system

Abstract

We provide the system. [Solution] Means for providing an integrated control surface that aggregates and displays the status and performance of multiple knowledge processing devices, A means for assigning tasks to the plurality of knowledge processing devices using an input device capable of receiving instructions from the user in natural language format, A means for efficiently exchanging and sharing information between the knowledge processing devices using a generation processing device, Means for dynamically allocating and adjusting computing resources based on the workload of the knowledge processing device, A means for aggregating and analyzing data collected from the aforementioned multiple knowledge processing devices and generating a results report, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There are problems that multiple knowledge processing devices operate individually, increasing the management burden, inefficient resource utilization, and duplication and contradictions in work due to lack of information sharing, resulting in a decrease in overall work efficiency. This problem causes an increase in time and cost for users, so a system that effectively solves this and facilitates management is needed.

Means for Solving the Problems

[0005] The present invention provides an integrated control surface that aggregates and displays the status and performance of multiple knowledge processing devices, through which users can input task instructions in natural language format. Furthermore, it optimizes information exchange between knowledge processing devices using a generation processing device, enabling real-time information sharing. This generation processing device also utilizes natural language interpretation technology to convert input instructions into an executable form, effectively assigning tasks to each knowledge processing device. Additionally, it has a function to dynamically adjust computing resources according to the load status of the knowledge processing devices, achieving efficient resource utilization. In this way, the invention provides a system that enables integrated and efficient management, improving the overall efficiency of operations.

[0006] A "knowledge processing device" is a computer program or system designed to perform a specific task, and is a device that performs intelligent work such as data collection, analysis, and generation.

[0007] An "integrated control surface" is a display area that aggregates the status and performance of multiple knowledge processing devices and provides an interface for users to manage and control them.

[0008] A "generation and processing device" is a computer system or program that has the ability to generate and transform information, and in particular, a device that has the function of interpreting task instructions through natural language processing and distributing them to a knowledge processing device.

[0009] "Natural language form" refers to the language form that humans use on a daily basis, and is a form of expression that expresses information based on grammar and vocabulary, rather than a specific programming language.

[0010] "Information exchange" is a concept that refers to the process of transmitting data and knowledge between multiple devices or systems and fostering mutual understanding. [Brief explanation of the drawing]

[0011] [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, when an emotion engine is combined. [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]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, the terms used in the following description will be explained.

[0014] 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.

[0015] 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.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention is a system for efficiently managing and optimally operating multiple knowledge processing devices. This system aggregates and displays the operating status and performance of various knowledge processing devices on an integrated control surface, allowing the user to grasp the overall status at a glance.

[0033] The user inputs the task in natural language format via a terminal. This input instruction is sent from the terminal to the server. The server analyzes this instruction using a generation processing unit and distributes it to each knowledge processing unit in the appropriate format.

[0034] The generation and processing unit interprets user instructions using advanced natural language processing technology. Therefore, users can operate the system using everyday language without needing to use specific programming languages ​​or commands.

[0035] Furthermore, the server facilitates effective information exchange between knowledge processing devices. For example, if a knowledge processing device performing a document review requires the results of data analysis, it automatically retrieves the appropriate information from related knowledge processing devices.

[0036] For example, if a user issues a request such as "Prepare a sales analysis report for the end-of-month meeting," the server will have the schedule management knowledge processing unit check the schedule, instruct the document review knowledge processing unit to prepare the relevant documents, and request the data analysis knowledge processing unit to analyze the sales data. Finally, the server will report to the user that the preparations are complete from an integrated control perspective.

[0037] Thus, this invention provides a configuration that centrally manages the operation of a knowledge processing device and can efficiently issue instructions through natural language. As a result, it is possible to significantly improve the efficiency of business operations.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users input tasks using a terminal in natural language format. For example, they might input instructions such as "Prepare materials for next month's sales meeting."

[0041] Step 2:

[0042] The terminal sends the input instructions to the server. During this process, the instructions are converted into a data format and processed to enable efficient communication.

[0043] Step 3:

[0044] The server passes the received instructions to the generation and processing unit, which then analyzes them using a natural language processing engine. It interprets the tasks that make up the instructions and formulates a specific execution plan.

[0045] Step 4:

[0046] The server assigns tasks to appropriate knowledge processing units based on the analysis results from the generation processing units. It generates individual instructions for each unit and efficiently allocates resources.

[0047] Step 5:

[0048] Each knowledge processing device begins executing its assigned task. For example, a scheduling device checks meeting dates, and a document review device prepares necessary materials.

[0049] Step 6:

[0050] The server periodically collects task progress from each knowledge processing unit. This allows it to monitor whether tasks are progressing properly and make adjustments as needed.

[0051] Step 7:

[0052] The server generates user reports based on the aggregated information. Progress information and results are displayed on the integrated control panel, notifying users when tasks are completed.

[0053] Step 8:

[0054] The user reviews the report displayed on the integrated control panel to confirm that the task was completed correctly. If necessary, they can re-enter additional instructions.

[0055] (Example 1)

[0056] 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."

[0057] In information processing systems, a key challenge is to centrally understand and efficiently manage the status and performance of multiple information processing devices. Furthermore, it is essential that users can easily operate the system using natural language and that effective information exchange takes place between the devices. This necessitates a system that can improve operational efficiency while optimally allocating computing resources.

[0058] 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.

[0059] In this invention, the server includes means for providing an integrated control screen that aggregates and displays the status and performance of multiple information processing devices, means for utilizing a generative AI model to analyze user instructions in natural language and convert them into an executable format, and means for dynamically allocating and adjusting computing resources based on the workload of the information processing devices. This enables users to operate intuitively using natural language and realizes efficient information exchange and dynamic resource management among multiple information processing devices.

[0060] An "information processing device" is a general term for a device that has the function of processing data and performing specific tasks.

[0061] An "integrated control screen" is a display interface that centrally displays the status and performance of multiple information processing devices, making management easier.

[0062] "Natural language" refers to language used in everyday life, and is not a specific programming language or command format.

[0063] A "generative AI model" is a model that uses artificial intelligence technology to analyze natural language and interpret instructions.

[0064] "Computational resources" refer to resources such as CPU time, memory, and storage that an information processing device requires to perform a task.

[0065] "Dynamic allocation and adjustment" refers to rearranging computing resources according to time and circumstances to optimize their use.

[0066] This invention is a system for achieving efficient management and operation of information processing devices. It mainly consists of a server, a terminal, and a generative AI model.

[0067] Users instruct tasks using natural language via a terminal. For example, by entering a prompt such as "Prepare a sales analysis report for the end-of-month meeting," the corresponding task for each information processing device is initiated. The terminal is intuitively operable through its user interface, and users do not need to use specific programming languages ​​or complex commands.

[0068] Next, the instructions received from the terminal are sent to the server. The server interprets the instructions using a generative AI model and distributes the commands to multiple information processing devices as needed. The generative AI model utilizes natural language processing technology to convert the instructions into an executable format, thereby enabling efficient interpretation and processing of the information.

[0069] Furthermore, the server manages information exchange between information processing devices and optimizes overall performance by dynamically allocating computing resources based on the workload. For example, if a data analysis device shares data with a separate document creation device, the server manages the entire process, enabling smooth collaboration.

[0070] Thus, the present invention allows users to easily control the entire system using everyday language, efficiently manage the operation of multiple information processing devices, and improve the efficiency of business operations. Furthermore, the specific flow of operations is clearly illustrated through examples of prompt statements, serving as a reference for other users when utilizing this system.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The user enters instructions in natural language using a terminal. A specific prompt might be, "Prepare the sales analysis report for the end-of-month meeting." The entered natural language is sent directly from the terminal to the server.

[0074] Step 2:

[0075] The server passes natural language instructions received from the terminal to a generative AI model. The generative AI model analyzes these natural language instructions and breaks them down into executable tasks. This process involves sentence structure analysis and semantic extraction. Finally, specific instructions for each information processing device, based on the user's intent, are generated.

[0076] Step 3:

[0077] Based on the generated task instructions, the server constructs a set of instructions for each appropriate process and distributes them to the respective information processing devices. For example, it might instruct the data analysis information processing device to analyze sales data and the document creation information processing device to prepare related documents.

[0078] Step 4:

[0079] Each information processing unit receives instructions from the server and performs the necessary data processing and calculations. The data analysis information processing unit aggregates sales data and generates results. The document creation information processing unit prepares a sales analysis report based on these results.

[0080] Step 5:

[0081] The server aggregates the output from each information processing device, prepares the final deliverable, and then reports it to the user via an integrated control screen. This allows the user to confirm that the assigned task has been completed.

[0082] (Application Example 1)

[0083] 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."

[0084] Modern urban management requires the interconnected and integrated management of a wide range of information processing systems. However, effectively controlling the operation of each information processing system and dynamically allocating and adjusting resources presents significant challenges. In particular, there is a lack of automated means for optimizing traffic flow, monitoring urban infrastructure, and analyzing resource consumption. Therefore, there is a need for a system that allows urban managers to intuitively grasp the overall situation and issue instructions efficiently.

[0085] 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.

[0086] In this invention, the server includes means for providing an integrated control screen that aggregates and displays the status and performance of multiple information processing devices; means for assigning tasks to the multiple information processing devices using an input device capable of receiving instructions from a user in natural language format; and means for efficiently exchanging and sharing information among the information processing devices using a generation processing function. This enables urban administrators to automatically optimize traffic flow, urban infrastructure monitoring, and resource consumption analysis using natural language.

[0087] An "integrated control screen" is a screen that displays the status and performance of multiple information processing devices in a single visual interface, allowing users to understand it intuitively.

[0088] An "information processing device" is a device that has the function of receiving specific data and performing a specified task based on that data.

[0089] "Natural language form" refers to the language form that humans use on a daily basis, and is a method of inputting instructions to a system without using a special programming language.

