a specific factory, suggest the optimal way for each robot to optimize the packaging process. Also, consider rapid response measures in case of anomaly detection." Based on this prompt, the AI ​​generates an optimization strategy and provides advice to the user.

[0759] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0760] Step 1:

[0761] The server registers multiple intelligent devices over the network and assigns a specific role to each device. The input requires the ID and role information of each intelligent device, and the output returns a registration completion status. This process clarifies which tasks each device is responsible for.

[0762] Step 2:

[0763] The terminal sends pre-processed input information acquired by sensors from the manufacturing line to the server. The input includes production data from the factory floor, and the output is used for data analysis on the server. The server receives this data and performs data processing according to its role, or executes predictive models using TensorFlow.

[0764] Step 3:

[0765] The server makes decisions based on the data analysis results and subdivides and assigns the most suitable tasks to each intelligent device. Inputs include analysis results and device capability data, and output is the generation of specific work instructions. These instructions are encrypted and transmitted to each device.

[0766] Step 4:

[0767] The intelligent device autonomously processes assigned tasks and returns the results to the server. Inputs include work instructions and on-site situation data, while output is the processing results reported to the server. This processing involves real-time data calculations.

[0768] Step 5:

[0769] The server integrates results from intelligent devices and uses a generative AI model to form a strategy based on prompts. Input includes processing results and prompts from the generative AI model. Output is an optimized strategy proposal, which is visually presented to the user on a dashboard.

[0770] Step 6:

[0771] Users view strategic suggestions and alerts based on anomaly detection through their display devices. Output includes visual information and suggested countermeasures for the user. Based on this, users can take necessary improvements and actions.

[0772] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0773] This invention is a system that combines multiple artificial intelligence devices on a network with an emotion engine that recognizes user emotions. This makes it possible to change task priorities and optimize service delivery based on user input data and interactions. The configuration and operation of this system are outlined below.

[0774] The system primarily consists of a server, terminals, multiple artificial intelligence (AI) devices, and an emotion engine. The server manages and controls the entire system and distributes tasks to each AI device. Terminals receive data input from users and provide information for the emotion engine to analyze emotional states.

[0775] 1. Sentiment analysis

[0776] The device transmits emotions to the emotion engine in real time based on user input data and actions. For example, the tone of text, voice intonation, and changes in facial expressions can serve as input data. The emotion engine uses this information to evaluate the user's current emotional state and quantifies the result.

[0777] 2. Prioritizing tasks

[0778] The server dynamically adjusts task priorities among artificial intelligence devices based on emotional data from the emotion engine. For example, if a user is feeling stressed, the system will present information in a user-friendly way.

[0779] 3. Data Sharing and Processing

[0780] Artificial intelligence devices share data, including emotional data, via blockchain and perform tasks according to their respective roles. This ensures the integrity of processing and the transparency of results.

[0781] 4. Generating and displaying results

[0782] The server integrates the final decision-making results and presents them in a user-friendly format. For example, a visual dashboard might provide customized advice based on changes in emotions.

[0783] To give a concrete example, in an online shopping environment, users search for products they want to buy through their devices. The emotion engine analyzes the user's voice and text to determine their feelings of excitement and indecision. Based on this analysis, the server instructs the artificial intelligence device to prioritize displaying products and related advice that the user is most interested in. This improves the user experience and streamlines the purchasing process.

[0784] In this way, the present invention enables the provision of flexible services that respond to the user's emotions, thereby improving user satisfaction.

[0785] The following describes the processing flow.

[0786] Step 1:

[0787] The device receives voice and text input from the user. This data contains cues indicating the user's intentions and emotional state. This input data is sent to the emotion engine in real time.

[0788] Step 2:

[0789] The emotion engine analyzes data received from the device. Using natural language processing techniques to understand the text context and voice analysis to evaluate tone of voice, it determines the user's emotional state. The results are generated as quantified emotion data.

[0790] Step 3:

[0791] The server receives emotional data from the emotion engine and updates the information dashboard. It also takes this emotional data into consideration when assigning tasks to each artificial intelligence device. The order and content of information presentation are adjusted based on the user's emotional state.

[0792] Step 4:

[0793] Each artificial intelligence unit performs tasks received from the server. For example, a product recommendation system might take an approach that encourages users to consider new products if they seem happy, and prioritize providing promotional and support information if the user is unhappy.

[0794] Step 5:

[0795] Information sharing between artificial intelligence devices will be conducted using a blockchain network in an encrypted form. This will guarantee the integrity of the processing and the transparency of the results.

[0796] Step 6:

[0797] The server integrates the processing results from each artificial intelligence device and makes a final decision. This result is presented as customized information to help the user make better choices.