[0090] "Generation processing function" refers to the technology that analyzes input natural language and converts it into a format that can be processed by an information processing device.

[0091] "Workload" refers to the degree of burden on an information processing device for the tasks it is currently performing, and is a factor that serves as a basis for the dynamic allocation of resources.

[0092] "Computational resources" refer to the hardware and software resources necessary for information processing devices to perform their tasks efficiently.

[0093] "Aggregative analysis" is a method of integrating multiple data sets and analyzing them statistically or logically.

[0094] A "results report" is a document or digital data that organizes the results of aggregation and analysis and presents them in a format that is easy for users to understand.

[0095] "Traffic fluidity" is a concept that indicates how smoothly traffic flows within a particular area.

[0096] "Urban infrastructure monitoring" is a process of monitoring the status and operation of urban infrastructure in real time to check for any abnormalities.

[0097] "Resource consumption analysis" is a method for analyzing the consumption patterns of energy and other resources used to find more efficient ways to utilize them.

[0098] To realize this application, the following system and program need to be constructed. The server collects data from multiple information processing devices in real time and displays their status and performance on an integrated control screen. This allows city administrators to intuitively grasp the overall picture of the information.

[0099] Users send instructions for urban management to the system using a natural language input device. These instructions are parsed by a generation processing function and converted into a format that can be processed by an information processing device. This generation processing function uses the OpenAI® GPT API as its natural language processing technology.

[0100] The information processing system automatically performs tasks such as optimizing traffic flow, monitoring urban infrastructure, and analyzing resource consumption. The Python Pandas library is used for data analysis. Furthermore, a mobile app developed using React Native allows users to access and control these functions from anywhere.

[0101] As a concrete example, by entering a prompt message such as "Generate an optimal traffic plan considering the increase in traffic volume for the next festival season" for a certain community event, the system analyzes traffic patterns and generates an optimized traffic signal plan. This result is reflected on the integrated control screen and reported to the user.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The user enters instructions in natural language format on the terminal. The prompt message "Generate an optimal traffic plan considering increased traffic volume for the next festival season" is entered. The entered instructions are sent to the server via the terminal.

[0105] Step 2:

[0106] The server analyzes the received natural language instructions using the OpenAI GPT API, a generation processing function. It analyzes the prompt text received as input, converts it into a format that the information processing device can understand, and outputs it.

[0107] Step 3:

[0108] The server distributes the converted instructions to the appropriate information processing unit. Here, an analysis module that processes traffic data is selected. This process takes real-time traffic data as input and applies an optimization algorithm based on that data to generate output.

[0109] Step 4:

[0110] The information processing device generates analysis results and returns them to the server. Specifically, it uses traffic flow data to derive the optimal signal control plan. This result is output and sent back to the server.

[0111] Step 5:

[0112] The server reflects the received analysis results on the integrated control screen and reports them to the user. The generated traffic signal plan is visually displayed on the screen, allowing the user to confirm the optimization results.

[0113] Step 6:

[0114] The user can issue additional instructions as needed from the integrated control screen, attempting further optimization. This process is repeated, with the server continuously processing new instructions each time.

[0115] 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.

[0116] This invention provides a system that improves the user experience by effectively managing multiple knowledge processing devices and combining them with an emotion engine. This system provides an integrated control surface and is configured to visually display the operating status and performance of each knowledge processing device.

[0117] Users can input tasks in natural language format using a terminal. The input information is sent to the server via the terminal and interpreted by a generation processing unit. In this interpretation process, the generation processing unit performs natural language analysis, converts the instructions into a concretely executable format, and assigns the task to the appropriate knowledge processing unit.

[0118] Furthermore, a mechanism for emotion recognition is incorporated into the system. This emotion engine can estimate the user's emotional state from their voice tone and text input. This information helps to adjust the behavior of the knowledge processing unit during task execution, providing personalized suggestions and feedback to the user.

[0119] For example, if the emotion engine recognizes that a user is experiencing stress, the server can instruct the knowledge processing unit to simplify complex tasks or present information in a more user-friendly format.

[0120] Finally, the server aggregates data from all knowledge processing devices and generates a results report in a user-friendly format. The emotion engine's estimations can also be incorporated into this report, allowing users to review their work performance including emotional factors.

[0121] In this way, the present invention utilizes emotion recognition to provide a system that not only manages knowledge processing devices but also enables flexible responses in accordance with the user's emotions.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The user enters instructions in natural language using a terminal. For example, they might type, "Prepare the latest sales report for next week's meeting."

[0125] Step 2:

[0126] The terminal sends the input instructions to the server and converts the input data into a format suitable for analysis.

[0127] Step 3:

[0128] The server uses a generation and processing unit to analyze the received instructions using natural language processing and understand the content of the task. This analysis includes identifying tasks related to the instructions.

[0129] Step 4:

[0130] The server uses an emotion engine to assess the user's emotions. It estimates emotions from the input voice and text and incorporates the results into processing.

[0131] Step 5:

[0132] The server assigns tasks to each knowledge processing unit based on the analysis results and sentiment evaluations. For example, it might have a data analysis knowledge processing unit analyze sales data and a report generation knowledge processing unit summarize the results.

[0133] Step 6:

[0134] Each knowledge processing unit performs its assigned task and processes the necessary data. Progress is constantly reported to the server.

[0135] Step 7:

[0136] The server displays task progress and sentiment evaluation results on an integrated control surface. Users can visually check the status in real time.

[0137] Step 8:

[0138] Once all processing is complete, the server aggregates the data from the knowledge processing unit and generates a results report for the user. This report includes sentiment-based comments and suggestions.

[0139] Step 9:

[0140] The user reviews the report generated on the terminal and enters any new instructions as needed. The system then restarts processing from step 1.

[0141] (Example 2)

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

[0143] In today's digital environment, advanced integrated management functions and flexible responses that respond to user emotions are required for users to efficiently manage various data processing devices and improve the user experience. However, conventional systems are insufficient in managing the status of individual devices and optimizing interactions that take user emotions into consideration, and therefore do not provide sufficient convenience for users.

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

[0145] In this invention, the server includes means for providing an integrated management function that aggregates and displays the operating status and performance of multiple data processing devices, means for efficiently exchanging and sharing information between data processing devices using an information generation device, and means for analyzing the user's emotional state using an emotion recognition device and adjusting the operation of the data processing devices according to the user's emotions. This enables the user to grasp the status of the data processing devices in real time and to provide optimal interaction and information presentation according to their individual emotions.

[0146] The "integrated management function" is a function that centrally aggregates and displays the operating status and performance of multiple data processing devices, allowing users to intuitively understand the status of each device.

[0147] An "input device" is a device or interface that can receive commands from a user in natural language format.

[0148] An "information generation device" is a system component that is responsible for the process of efficiently exchanging and sharing information between data processing devices and has the function of converting natural language into a system-executable format.

[0149] An "emotion recognition device" is a machine that analyzes a user's emotional state from their voice or text and adjusts the operation of related systems based on that analysis.

[0150] A "data processing system" is a collection of hardware and software designed to perform specific tasks or operations.

[0151] In this invention, users can input tasks in natural language format through a terminal. These tasks are expressed as prompts, such as "Prepare materials for next week's project meeting." The terminal receives this input and sends it to the server.

[0152] The server uses a specific generative AI model to analyze user input and interpret the instructions. This AI model has the ability to convert the input natural language into a machine-executable format. For example, if the analyzed content is "document creation," the appropriate document creation software will be selected, and the task will be made concrete.

[0153] Furthermore, the server incorporates an emotion recognition device that analyzes the user's emotional state. This device grasps the user's emotions through voice input and text analysis, and adjusts the operation of the data processing device accordingly. As a result, flexible interaction tailored to the user's emotional state becomes possible.

[0154] Specifically, if the system detects that a user is experiencing stress, the server simplifies the output information from the data processing device and provides it in a format that is easy for the user to understand. In this way, the present invention functions as a system that streamlines user task management and provides support tailored to the user's emotional state.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The user inputs tasks in natural language format using a terminal. This includes specific prompts such as, "Prepare materials for next week's project meeting." The input text is processed directly as data on the terminal and converted into the format required for the next step.

[0158] Step 2:

[0159] The terminal sends the text data entered by the user to the server. The server receives this input and begins processing it with a generative AI model. This model performs natural language analysis, analyzing the input text and converting it into a command that the system can execute. For example, it might be converted into a specific command such as "create a document."

[0160] Step 3:

[0161] Based on the analysis results, the server assigns tasks to appropriate data processing devices. Specifically, it launches document creation software according to the analyzed instructions, selects the necessary templates, and automatically prepares materials for a meeting. In this process, information obtained from the generative AI model is used to perform data processing.

[0162] Step 4:

[0163] The server is equipped with an emotion recognition device that analyzes the user's emotions. This device takes in text and voice data entered by the user and estimates their emotional state from it. For example, if it is estimated that the user is feeling stressed, the data processing device adjusts its operation and presents the information in a format that is easy for the user to understand.

[0164] Step 5:

[0165] Finally, the server aggregates all processing results and generates a report for the user. This report includes not only the task execution details but also the sentiment analysis results. The report is easy for the user to understand, for example, visually displaying task progress and changes in sentiment.

[0166] (Application Example 2)

[0167] 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".

[0168] In today's living environment, flexible responses that respond to users' emotional states are required. However, conventional systems are insufficient in recognizing user emotions, limiting their ability to improve the user experience. To solve this problem, a system is needed that can appropriately recognize user emotions and utilize that information in real time.

[0169] 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.

[0170] In this invention, the server includes means for estimating the user's emotional state using an emotion recognition engine, means for adjusting the operation of an information processing device based on the emotional state, and means for providing suggestions and feedback based on the user's emotions. This enables the provision of appropriate suggestions and feedback in real time that correspond to the user's emotions, thereby improving the user experience.

[0171] An "information processing device" is a computing device that analyzes input information and generates specific results.

[0172] An "integrated control surface" is an interface that aggregates and visually presents the status and performance of multiple information processing devices.

[0173] An "input / output device" is a device that receives instructions in natural language format from a user and presents the results to the user.

[0174] A "generation processing device" is a device that efficiently exchanges information between information processing devices and generates results.

[0175] "Dynamic allocation of computing resources" refers to the act of appropriately allocating and optimizing computing power according to the workload of an information processing device.

[0176] An "emotion recognition engine" is a device or function that estimates a user's emotional state from their voice or input.

[0177] "Suggestions and feedback" refers to the act of providing appropriate actions or information and responding based on the user's input or status.

[0178] To realize this invention, the system comprises multiple information processing devices and a program that manages them in an integrated manner. The main hardware includes information processing devices, user-operated input / output devices, and an integrated control surface for aggregating and visualizing information. The software needs to include a speech recognition engine, a natural language processing engine, and an emotion recognition engine.

[0179] The server receives instructions from the user in natural language format via input / output devices. These instructions are interpreted by a natural language processing engine and converted into a format that can be executed by the information processing device. The server uses an emotion recognition engine to estimate the user's emotional state and adjusts the operation of the information processing device accordingly. This allows the server to provide the user with suggestions and feedback based on their emotional state.

[0180] As a concrete example, suppose a user inputs the voice command "I want to relax." In this case, the emotion recognition engine analyzes the tone of voice to determine if the user is experiencing stress. Based on this information, the server instructs the information processing unit to take actions such as playing relaxing music and dimming the lights.

[0181] An example of a prompt for a generative AI model is a question like, "The user wants to relax. What kind of entertainment or environmental adjustments should be offered?" In this way, emotion recognition can be utilized to provide flexible responses that are sensitive to the user's emotions.

[0182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0183] Step 1:

[0184] The user inputs natural language instructions into the input / output device. For example, they might input the voice command "I want to relax." Upon receiving this input, the terminal converts the voice data into text data, making it ready for natural language processing.

[0185] Step 2:

[0186] Text data sent from the terminal is analyzed by a natural language processing engine on the server. Here, the intent of the instruction is interpreted, and the meaning "I want to relax" is extracted. The interpreted data is then converted into an instruction format that can be executed by the information processing device.

[0187] Step 3:

[0188] The server uses an emotion recognition engine to estimate the user's emotional state from their voice tone and expressions. In this step, text and audio data are taken as input, emotion analysis is performed, and the result "the user is stressed" is output.

[0189] Step 4:

[0190] Based on the analyzed emotional state and user intent, the server instructs the information processing unit to take specific actions. For example, it generates commands to play relaxation music or soften the lighting.

[0191] Step 5:

[0192] The information processing device performs actual operations based on commands from the server. For example, it may start a music player and play selected relaxation music, or adjust the brightness of a lighting device.

[0193] Step 6:

[0194] The server aggregates the overall processing results and provides feedback to the user in an easy-to-understand format, either on screen or via audio. Here, it confirms that the user's requested actions have been performed and considers any additional actions that may be necessary.

[0195] Through this series of steps, flexible responses that take user emotions into consideration become possible.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Second Embodiment]

[0200] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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".

[0212] This invention is a system for efficiently managing and optimally operating multiple knowledge processing devices. This system aggregates and displays the operating status and performance of various knowledge processing devices on an integrated control surface, allowing the user to grasp the overall status at a glance.

[0213] The user inputs the task in natural language format via a terminal. This input instruction is sent from the terminal to the server. The server analyzes this instruction using a generation processing unit and distributes it to each knowledge processing unit in the appropriate format.

[0214] The generation and processing unit interprets user instructions using advanced natural language processing technology. Therefore, users can operate the system using everyday language without needing to use specific programming languages ​​or commands.

[0215] Furthermore, the server facilitates effective information exchange between knowledge processing devices. For example, if a knowledge processing device performing a document review requires the results of data analysis, it automatically retrieves the appropriate information from related knowledge processing devices.

[0216] For example, if a user issues a request such as "Prepare a sales analysis report for the end-of-month meeting," the server will have the schedule management knowledge processing unit check the schedule, instruct the document review knowledge processing unit to prepare the relevant documents, and request the data analysis knowledge processing unit to analyze the sales data. Finally, the server will report to the user that the preparations are complete from an integrated control perspective.

[0217] Thus, this invention provides a configuration that centrally manages the operation of a knowledge processing device and can efficiently issue instructions through natural language. As a result, it is possible to significantly improve the efficiency of business operations.

[0218] The following describes the processing flow.

[0219] Step 1:

[0220] Users input tasks using a terminal in natural language format. For example, they might input instructions such as "Prepare materials for next month's sales meeting."

[0221] Step 2:

[0222] The terminal sends the input instructions to the server. During this process, the instructions are converted into a data format and processed to enable efficient communication.

[0223] Step 3:

[0224] The server passes the received instructions to the generation and processing unit, which then analyzes them using a natural language processing engine. It interprets the tasks that make up the instructions and formulates a specific execution plan.

[0225] Step 4:

[0226] The server assigns tasks to appropriate knowledge processing units based on the analysis results from the generation processing units. It generates individual instructions for each unit and efficiently allocates resources.

[0227] Step 5:

[0228] Each knowledge processing device begins executing its assigned task. For example, a scheduling device checks meeting dates, and a document review device prepares necessary materials.

[0229] Step 6:

[0230] The server periodically collects task progress from each knowledge processing unit. This allows it to monitor whether tasks are progressing properly and make adjustments as needed.

[0231] Step 7:

[0232] The server generates user reports based on the aggregated information. Progress information and results are displayed on the integrated control panel, notifying users when tasks are completed.

[0233] Step 8:

[0234] The user reviews the report displayed on the integrated control panel to confirm that the task was completed correctly. If necessary, they can re-enter additional instructions.

[0235] (Example 1)

[0236] 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."

[0237] In information processing systems, a key challenge is to centrally understand and efficiently manage the status and performance of multiple information processing devices. Furthermore, it is essential that users can easily operate the system using natural language and that effective information exchange takes place between the devices. This necessitates a system that can improve operational efficiency while optimally allocating computing resources.

[0238] 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.

[0239] In this invention, the server includes means for providing an integrated control screen that aggregates and displays the status and performance of multiple information processing devices, means for utilizing a generative AI model to analyze user instructions in natural language and convert them into an executable format, and means for dynamically allocating and adjusting computing resources based on the workload of the information processing devices. This enables users to operate intuitively using natural language and realizes efficient information exchange and dynamic resource management among multiple information processing devices.

[0240] An "information processing device" is a general term for a device that has the function of processing data and performing specific tasks.

[0241] An "integrated control screen" is a display interface that centrally displays the status and performance of multiple information processing devices, making management easier.

[0242] "Natural language" refers to language used in everyday life, and is not a specific programming language or command format.

[0243] A "generative AI model" is a model that uses artificial intelligence technology to analyze natural language and interpret instructions.

[0244] "Computational resources" refer to resources such as CPU time, memory, and storage that an information processing device requires to perform a task.

[0245] "Dynamic allocation and adjustment" refers to rearranging computing resources according to time and circumstances to optimize their use.

[0246] This invention is a system for achieving efficient management and operation of information processing devices. It mainly consists of a server, a terminal, and a generative AI model.

[0247] Users instruct tasks using natural language via a terminal. For example, by entering a prompt such as "Prepare a sales analysis report for the end-of-month meeting," the corresponding task for each information processing device is initiated. The terminal is intuitively operable through its user interface, and users do not need to use specific programming languages ​​or complex commands.

[0248] Next, the instructions received from the terminal are sent to the server. The server interprets the instructions using a generative AI model and distributes the commands to multiple information processing devices as needed. The generative AI model utilizes natural language processing technology to convert the instructions into an executable format, thereby enabling efficient interpretation and processing of the information.

[0249] Furthermore, the server manages information exchange between information processing devices and optimizes overall performance by dynamically allocating computing resources based on the workload. For example, if a data analysis device shares data with a separate document creation device, the server manages the entire process, enabling smooth collaboration.

[0250] Thus, the present invention allows users to easily control the entire system using everyday language, efficiently manage the operation of multiple information processing devices, and improve the efficiency of business operations. Furthermore, the specific flow of operations is clearly illustrated through examples of prompt statements, serving as a reference for other users when utilizing this system.

[0251] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0252] Step 1:

[0253] The user enters instructions in natural language using a terminal. A specific prompt might be, "Prepare the sales analysis report for the end-of-month meeting." The entered natural language is sent directly from the terminal to the server.

[0254] Step 2:

[0255] The server passes natural language instructions received from the terminal to a generative AI model. The generative AI model analyzes these natural language instructions and breaks them down into executable tasks. This process involves sentence structure analysis and semantic extraction. Finally, specific instructions for each information processing device, based on the user's intent, are generated.

[0256] Step 3:

[0257] Based on the generated task instructions, the server constructs a set of instructions for each appropriate process and distributes them to the respective information processing devices. For example, it might instruct the data analysis information processing device to analyze sales data and the document creation information processing device to prepare related documents.

[0258] Step 4:

[0259] Each information processing unit receives instructions from the server and performs the necessary data processing and calculations. The data analysis information processing unit aggregates sales data and generates results. The document creation information processing unit prepares a sales analysis report based on these results.

[0260] Step 5:

[0261] The server aggregates the output from each information processing device, prepares the final deliverable, and then reports it to the user via an integrated control screen. This allows the user to confirm that the assigned task has been completed.