[0798] Step 7:

[0799] Users can visually confirm the final results through the dashboard. Receiving feedback and suggestions based on changes in emotions creates an interactive interface and provides a more enriching user experience.

[0800] (Example 2)

[0801] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0802] Traditional systems provided information without considering user emotions, resulting in limited user satisfaction and experience. Furthermore, insufficient data consistency and transparency of results undermined the overall reliability of the system. Additionally, the uniform nature of information provision made it difficult to flexibly respond to user needs.

[0803] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0804] In this invention, the server includes means for registering multiple information processing devices operating on a network and assigning specific functions to them; means for analyzing user input data to evaluate emotional states and dynamically adjusting processing priorities; and means for sharing and integrating results in encrypted form to ensure data integrity and transparency among information processing devices and to make decisions. This enables the provision of customized information based on user emotions, thereby improving user satisfaction and increasing system reliability.

[0805] A "network" is a system for exchanging information in which multiple terminals or devices are connected so that they can communicate with each other.

[0806] An "information processing device" is a device used for inputting, processing, and outputting data, and mainly refers to computers and servers.

[0807] "Function" refers to the type of operation or process that an information processing device performs to achieve a specific purpose.

[0808] A "user" is an entity that uses a system to input or manipulate data.

[0809] "Input data" refers to text, audio, and other forms of information that users provide to the system.

[0810] "Emotional state" refers to an evaluation result that indicates the user's psychological or emotional condition.

[0811] "Priority" is a ranking system that determines the importance and order in which multiple tasks or processes should be handled.

[0812] "Encrypted format" is a technology that protects privacy and security by converting data into a form that is not easily understood by third parties.

[0813] "Decision-making" is the process by which a system selects the optimal action or output based on the results of its processing.

[0814] "Visual display" refers to providing information graphically in a way that is easy for users to see and understand.

[0815] "Distributed ledger technology" is a technology that stores data on multiple distributed nodes, making it accessible while maintaining consistency, thereby improving reliability.

[0816] This invention realizes an information provision system in which the system dynamically responds while taking into account the user's emotional state. It primarily involves the coordinated operation of a server, terminals, multiple information processing devices, and an emotion evaluation engine.

[0817] Terminal role:

[0818] Users input data via their device in either voice or text format. This input data includes user questions, requests, and feedback. The device sends the input data to a sentiment evaluation engine in real time. This process can utilize speech recognition software or natural language processing technologies. Specific software examples include "Google Speech-to-Text API" and "Microsoft Azure Cognitive Services."

[0819] The role of the emotion evaluation engine:

[0820] The emotion evaluation engine analyzes data received from users and quantifies their emotional state. It utilizes a generative AI model to evaluate emotional tendencies from the intonation of input text and speech. For example, it numerically represents whether the emotion is positive or negative and sends that data to the server.

[0821] Server role:

[0822] The server dynamically adjusts task priorities among the information processing devices used based on the sentiment data it receives. Furthermore, the server encrypts the sentiment data and uses distributed ledger technology (blockchain) to ensure data integrity and transparency among the information processing devices. Technologies such as Apache Kafka and Hyperledger Fabric are utilized on the server.

[0823] The role of information processing equipment:

[0824] Information processing devices perform specific tasks according to their respective functions. They apply AI models and big data analysis techniques to analyze data and prepare information to be provided to users. Libraries such as TensorFlow and PyTorch can be used to improve the overall efficiency of the system.

[0825] Specific example:

[0826] When a user types "I want a new smartphone" into the device, the emotion evaluation engine recognizes emotions such as excitement and anticipation from the user's tone of voice and text content, and sends an emotion score to the server. The server then instructs the information processing unit to prioritize displaying smartphone options that are most likely to interest the user.

[0827] Example of a prompt:

[0828] When a user types, "I'm looking for running shoes. Do you have any recommendations?", the system responds to their expectations by prompting them with information such as, "Here is a list of running shoes we recommend for you." This prompt is generated using a generative AI model and optimized to the user's emotions.

[0829] In this way, the invented system can flexibly provide services in response to the user's emotions and improve the user experience.

[0830] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0831] Step 1:

[0832] The user provides input data through the device. Input can be in text or voice. The device temporarily stores the input data and prepares to send it to the sentiment evaluation engine in real time. Specifically, if voice input is received, it uses speech recognition technology to convert it into text data.

[0833] Step 2:

[0834] The device sends input data to the sentiment evaluation engine. The sentiment evaluation engine uses a generative AI model to analyze the content of the text and the intonation of the voice, and quantifies the emotional state. For example, if there are many positive words, a high sentiment score is generated. This score is output to the server.

[0835] Step 3:

[0836] The server determines the priority of tasks to be sent to the information processing unit based on the sentiment score received from the sentiment evaluation engine. Based on this score, the server infers what information the user needs next and sets a corresponding priority. Then, it sends the determined priority to the information processing unit.