[0262] (Application Example 1)

[0263] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0264] Modern urban management requires the interconnected and integrated management of a wide range of information processing systems. However, effectively controlling the operation of each information processing system and dynamically allocating and adjusting resources presents significant challenges. In particular, there is a lack of automated means for optimizing traffic flow, monitoring urban infrastructure, and analyzing resource consumption. Therefore, there is a need for a system that allows urban managers to intuitively grasp the overall situation and issue instructions efficiently.

[0265] 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.

[0266] In this invention, the server includes means for providing an integrated control screen that aggregates and displays the status and performance of multiple information processing devices; means for assigning tasks to the multiple information processing devices using an input device capable of receiving instructions from a user in natural language format; and means for efficiently exchanging and sharing information among the information processing devices using a generation processing function. This enables urban administrators to automatically optimize traffic flow, urban infrastructure monitoring, and resource consumption analysis using natural language.

[0267] An "integrated control screen" is a screen that displays the status and performance of multiple information processing devices in a single visual interface, allowing users to understand it intuitively.

[0268] An "information processing device" is a device that has the function of receiving specific data and performing a specified task based on that data.

[0269] "Natural language form" refers to the language form that humans use on a daily basis, and is a method of inputting instructions to a system without using a special programming language.

[0270] "Generation processing function" refers to the technology that analyzes input natural language and converts it into a format that can be processed by an information processing device.

[0271] "Workload" refers to the degree of burden on an information processing device for the tasks it is currently performing, and is a factor that serves as a basis for the dynamic allocation of resources.

[0272] "Computational resources" refer to the hardware and software resources necessary for information processing devices to perform their tasks efficiently.

[0273] "Aggregative analysis" is a method of integrating multiple data sets and analyzing them statistically or logically.

[0274] A "results report" is a document or digital data that organizes the results of aggregation and analysis and presents them in a format that is easy for users to understand.

[0275] "Traffic fluidity" is a concept that indicates how smoothly traffic flows within a particular area.

[0276] "Urban infrastructure monitoring" is a process of monitoring the status and operation of urban infrastructure in real time to check for any abnormalities.

[0277] "Resource consumption analysis" is a method for analyzing the consumption patterns of energy and other resources used to find more efficient ways to utilize them.

[0278] To implement this application example, the following systems and programs need to be constructed. The server collects data from multiple information processing devices in real time and aggregates and displays their status and performance on an integrated control screen. This enables urban managers to intuitively grasp the overall picture of the information.

[0279] The user uses an input device in natural language form to send instructions for urban management to the system. This instruction is analyzed by the generation processing function and converted into a form that can be processed by the information processing device. The OpenAI GPT API is used as the natural language processing technology for this generation processing function.

[0280] The information processing device automatically executes tasks such as optimizing traffic mobility, monitoring urban infrastructure, and analyzing resource consumption. The Python Pandas library is used for data analysis. Also, a mobile app developed using React Native enables users to access these functions and issue instructions from anywhere.

[0281] As a specific example, by inputting a prompt sentence "Please generate an optimal traffic plan considering the increase in traffic volume for the next festival season" for a certain civic event, the movement of traffic is analyzed and an optimized traffic signal plan is generated. This result is reflected on the integrated control screen and reported to the user.

[0282] The flow of specific processing in Application Example 1 will be described using Figure 12.

[0283] Step 1:

[0284] The user inputs an instruction in natural language form on the terminal. The prompt sentence "Please generate an optimal traffic plan considering the increase in traffic volume for the next festival season" is input. The input instruction is sent to the server via the terminal.

[0285] Step 2:

[0286] The server analyzes the received natural language instructions using the OpenAI GPT API, which is a generation processing function. It analyzes the prompt sentence received as input, converts it into a format that the information processing device can understand, and outputs it.

[0287] Step 3:

[0288] The server distributes the converted instructions to the appropriate information processing device. Here, an analysis module that processes traffic data is selected. For this processing, traffic data acquired in real time is used as input, and an optimization algorithm is applied based on this data to generate an output.

[0289] Step 4:

[0290] The information processing device generates an analysis result and returns the result to the server. Specifically, using the traffic mobility data, an optimal signal control plan is derived. This result is output and sent back to the server.

[0291] Step 5:

[0292] The server reflects the received analysis result on the integrated control screen and reports it to the user. On the screen, the generated traffic signal plan is visually displayed so that the user can confirm the optimization result.

[0293] Step 6:

[0294] The user can issue additional instructions from the integrated control screen as needed and attempt further optimization. This process is repeated, and each time, the server continues to process the new instructions.

[0295] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0296] This invention provides a system that improves the user experience by effectively managing multiple knowledge processing devices and combining them with an emotion engine. This system provides an integrated control surface and is configured to visually display the operating status and performance of each knowledge processing device.

[0297] Users can input tasks in natural language format using a terminal. The input information is sent to the server via the terminal and interpreted by a generation processing unit. In this interpretation process, the generation processing unit performs natural language analysis, converts the instructions into a concretely executable format, and assigns the task to the appropriate knowledge processing unit.

[0298] Furthermore, a mechanism for emotion recognition is incorporated into the system. This emotion engine can estimate the user's emotional state from their voice tone and text input. This information helps to adjust the behavior of the knowledge processing unit during task execution, providing personalized suggestions and feedback to the user.

[0299] For example, if the emotion engine recognizes that a user is experiencing stress, the server can instruct the knowledge processing unit to simplify complex tasks or present information in a more user-friendly format.

[0300] Finally, the server aggregates data from all knowledge processing devices and generates a results report in a user-friendly format. The emotion engine's estimations can also be incorporated into this report, allowing users to review their work performance including emotional factors.

[0301] In this way, the present invention utilizes emotion recognition to provide a system that not only manages knowledge processing devices but also enables flexible responses in accordance with the user's emotions.

[0302] The following describes the processing flow.

[0303] Step 1:

[0304] The user inputs instructions in natural language form using a terminal. For example, the user inputs "Prepare the latest sales report for next week's meeting."

[0305] Step 2:

[0306] The terminal sends the input instructions to the server and converts the input data into a form suitable for analysis.

[0307] Step 3:

[0308] The server uses a generation processing device to analyze the received instructions through natural language processing and understand the content of the task. This analysis includes identifying the task related to the instructions.

[0309] Step 4:

[0310] The server uses an emotion engine to evaluate the user's emotion. It estimates the emotion from the input voice or text and incorporates the result into the processing.

[0311] Step 5:

[0312] Based on the analysis results and emotion evaluation, the server assigns tasks to each knowledge processing device. For example, it makes the data analysis knowledge processing device analyze the sales data and the report generation knowledge processing device summarize the results.

[0313] Step 6:

[0314] Each knowledge processing device executes the assigned task and processes the necessary data. The progress status is always reported to the server.

[0315] Step 7:

[0316] The server displays the progress status of the task and the emotion evaluation results on the integrated control panel. The user can visually confirm the situation in real time.

[0317] Step 8:

[0318] Once all processing is complete, the server aggregates the data from the knowledge processing unit and generates a results report for the user. This report includes sentiment-based comments and suggestions.

[0319] Step 9:

[0320] The user reviews the report generated on the terminal and enters any new instructions as needed. The system then restarts processing from step 1.

[0321] (Example 2)

[0322] 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".

[0323] In today's digital environment, advanced integrated management functions and flexible responses that respond to user emotions are required for users to efficiently manage various data processing devices and improve the user experience. However, conventional systems are insufficient in managing the status of individual devices and optimizing interactions that take user emotions into consideration, and therefore do not provide sufficient convenience for users.

[0324] 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.

[0325] In this invention, the server includes means for providing an integrated management function that aggregates and displays the operating status and performance of multiple data processing devices, means for efficiently exchanging and sharing information between data processing devices using an information generation device, and means for analyzing the user's emotional state using an emotion recognition device and adjusting the operation of the data processing devices according to the user's emotions. This enables the user to grasp the status of the data processing devices in real time and to provide optimal interaction and information presentation according to their individual emotions.

[0326] The "integrated management function" is a function that centrally aggregates and displays the operating status and performance of multiple data processing devices, allowing users to intuitively understand the status of each device.

[0327] An "input device" is a device or interface that can receive commands from a user in natural language format.

[0328] An "information generation device" is a system component that is responsible for the process of efficiently exchanging and sharing information between data processing devices and has the function of converting natural language into a system-executable format.

[0329] An "emotion recognition device" is a machine that analyzes a user's emotional state from their voice or text and adjusts the operation of related systems based on that analysis.

[0330] A "data processing system" is a collection of hardware and software designed to perform specific tasks or operations.

[0331] In this invention, users can input tasks in natural language format through a terminal. These tasks are expressed as prompts, such as "Prepare materials for next week's project meeting." The terminal receives this input and sends it to the server.

[0332] The server uses a specific generative AI model to analyze user input and interpret the instructions. This AI model has the ability to convert the input natural language into a machine-executable format. For example, if the analyzed content is "document creation," the appropriate document creation software will be selected, and the task will be made concrete.

[0333] Furthermore, the server incorporates an emotion recognition device that analyzes the user's emotional state. This device grasps the user's emotions through voice input and text analysis, and adjusts the operation of the data processing device accordingly. As a result, flexible interaction tailored to the user's emotional state becomes possible.

[0334] Specifically, if the system detects that a user is experiencing stress, the server simplifies the output information from the data processing device and provides it in a format that is easy for the user to understand. In this way, the present invention functions as a system that streamlines user task management and provides support tailored to the user's emotional state.

[0335] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0336] Step 1:

[0337] The user inputs tasks in natural language format using a terminal. This includes specific prompts such as, "Prepare materials for next week's project meeting." The input text is processed directly as data on the terminal and converted into the format required for the next step.

[0338] Step 2:

[0339] The terminal sends the text data entered by the user to the server. The server receives this input and begins processing it with a generative AI model. This model performs natural language analysis, analyzing the input text and converting it into a command that the system can execute. For example, it might be converted into a specific command such as "create a document."

[0340] Step 3:

[0341] Based on the analysis results, the server assigns tasks to appropriate data processing devices. Specifically, it launches document creation software according to the analyzed instructions, selects the necessary templates, and automatically prepares materials for a meeting. In this process, information obtained from the generative AI model is used to perform data processing.

[0342] Step 4:

[0343] The server is equipped with an emotion recognition device that analyzes the user's emotions. This device takes in text and voice data entered by the user and estimates their emotional state from it. For example, if it is estimated that the user is feeling stressed, the data processing device adjusts its operation and presents the information in a format that is easy for the user to understand.

[0344] Step 5:

[0345] Finally, the server aggregates all processing results and generates a report for the user. This report includes not only the task execution details but also the sentiment analysis results. The report is easy for the user to understand, for example, visually displaying task progress and changes in sentiment.

[0346] (Application Example 2)

[0347] 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 as the "terminal".

[0348] In today's living environment, flexible responses that respond to users' emotional states are required. However, conventional systems are insufficient in recognizing user emotions, limiting their ability to improve the user experience. To solve this problem, a system is needed that can appropriately recognize user emotions and utilize that information in real time.

[0349] 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.

[0350] In this invention, the server includes means for estimating the user's emotional state using an emotion recognition engine, means for adjusting the operation of an information processing device based on the emotional state, and means for providing suggestions and feedback based on the user's emotions. This enables the provision of appropriate suggestions and feedback in real time that correspond to the user's emotions, thereby improving the user experience.

[0351] An "information processing device" is a computing device that analyzes input information and generates specific results.

[0352] An "integrated control surface" is an interface that aggregates and visually presents the status and performance of multiple information processing devices.

[0353] An "input / output device" is a device that receives instructions in natural language format from a user and presents the results to the user.

[0354] A "generation processing device" is a device that efficiently exchanges information between information processing devices and generates results.

[0355] "Dynamic allocation of computing resources" refers to the act of appropriately allocating and optimizing computing power according to the workload of an information processing device.

[0356] An "emotion recognition engine" is a device or function that estimates a user's emotional state from their voice or input.

[0357] "Suggestions and feedback" refers to the act of providing appropriate actions or information and responding based on the user's input or status.

[0358] To realize this invention, the system comprises multiple information processing devices and a program that manages them in an integrated manner. The main hardware includes information processing devices, user-operated input / output devices, and an integrated control surface for aggregating and visualizing information. The software needs to include a speech recognition engine, a natural language processing engine, and an emotion recognition engine.

[0359] The server receives instructions from the user in natural language format via input / output devices. These instructions are interpreted by a natural language processing engine and converted into a format that can be executed by the information processing device. The server uses an emotion recognition engine to estimate the user's emotional state and adjusts the operation of the information processing device accordingly. This allows the server to provide the user with suggestions and feedback based on their emotional state.

[0360] As a concrete example, suppose a user inputs the voice command "I want to relax." In this case, the emotion recognition engine analyzes the tone of voice to determine if the user is experiencing stress. Based on this information, the server instructs the information processing unit to take actions such as playing relaxing music and dimming the lights.

[0361] An example of a prompt for a generative AI model is a question like, "The user wants to relax. What kind of entertainment or environmental adjustments should be offered?" In this way, emotion recognition can be utilized to provide flexible responses that are sensitive to the user's emotions.

[0362] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0363] Step 1:

[0364] The user inputs natural language instructions into the input / output device. For example, they might input the voice command "I want to relax." Upon receiving this input, the terminal converts the voice data into text data, making it ready for natural language processing.

[0365] Step 2:

[0366] Text data sent from the terminal is analyzed by a natural language processing engine on the server. Here, the intent of the instruction is interpreted, and the meaning "I want to relax" is extracted. The interpreted data is then converted into an instruction format that can be executed by the information processing device.

[0367] Step 3:

[0368] The server uses an emotion recognition engine to estimate the user's emotional state from their voice tone and expressions. In this step, text and audio data are taken as input, emotion analysis is performed, and the result "the user is stressed" is output.

[0369] Step 4:

[0370] Based on the analyzed emotional state and user intent, the server instructs the information processing unit to take specific actions. For example, it generates commands to play relaxation music or soften the lighting.

[0371] Step 5:

[0372] The information processing device performs actual operations based on commands from the server. For example, it may start a music player and play selected relaxation music, or adjust the brightness of a lighting device.

[0373] Step 6:

[0374] The server aggregates the overall processing results and provides feedback to the user in an easy-to-understand format, either on screen or via audio. Here, it confirms that the user's requested actions have been performed and considers any additional actions that may be necessary.

[0375] Through this series of steps, flexible responses that take user emotions into consideration become possible.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] [Third Embodiment]

[0380] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0381] 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.

[0382] 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).

[0383] 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.

[0384] 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.

[0385] 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).

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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".

[0392] This invention is a system for efficiently managing and optimally operating multiple knowledge processing devices. This system aggregates and displays the operating status and performance of various knowledge processing devices on an integrated control surface, allowing the user to grasp the overall status at a glance.

[0393] The user inputs the task in natural language format via a terminal. This input instruction is sent from the terminal to the server. The server analyzes this instruction using a generation processing unit and distributes it to each knowledge processing unit in the appropriate format.

[0394] The generation and processing unit interprets user instructions using advanced natural language processing technology. Therefore, users can operate the system using everyday language without needing to use specific programming languages ​​or commands.

[0395] Furthermore, the server facilitates effective information exchange between knowledge processing devices. For example, if a knowledge processing device performing a document review requires the results of data analysis, it automatically retrieves the appropriate information from related knowledge processing devices.

[0396] For example, if a user issues a request such as "Prepare a sales analysis report for the end-of-month meeting," the server will have the schedule management knowledge processing unit check the schedule, instruct the document review knowledge processing unit to prepare the relevant documents, and request the data analysis knowledge processing unit to analyze the sales data. Finally, the server will report to the user that the preparations are complete from an integrated control perspective.

[0397] Thus, this invention provides a configuration that centrally manages the operation of a knowledge processing device and can efficiently issue instructions through natural language. As a result, it is possible to significantly improve the efficiency of business operations.

[0398] The following describes the processing flow.

[0399] Step 1:

[0400] Users input tasks using a terminal in natural language format. For example, they might input instructions such as "Prepare materials for next month's sales meeting."

[0401] Step 2:

[0402] The terminal sends the input instructions to the server. During this process, the instructions are converted into a data format and processed to enable efficient communication.

[0403] Step 3:

[0404] The server passes the received instructions to the generation and processing unit, which then analyzes them using a natural language processing engine. It interprets the tasks that make up the instructions and formulates a specific execution plan.

[0405] Step 4:

[0406] The server assigns tasks to appropriate knowledge processing units based on the analysis results from the generation processing units. It generates individual instructions for each unit and efficiently allocates resources.

[0407] Step 5:

[0408] Each knowledge processing device begins executing its assigned task. For example, a scheduling device checks meeting dates, and a document review device prepares necessary materials.

[0409] Step 6:

[0410] The server periodically collects task progress from each knowledge processing unit. This allows it to monitor whether tasks are progressing properly and make adjustments as needed.

[0411] Step 7:

[0412] The server generates user reports based on the aggregated information. Progress information and results are displayed on the integrated control panel, notifying users when tasks are completed.

[0413] Step 8:

[0414] The user reviews the report displayed on the integrated control panel to confirm that the task was completed correctly. If necessary, they can re-enter additional instructions.

[0415] (Example 1)

[0416] 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."

[0417] In information processing systems, a key challenge is to centrally understand and efficiently manage the status and performance of multiple information processing devices. Furthermore, it is essential that users can easily operate the system using natural language and that effective information exchange takes place between the devices. This necessitates a system that can improve operational efficiency while optimally allocating computing resources.

[0418] 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.

[0419] In this invention, the server includes means for providing an integrated control screen that aggregates and displays the status and performance of multiple information processing devices, means for utilizing a generative AI model to analyze user instructions in natural language and convert them into an executable format, and means for dynamically allocating and adjusting computing resources based on the workload of the information processing devices. This enables users to operate intuitively using natural language and realizes efficient information exchange and dynamic resource management among multiple information processing devices.

[0420] An "information processing device" is a general term for a device that has the function of processing data and performing specific tasks.

[0421] An "integrated control screen" is a display interface that centrally displays the status and performance of multiple information processing devices, making management easier.

[0422] "Natural language" refers to language used in everyday life, and is not a specific programming language or command format.

[0423] A "generative AI model" is a model that uses artificial intelligence technology to analyze natural language and interpret instructions.

[0424] "Computational resources" refer to resources such as CPU time, memory, and storage that an information processing device requires to perform a task.

[0425] "Dynamic allocation and adjustment" refers to rearranging computing resources according to time and circumstances to optimize their use.

[0426] This invention is a system for achieving efficient management and operation of information processing devices. It mainly consists of a server, a terminal, and a generative AI model.

[0427] Users instruct tasks using natural language via a terminal. For example, by entering a prompt such as "Prepare a sales analysis report for the end-of-month meeting," the corresponding task for each information processing device is initiated. The terminal is intuitively operable through its user interface, and users do not need to use specific programming languages ​​or complex commands.

[0428] Next, the instructions received from the terminal are sent to the server. The server interprets the instructions using a generative AI model and distributes the commands to multiple information processing devices as needed. The generative AI model utilizes natural language processing technology to convert the instructions into an executable format, thereby enabling efficient interpretation and processing of the information.

[0429] Furthermore, the server manages information exchange between information processing devices and optimizes overall performance by dynamically allocating computing resources based on the workload. For example, if a data analysis device shares data with a separate document creation device, the server manages the entire process, enabling smooth collaboration.