[0837] Step 4:

[0838] The information processing device performs specific processing based on priority information received from the server. It selects a specific dataset and uses a generative AI model to generate information most relevant to the user. For example, when creating a related product list, it analyzes past purchase data and emotional states. The output results are returned to the server.

[0839] Step 5:

[0840] The server receives the results from the information processing device and verifies them using encryption technology to ensure data integrity and transparency. This process utilizes distributed ledger technology. If the results are deemed correct, the process proceeds to display the dashboard.

[0841] Step 6:

[0842] The terminal presents the user with verified information received from the server. This information is displayed in a visually easy-to-understand format. For example, a visual dashboard displays customized product suggestions and related information, which the user uses to decide on the next steps.

[0843] This series of processes enables the system to provide flexible and personalized information that responds to the user's emotions.

[0844] (Application Example 2)

[0845] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0846] Modern online systems struggle to personalize service delivery based on user emotions and behavior. In particular, the inability to dynamically adjust services based on user emotional states results in a limited user experience. Furthermore, there is a need for efficient data processing methods that maintain data integrity and transparency.

[0847] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0848] In this invention, the server includes means for registering multiple processing units operating on a network and assigning specific roles to each unit; means for analyzing pre-processed input information to generate tasks necessary for processing, and subdividing and allocating each task to the processing units; and means for analyzing the user's emotional state from the input information using an emotion evaluation engine and dynamically adjusting the display order of information based on the analysis results. This enables the provision of personalized services based on the user's emotional state, thereby improving the user experience.

[0849] A "processing device" is a device that operates on a network and has the function of performing a specific role.

[0850] A "role" refers to a specific function or task assigned to a processing unit.

[0851] "Input information" refers to data and actions provided by the user, and is the data necessary for system processing.

[0852] "Business operations" refer to specific tasks or tasks that a system performs using its processing unit.

[0853] An "emotion evaluation engine" is a machine learning-based engine that analyzes the emotional state from user input information and generates the results.

[0854] "To adjust dynamically" means to make flexible changes on the spot according to the situation.

[0855] "Consistency" means that the generated data maintains a consistent and non-contradictory state.

[0856] "Transparency" refers to a state where the operation of a system and data processing are clear and easy to understand.

[0857] A system implementing this invention includes a processing unit operating on a network, an emotion evaluation engine, and a server. The server processes user input information in real time and analyzes the user's emotional state using a generative AI model. The server has the function of dynamically adjusting the display order of information based on the analysis results.

[0858] Specifically, the server receives user voice and text input from a smartphone device and inputs it into an emotion evaluation engine. The emotion evaluation engine analyzes the input information, quantifies the user's emotional state, and provides it to the server. The server uses the analysis results to optimize the order in which information is presented through a generative AI model. In this process, the generated data is encrypted via blockchain technology and shared among processing units while maintaining the integrity and transparency of the results.

[0859] For example, if a user is shopping using an e-commerce app on their smartphone and the emotion evaluation engine detects that the user's voice tone indicates excitement, the server will prioritize displaying trending products and relevant advice based on that analysis. This is an example of a prompt generated by a generative AI model: "This user is currently excited. Please list travel-related products and display the most popular items on the screen."

[0860] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0861] Step 1:

[0862] The device receives voice and text input from the user and transmits that data to the server in real time. The input data includes the user's voice tone and the content of the text.

[0863] Step 2:

[0864] The server sends the received input data to the sentiment evaluation engine. The sentiment evaluation engine analyzes the intonation of the voice data and the content of the text to generate numerical data representing the user's emotional state. This analysis result is then returned to the server.

[0865] Step 3:

[0866] The server uses a generative AI model to optimize the order in which information is presented, based on numerical data of emotional states obtained from the emotion evaluation engine. It takes emotional data as input and outputs a list of products that will interest the user.

[0867] Step 4:

[0868] The optimized product list is transferred to the device in the order set by the server and displayed on the user's screen. This display allows the user to prioritize and see recommended products that match their emotional state.

[0869] Step 5:

[0870] The server encrypts and shares the generated data among other processing units, ensuring consistency and transparency while providing a reliable service. Blockchain technology is used to prevent data tampering during the sharing process.

[0871] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0872] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0873] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0874] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0875] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0876] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0877] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0878] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0879] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0880] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0881] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0882] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0883] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0884] 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.

[0885] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0886] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0887] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0888] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0889] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0890] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0891] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0892] The following is further disclosed regarding the embodiments described above.