[0430] Thus, the present invention allows users to easily control the entire system using everyday language, efficiently manage the operation of multiple information processing devices, and improve the efficiency of business operations. Furthermore, the specific flow of operations is clearly illustrated through examples of prompt statements, serving as a reference for other users when utilizing this system.

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] The user enters instructions in natural language using a terminal. A specific prompt might be, "Prepare the sales analysis report for the end-of-month meeting." The entered natural language is sent directly from the terminal to the server.

[0434] Step 2:

[0435] The server passes natural language instructions received from the terminal to a generative AI model. The generative AI model analyzes these natural language instructions and breaks them down into executable tasks. This process involves sentence structure analysis and semantic extraction. Finally, specific instructions for each information processing device, based on the user's intent, are generated.

[0436] Step 3:

[0437] Based on the generated task instructions, the server constructs a set of instructions for each appropriate process and distributes them to the respective information processing devices. For example, it might instruct the data analysis information processing device to analyze sales data and the document creation information processing device to prepare related documents.

[0438] Step 4:

[0439] Each information processing unit receives instructions from the server and performs the necessary data processing and calculations. The data analysis information processing unit aggregates sales data and generates results. The document creation information processing unit prepares a sales analysis report based on these results.

[0440] Step 5:

[0441] The server aggregates the output from each information processing device, prepares the final deliverable, and then reports it to the user via an integrated control screen. This allows the user to confirm that the assigned task has been completed.

[0442] (Application Example 1)

[0443] 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."

[0444] Modern urban management requires the interconnected and integrated management of a wide range of information processing systems. However, effectively controlling the operation of each information processing system and dynamically allocating and adjusting resources presents significant challenges. In particular, there is a lack of automated means for optimizing traffic flow, monitoring urban infrastructure, and analyzing resource consumption. Therefore, there is a need for a system that allows urban managers to intuitively grasp the overall situation and issue instructions efficiently.

[0445] 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.

[0446] In this invention, the server includes means for providing an integrated control screen that aggregates and displays the status and performance of multiple information processing devices; means for assigning tasks to the multiple information processing devices using an input device capable of receiving instructions from a user in natural language format; and means for efficiently exchanging and sharing information among the information processing devices using a generation processing function. This enables urban administrators to automatically optimize traffic flow, urban infrastructure monitoring, and resource consumption analysis using natural language.

[0447] An "integrated control screen" is a screen that displays the status and performance of multiple information processing devices in a single visual interface, allowing users to understand it intuitively.

[0448] An "information processing device" is a device that has the function of receiving specific data and performing a specified task based on that data.

[0449] "Natural language form" refers to the language form that humans use on a daily basis, and is a method of inputting instructions to a system without using a special programming language.

[0450] "Generation processing function" refers to the technology that analyzes input natural language and converts it into a format that can be processed by an information processing device.

[0451] "Workload" refers to the degree of burden on an information processing device for the tasks it is currently performing, and is a factor that serves as a basis for the dynamic allocation of resources.

[0452] "Computational resources" refer to the hardware and software resources necessary for information processing devices to perform their tasks efficiently.

[0453] "Aggregative analysis" is a method of integrating multiple data sets and analyzing them statistically or logically.

[0454] A "results report" is a document or digital data that organizes the results of aggregation and analysis and presents them in a format that is easy for users to understand.

[0455] "Traffic fluidity" is a concept that indicates how smoothly traffic flows within a particular area.

[0456] "Urban infrastructure monitoring" is a process of monitoring the status and operation of urban infrastructure in real time to check for any abnormalities.

[0457] "Resource consumption analysis" is a method for analyzing the consumption patterns of energy and other resources used to find more efficient ways to utilize them.

[0458] To realize this application, the following system and program need to be constructed. The server collects data from multiple information processing devices in real time and displays their status and performance on an integrated control screen. This allows city administrators to intuitively grasp the overall picture of the information.

[0459] Users send instructions for urban management to the system using a natural language input device. These instructions are parsed by a generation processing function and converted into a format that can be processed by an information processing device. This generation processing function uses the OpenAI GPT API as its natural language processing technology.

[0460] The information processing system automatically performs tasks such as optimizing traffic flow, monitoring urban infrastructure, and analyzing resource consumption. The Python Pandas library is used for data analysis. Furthermore, a mobile app developed using React Native allows users to access and control these functions from anywhere.

[0461] As a concrete example, by entering a prompt message such as "Generate an optimal traffic plan considering the increase in traffic volume for the next festival season" for a certain community event, the system analyzes traffic patterns and generates an optimized traffic signal plan. This result is reflected on the integrated control screen and reported to the user.

[0462] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0463] Step 1:

[0464] The user enters instructions in natural language format on the terminal. The prompt message "Generate an optimal traffic plan considering increased traffic volume for the next festival season" is entered. The entered instructions are sent to the server via the terminal.

[0465] Step 2:

[0466] The server analyzes the received natural language instructions using the OpenAI GPT API, a generation processing function. It analyzes the prompt text received as input, converts it into a format that the information processing device can understand, and outputs it.

[0467] Step 3:

[0468] The server distributes the converted instructions to the appropriate information processing unit. Here, an analysis module that processes traffic data is selected. This process takes real-time traffic data as input and applies an optimization algorithm based on that data to generate output.

[0469] Step 4:

[0470] The information processing device generates analysis results and returns them to the server. Specifically, it uses traffic flow data to derive the optimal signal control plan. This result is output and sent back to the server.

[0471] Step 5:

[0472] The server reflects the received analysis results on the integrated control screen and reports them to the user. The generated traffic signal plan is visually displayed on the screen, allowing the user to confirm the optimization results.

[0473] Step 6:

[0474] The user can issue additional instructions as needed from the integrated control screen, attempting further optimization. This process is repeated, with the server continuously processing new instructions each time.

[0475] 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.

[0476] This invention provides a system that improves the user experience by effectively managing multiple knowledge processing devices and combining them with an emotion engine. This system provides an integrated control surface and is configured to visually display the operating status and performance of each knowledge processing device.

[0477] Users can input tasks in natural language format using a terminal. The input information is sent to the server via the terminal and interpreted by a generation processing unit. In this interpretation process, the generation processing unit performs natural language analysis, converts the instructions into a concretely executable format, and assigns the task to the appropriate knowledge processing unit.

[0478] Furthermore, a mechanism for emotion recognition is incorporated into the system. This emotion engine can estimate the user's emotional state from their voice tone and text input. This information helps to adjust the behavior of the knowledge processing unit during task execution, providing personalized suggestions and feedback to the user.

[0479] For example, if the emotion engine recognizes that a user is experiencing stress, the server can instruct the knowledge processing unit to simplify complex tasks or present information in a more user-friendly format.

[0480] Finally, the server aggregates data from all knowledge processing devices and generates a results report in a user-friendly format. The emotion engine's estimations can also be incorporated into this report, allowing users to review their work performance including emotional factors.

[0481] In this way, the present invention utilizes emotion recognition to provide a system that not only manages knowledge processing devices but also enables flexible responses in accordance with the user's emotions.

[0482] The following describes the processing flow.

[0483] Step 1:

[0484] The user enters instructions in natural language using a terminal. For example, they might type, "Prepare the latest sales report for next week's meeting."

[0485] Step 2:

[0486] The terminal sends the input instructions to the server and converts the input data into a format suitable for analysis.

[0487] Step 3:

[0488] The server uses a generation and processing unit to analyze the received instructions using natural language processing and understand the content of the task. This analysis includes identifying tasks related to the instructions.

[0489] Step 4:

[0490] The server uses an emotion engine to assess the user's emotions. It estimates emotions from the input voice and text and incorporates the results into processing.

[0491] Step 5:

[0492] The server assigns tasks to each knowledge processing unit based on the analysis results and sentiment evaluations. For example, it might have a data analysis knowledge processing unit analyze sales data and a report generation knowledge processing unit summarize the results.

[0493] Step 6:

[0494] Each knowledge processing unit performs its assigned task and processes the necessary data. Progress is constantly reported to the server.

[0495] Step 7:

[0496] The server displays task progress and sentiment evaluation results on an integrated control surface. Users can visually check the status in real time.

[0497] Step 8:

[0498] Once all processing is complete, the server aggregates the data from the knowledge processing unit and generates a results report for the user. This report includes sentiment-based comments and suggestions.

[0499] Step 9:

[0500] The user reviews the report generated on the terminal and enters any new instructions as needed. The system then restarts processing from step 1.

[0501] (Example 2)

[0502] 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."

[0503] In today's digital environment, advanced integrated management functions and flexible responses that respond to user emotions are required for users to efficiently manage various data processing devices and improve the user experience. However, conventional systems are insufficient in managing the status of individual devices and optimizing interactions that take user emotions into consideration, and therefore do not provide sufficient convenience for users.

[0504] 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.

[0505] In this invention, the server includes means for providing an integrated management function that aggregates and displays the operating status and performance of multiple data processing devices, means for efficiently exchanging and sharing information between data processing devices using an information generation device, and means for analyzing the user's emotional state using an emotion recognition device and adjusting the operation of the data processing devices according to the user's emotions. This enables the user to grasp the status of the data processing devices in real time and to provide optimal interaction and information presentation according to their individual emotions.

[0506] The "integrated management function" is a function that centrally aggregates and displays the operating status and performance of multiple data processing devices, allowing users to intuitively understand the status of each device.

[0507] An "input device" is a device or interface that can receive commands from a user in natural language format.

[0508] An "information generation device" is a system component that is responsible for the process of efficiently exchanging and sharing information between data processing devices and has the function of converting natural language into a system-executable format.

[0509] An "emotion recognition device" is a machine that analyzes a user's emotional state from their voice or text and adjusts the operation of related systems based on that analysis.

[0510] A "data processing system" is a collection of hardware and software designed to perform specific tasks or operations.

[0511] In this invention, users can input tasks in natural language format through a terminal. These tasks are expressed as prompts, such as "Prepare materials for next week's project meeting." The terminal receives this input and sends it to the server.

[0512] The server uses a specific generative AI model to analyze user input and interpret the instructions. This AI model has the ability to convert the input natural language into a machine-executable format. For example, if the analyzed content is "document creation," the appropriate document creation software will be selected, and the task will be made concrete.

[0513] Furthermore, the server incorporates an emotion recognition device that analyzes the user's emotional state. This device grasps the user's emotions through voice input and text analysis, and adjusts the operation of the data processing device accordingly. As a result, flexible interaction tailored to the user's emotional state becomes possible.

[0514] Specifically, if the system detects that a user is experiencing stress, the server simplifies the output information from the data processing device and provides it in a format that is easy for the user to understand. In this way, the present invention functions as a system that streamlines user task management and provides support tailored to the user's emotional state.

[0515] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0516] Step 1:

[0517] The user inputs tasks in natural language format using a terminal. This includes specific prompts such as, "Prepare materials for next week's project meeting." The input text is processed directly as data on the terminal and converted into the format required for the next step.

[0518] Step 2:

[0519] The terminal sends the text data entered by the user to the server. The server receives this input and begins processing it with a generative AI model. This model performs natural language analysis, analyzing the input text and converting it into a command that the system can execute. For example, it might be converted into a specific command such as "create a document."

[0520] Step 3:

[0521] Based on the analysis results, the server assigns tasks to appropriate data processing devices. Specifically, it launches document creation software according to the analyzed instructions, selects the necessary templates, and automatically prepares materials for a meeting. In this process, information obtained from the generative AI model is used to perform data processing.

[0522] Step 4:

[0523] The server is equipped with an emotion recognition device that analyzes the user's emotions. This device takes in text and voice data entered by the user and estimates their emotional state from it. For example, if it is estimated that the user is feeling stressed, the data processing device adjusts its operation and presents the information in a format that is easy for the user to understand.

[0524] Step 5:

[0525] Finally, the server aggregates all processing results and generates a report for the user. This report includes not only the task execution details but also the sentiment analysis results. The report is easy for the user to understand, for example, visually displaying task progress and changes in sentiment.

[0526] (Application Example 2)

[0527] 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."

[0528] In today's living environment, flexible responses that respond to users' emotional states are required. However, conventional systems are insufficient in recognizing user emotions, limiting their ability to improve the user experience. To solve this problem, a system is needed that can appropriately recognize user emotions and utilize that information in real time.

[0529] 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.

[0530] In this invention, the server includes means for estimating the user's emotional state using an emotion recognition engine, means for adjusting the operation of an information processing device based on the emotional state, and means for providing suggestions and feedback based on the user's emotions. This enables the provision of appropriate suggestions and feedback in real time that correspond to the user's emotions, thereby improving the user experience.

[0531] An "information processing device" is a computing device that analyzes input information and generates specific results.

[0532] An "integrated control surface" is an interface that aggregates and visually presents the status and performance of multiple information processing devices.

[0533] An "input / output device" is a device that receives instructions in natural language format from a user and presents the results to the user.

[0534] A "generation processing device" is a device that efficiently exchanges information between information processing devices and generates results.

[0535] "Dynamic allocation of computing resources" refers to the act of appropriately allocating and optimizing computing power according to the workload of an information processing device.

[0536] An "emotion recognition engine" is a device or function that estimates a user's emotional state from their voice or input.

[0537] "Suggestions and feedback" refers to the act of providing appropriate actions or information and responding based on the user's input or status.

[0538] To realize this invention, the system comprises multiple information processing devices and a program that manages them in an integrated manner. The main hardware includes information processing devices, user-operated input / output devices, and an integrated control surface for aggregating and visualizing information. The software needs to include a speech recognition engine, a natural language processing engine, and an emotion recognition engine.

[0539] The server receives instructions from the user in natural language format via input / output devices. These instructions are interpreted by a natural language processing engine and converted into a format that can be executed by the information processing device. The server uses an emotion recognition engine to estimate the user's emotional state and adjusts the operation of the information processing device accordingly. This allows the server to provide the user with suggestions and feedback based on their emotional state.

[0540] As a concrete example, suppose a user inputs the voice command "I want to relax." In this case, the emotion recognition engine analyzes the tone of voice to determine if the user is experiencing stress. Based on this information, the server instructs the information processing unit to take actions such as playing relaxing music and dimming the lights.

[0541] An example of a prompt for a generative AI model is a question like, "The user wants to relax. What kind of entertainment or environmental adjustments should be offered?" In this way, emotion recognition can be utilized to provide flexible responses that are sensitive to the user's emotions.

[0542] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0543] Step 1:

[0544] The user inputs natural language instructions into the input / output device. For example, they might input the voice command "I want to relax." Upon receiving this input, the terminal converts the voice data into text data, making it ready for natural language processing.

[0545] Step 2:

[0546] Text data sent from the terminal is analyzed by a natural language processing engine on the server. Here, the intent of the instruction is interpreted, and the meaning "I want to relax" is extracted. The interpreted data is then converted into an instruction format that can be executed by the information processing device.

[0547] Step 3:

[0548] The server uses an emotion recognition engine to estimate the user's emotional state from their voice tone and expressions. In this step, text and audio data are taken as input, emotion analysis is performed, and the result "the user is stressed" is output.

[0549] Step 4:

[0550] Based on the analyzed emotional state and user intent, the server instructs the information processing unit to take specific actions. For example, it generates commands to play relaxation music or soften the lighting.

[0551] Step 5:

[0552] The information processing device performs actual operations based on commands from the server. For example, it may start a music player and play selected relaxation music, or adjust the brightness of a lighting device.

[0553] Step 6:

[0554] The server aggregates the overall processing results and provides feedback to the user in an easy-to-understand format, either on screen or via audio. Here, it confirms that the user's requested actions have been performed and considers any additional actions that may be necessary.

[0555] Through this series of steps, flexible responses that take user emotions into consideration become possible.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] [Fourth Embodiment]

[0560] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0561] 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.

[0562] 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).

[0563] 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.

[0564] 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.

[0565] 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).

[0566] 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.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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".

[0573] This invention is a system for efficiently managing and optimally operating multiple knowledge processing devices. This system aggregates and displays the operating status and performance of various knowledge processing devices on an integrated control surface, allowing the user to grasp the overall status at a glance.

[0574] The user inputs the task in natural language format via a terminal. This input instruction is sent from the terminal to the server. The server analyzes this instruction using a generation processing unit and distributes it to each knowledge processing unit in the appropriate format.

[0575] The generation and processing unit interprets user instructions using advanced natural language processing technology. Therefore, users can operate the system using everyday language without needing to use specific programming languages ​​or commands.

[0576] Furthermore, the server facilitates effective information exchange between knowledge processing devices. For example, if a knowledge processing device performing a document review requires the results of data analysis, it automatically retrieves the appropriate information from related knowledge processing devices.

[0577] For example, if a user issues a request such as "Prepare a sales analysis report for the end-of-month meeting," the server will have the schedule management knowledge processing unit check the schedule, instruct the document review knowledge processing unit to prepare the relevant documents, and request the data analysis knowledge processing unit to analyze the sales data. Finally, the server will report to the user that the preparations are complete from an integrated control perspective.

[0578] Thus, this invention provides a configuration that centrally manages the operation of a knowledge processing device and can efficiently issue instructions through natural language. As a result, it is possible to significantly improve the efficiency of business operations.

[0579] The following describes the processing flow.

[0580] Step 1:

[0581] Users input tasks using a terminal in natural language format. For example, they might input instructions such as "Prepare materials for next month's sales meeting."

[0582] Step 2:

[0583] The terminal sends the input instructions to the server. During this process, the instructions are converted into a data format and processed to enable efficient communication.

[0584] Step 3:

[0585] The server passes the received instructions to the generation and processing unit, which then analyzes them using a natural language processing engine. It interprets the tasks that make up the instructions and formulates a specific execution plan.

[0586] Step 4:

[0587] The server assigns tasks to appropriate knowledge processing units based on the analysis results from the generation processing units. It generates individual instructions for each unit and efficiently allocates resources.

[0588] Step 5:

[0589] Each knowledge processing device begins executing its assigned task. For example, a scheduling device checks meeting dates, and a document review device prepares necessary materials.

[0590] Step 6:

[0591] The server periodically collects task progress from each knowledge processing unit. This allows it to monitor whether tasks are progressing properly and make adjustments as needed.

[0592] Step 7:

[0593] The server generates user reports based on the aggregated information. Progress information and results are displayed on the integrated control panel, notifying users when tasks are completed.

[0594] Step 8:

[0595] The user reviews the report displayed on the integrated control panel to confirm that the task was completed correctly. If necessary, they can re-enter additional instructions.

[0596] (Example 1)

[0597] 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".

[0598] In information processing systems, a key challenge is to centrally understand and efficiently manage the status and performance of multiple information processing devices. Furthermore, it is essential that users can easily operate the system using natural language and that effective information exchange takes place between the devices. This necessitates a system that can improve operational efficiency while optimally allocating computing resources.

[0599] 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.

[0600] In this invention, the server includes means for providing an integrated control screen that aggregates and displays the status and performance of multiple information processing devices, means for utilizing a generative AI model to analyze user instructions in natural language and convert them into an executable format, and means for dynamically allocating and adjusting computing resources based on the workload of the information processing devices. This enables users to operate intuitively using natural language and realizes efficient information exchange and dynamic resource management among multiple information processing devices.

[0601] An "information processing device" is a general term for a device that has the function of processing data and performing specific tasks.

[0602] An "integrated control screen" is a display interface that centrally displays the status and performance of multiple information processing devices, making management easier.