[0893] (Claim 1)

[0894] A means for registering multiple artificial intelligence devices operating on a network and assigning a specific role to each device,

[0895] A means for analyzing pre-processed input data to generate tasks necessary for processing, and for subdividing and assigning each task to the artificial intelligence device,

[0896] A means for sharing and integrating results in an encrypted format to ensure the integrity and transparency of data generated between the artificial intelligence devices, and for making decisions.

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, wherein each artificial intelligence device processes an independently assigned task and shares the results via blockchain technology.

[0900] (Claim 3)

[0901] The system according to claim 1, which displays the output results in a dashboard or report format in order to present the decision-making results in a format that is easily understandable to the user.

[0902] "Example 1"

[0903] (Claim 1)

[0904] A means for registering a large number of intelligent devices operating on a network and assigning specific functions to each device,

[0905] A means for analyzing the input information to generate the tasks necessary for processing, and for subdividing and assigning each task to the intelligent device,

[0906] In order to ensure the consistency and transparency of information generated between the intelligent devices, means for sharing and integrating results in an encrypted format to perform decision evaluation,

[0907] A means of inputting information through a terminal,

[0908] In order to present the integrated final decision evaluation results in a format that is easy for users to understand, a means of displaying the output results in a visualization screen or document format is provided.

[0909] A means of checking the processing progress in real time,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, wherein each intelligent device independently performs assigned tasks and shares the results via distributed ledger technology.

[0913] (Claim 3)

[0914] The system according to claim 1, comprising means for securely and efficiently transmitting information from a terminal to a server.

[0915] "Application Example 1"

[0916] (Claim 1)

[0917] A means for registering multiple intelligent devices operating on a network and assigning a specific role to each device,

[0918] A means for analyzing pre-processed input information to generate the tasks necessary for processing, and for subdividing and assigning each task to the intelligent device,

[0919] A means for sharing and integrating results in an encrypted format to ensure the integrity and transparency of information generated between the aforementioned intelligent devices,

[0920] In order to present the judgment results in a format that workers can easily understand, means of representing the output results in a display device or report format,

[0921] A means of notifying workers of warnings to encourage a quick response when an anomaly is detected,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, wherein each intelligent device processes an independently assigned task and shares the results via a sharing technology.

[0925] (Claim 3)

[0926] The system according to claim 1, which collects information in real time from all intelligent devices and continuously displays the status of production to workers.

[0927] "Example 2 of combining an emotion engine"

[0928] (Claim 1)

[0929] A means for registering multiple information processing devices operating on a network and assigning specific functions to each device,

[0930] A means for analyzing user input data to evaluate emotional state and dynamically adjusting processing priorities based on the evaluation results,

[0931] In order to ensure the integrity and transparency of the data collected between the aforementioned information processing devices, a means is provided for sharing and integrating results in an encrypted format to make decisions.

[0932] In order to present the aforementioned decision-making results to the user in an appropriate format, means for visually displaying the results,

[0933] A system that includes this.

[0934] (Claim 2)

[0935] The system according to claim 1, wherein each information processing device performs independently assigned processing and shares the results using distributed ledger technology.

[0936] (Claim 3)

[0937] The system according to claim 1, wherein the visually displayed results are customized according to the user's emotional state, and the outputted information is made intuitively understandable to the user.

[0938] "Application example 2 when combining with an emotional engine"

[0939] (Claim 1)

[0940] A means for registering multiple processing devices operating on a network and assigning a specific role to each device,

[0941] A means for analyzing pre-processed input information to generate the tasks necessary for processing, and for subdividing and allocating each task to the processing device,

[0942] A means for sharing and integrating results in an encrypted format to ensure the integrity and transparency of information generated between the aforementioned processing devices, and for making decisions based on that integration,

[0943] A means for analyzing the user's emotional state from input information using an emotion evaluation engine, and dynamically adjusting the display order of information based on the analysis results,

[0944] A system that includes this.

[0945] (Claim 2)

[0946] The system according to claim 1, wherein each processing unit independently processes assigned tasks and shares the results via distributed ledger technology.

[0947] (Claim 3)

[0948] The system according to claim 1, which displays the output results in a visualized interface or report format in order to present the decision-making results in a format that is easily understandable to the user. [Explanation of symbols]

[0949] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for registering multiple artificial intelligence devices operating on a network and assigning a specific role to each device, A means for analyzing pre-processed input data to generate tasks necessary for processing, and for subdividing and assigning each task to the artificial intelligence device, A means for sharing and integrating results in an encrypted format to ensure the integrity and transparency of data generated between the artificial intelligence devices, and for making decisions. A system that includes this.

2. The system according to claim 1, wherein each artificial intelligence device processes an independently assigned task and shares the results via blockchain technology.

3. The system according to claim 1, which displays the output results in a dashboard or report format in order to present the decision-making results in a format that is easily understandable to the user.

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