[0603] "Natural language" refers to language used in everyday life, and is not a specific programming language or command format.

[0604] A "generative AI model" is a model that uses artificial intelligence technology to analyze natural language and interpret instructions.

[0605] "Computational resources" refer to resources such as CPU time, memory, and storage that an information processing device requires to perform a task.

[0606] "Dynamic allocation and adjustment" refers to rearranging computing resources according to time and circumstances to optimize their use.

[0607] This invention is a system for achieving efficient management and operation of information processing devices. It mainly consists of a server, a terminal, and a generative AI model.

[0608] Users instruct tasks using natural language via a terminal. For example, by entering a prompt such as "Prepare a sales analysis report for the end-of-month meeting," the corresponding task for each information processing device is initiated. The terminal is intuitively operable through its user interface, and users do not need to use specific programming languages ​​or complex commands.

[0609] Next, the instructions received from the terminal are sent to the server. The server interprets the instructions using a generative AI model and distributes the commands to multiple information processing devices as needed. The generative AI model utilizes natural language processing technology to convert the instructions into an executable format, thereby enabling efficient interpretation and processing of the information.

[0610] Furthermore, the server manages information exchange between information processing devices and optimizes overall performance by dynamically allocating computing resources based on the workload. For example, if a data analysis device shares data with a separate document creation device, the server manages the entire process, enabling smooth collaboration.

[0611] Thus, the present invention allows users to easily control the entire system using everyday language, efficiently manage the operation of multiple information processing devices, and improve the efficiency of business operations. Furthermore, the specific flow of operations is clearly illustrated through examples of prompt statements, serving as a reference for other users when utilizing this system.

[0612] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0613] Step 1:

[0614] The user enters instructions in natural language using a terminal. A specific prompt might be, "Prepare the sales analysis report for the end-of-month meeting." The entered natural language is sent directly from the terminal to the server.

[0615] Step 2:

[0616] The server passes natural language instructions received from the terminal to a generative AI model. The generative AI model analyzes these natural language instructions and breaks them down into executable tasks. This process involves sentence structure analysis and semantic extraction. Finally, specific instructions for each information processing device, based on the user's intent, are generated.

[0617] Step 3:

[0618] Based on the generated task instructions, the server constructs a set of instructions for each appropriate process and distributes them to the respective information processing devices. For example, it might instruct the data analysis information processing device to analyze sales data and the document creation information processing device to prepare related documents.

[0619] Step 4:

[0620] Each information processing unit receives instructions from the server and performs the necessary data processing and calculations. The data analysis information processing unit aggregates sales data and generates results. The document creation information processing unit prepares a sales analysis report based on these results.

[0621] Step 5:

[0622] The server aggregates the output from each information processing device, prepares the final deliverable, and then reports it to the user via an integrated control screen. This allows the user to confirm that the assigned task has been completed.

[0623] (Application Example 1)

[0624] 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".

[0625] Modern urban management requires the interconnected and integrated management of a wide range of information processing systems. However, effectively controlling the operation of each information processing system and dynamically allocating and adjusting resources presents significant challenges. In particular, there is a lack of automated means for optimizing traffic flow, monitoring urban infrastructure, and analyzing resource consumption. Therefore, there is a need for a system that allows urban managers to intuitively grasp the overall situation and issue instructions efficiently.

[0626] 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.

[0627] In this invention, the server includes means for providing an integrated control screen that aggregates and displays the status and performance of multiple information processing devices; means for assigning tasks to the multiple information processing devices using an input device capable of receiving instructions from a user in natural language format; and means for efficiently exchanging and sharing information among the information processing devices using a generation processing function. This enables urban administrators to automatically optimize traffic flow, urban infrastructure monitoring, and resource consumption analysis using natural language.

[0628] An "integrated control screen" is a screen that displays the status and performance of multiple information processing devices in a single visual interface, allowing users to understand it intuitively.

[0629] An "information processing device" is a device that has the function of receiving specific data and performing a specified task based on that data.

[0630] "Natural language form" refers to the language form that humans use on a daily basis, and is a method of inputting instructions to a system without using a special programming language.

[0631] "Generation processing function" refers to the technology that analyzes input natural language and converts it into a format that can be processed by an information processing device.

[0632] "Workload" refers to the degree of burden on an information processing device for the tasks it is currently performing, and is a factor that serves as a basis for the dynamic allocation of resources.

[0633] "Computational resources" refer to the hardware and software resources necessary for information processing devices to perform their tasks efficiently.

[0634] "Aggregative analysis" is a method of integrating multiple data sets and analyzing them statistically or logically.

[0635] A "results report" is a document or digital data that organizes the results of aggregation and analysis and presents them in a format that is easy for users to understand.

[0636] "Traffic fluidity" is a concept that indicates how smoothly traffic flows within a particular area.

[0637] "Urban infrastructure monitoring" is a process of monitoring the status and operation of urban infrastructure in real time to check for any abnormalities.

[0638] "Resource consumption analysis" is a method for analyzing the consumption patterns of energy and other resources used to find more efficient ways to utilize them.

[0639] To realize this application, the following system and program need to be constructed. The server collects data from multiple information processing devices in real time and displays their status and performance on an integrated control screen. This allows city administrators to intuitively grasp the overall picture of the information.

[0640] Users send instructions for urban management to the system using a natural language input device. These instructions are parsed by a generation processing function and converted into a format that can be processed by an information processing device. This generation processing function uses the OpenAI GPT API as its natural language processing technology.

[0641] The information processing system automatically performs tasks such as optimizing traffic flow, monitoring urban infrastructure, and analyzing resource consumption. The Python Pandas library is used for data analysis. Furthermore, a mobile app developed using React Native allows users to access and control these functions from anywhere.

[0642] As a concrete example, by entering a prompt message such as "Generate an optimal traffic plan considering the increase in traffic volume for the next festival season" for a certain community event, the system analyzes traffic patterns and generates an optimized traffic signal plan. This result is reflected on the integrated control screen and reported to the user.

[0643] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0644] Step 1:

[0645] The user enters instructions in natural language format on the terminal. The prompt message "Generate an optimal traffic plan considering increased traffic volume for the next festival season" is entered. The entered instructions are sent to the server via the terminal.

[0646] Step 2:

[0647] The server analyzes the received natural language instructions using the OpenAI GPT API, a generation processing function. It analyzes the prompt text received as input, converts it into a format that the information processing device can understand, and outputs it.

[0648] Step 3:

[0649] The server distributes the converted instructions to the appropriate information processing unit. Here, an analysis module that processes traffic data is selected. This process takes real-time traffic data as input and applies an optimization algorithm based on that data to generate output.

[0650] Step 4:

[0651] The information processing device generates analysis results and returns them to the server. Specifically, it uses traffic flow data to derive the optimal signal control plan. This result is output and sent back to the server.

[0652] Step 5:

[0653] The server reflects the received analysis results on the integrated control screen and reports them to the user. The generated traffic signal plan is visually displayed on the screen, allowing the user to confirm the optimization results.

[0654] Step 6:

[0655] The user can issue additional instructions as needed from the integrated control screen, attempting further optimization. This process is repeated, with the server continuously processing new instructions each time.

[0656] 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.

[0657] This invention provides a system that improves the user experience by effectively managing multiple knowledge processing devices and combining them with an emotion engine. This system provides an integrated control surface and is configured to visually display the operating status and performance of each knowledge processing device.

[0658] Users can input tasks in natural language format using a terminal. The input information is sent to the server via the terminal and interpreted by a generation processing unit. In this interpretation process, the generation processing unit performs natural language analysis, converts the instructions into a concretely executable format, and assigns the task to the appropriate knowledge processing unit.

[0659] Furthermore, a mechanism for emotion recognition is incorporated into the system. This emotion engine can estimate the user's emotional state from their voice tone and text input. This information helps to adjust the behavior of the knowledge processing unit during task execution, providing personalized suggestions and feedback to the user.

[0660] For example, if the emotion engine recognizes that a user is experiencing stress, the server can instruct the knowledge processing unit to simplify complex tasks or present information in a more user-friendly format.

[0661] Finally, the server aggregates data from all knowledge processing devices and generates a results report in a user-friendly format. The emotion engine's estimations can also be incorporated into this report, allowing users to review their work performance including emotional factors.

[0662] In this way, the present invention utilizes emotion recognition to provide a system that not only manages knowledge processing devices but also enables flexible responses in accordance with the user's emotions.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] The user enters instructions in natural language using a terminal. For example, they might type, "Prepare the latest sales report for next week's meeting."

[0666] Step 2:

[0667] The terminal sends the input instructions to the server and converts the input data into a format suitable for analysis.

[0668] Step 3:

[0669] The server uses a generation and processing unit to analyze the received instructions using natural language processing and understand the content of the task. This analysis includes identifying tasks related to the instructions.

[0670] Step 4:

[0671] The server uses an emotion engine to assess the user's emotions. It estimates emotions from the input voice and text and incorporates the results into processing.

[0672] Step 5:

[0673] The server assigns tasks to each knowledge processing unit based on the analysis results and sentiment evaluations. For example, it might have a data analysis knowledge processing unit analyze sales data and a report generation knowledge processing unit summarize the results.

[0674] Step 6:

[0675] Each knowledge processing unit performs its assigned task and processes the necessary data. Progress is constantly reported to the server.

[0676] Step 7:

[0677] The server displays task progress and sentiment evaluation results on an integrated control surface. Users can visually check the status in real time.

[0678] Step 8:

[0679] Once all processing is complete, the server aggregates the data from the knowledge processing unit and generates a results report for the user. This report includes sentiment-based comments and suggestions.

[0680] Step 9:

[0681] The user reviews the report generated on the terminal and enters any new instructions as needed. The system then restarts processing from step 1.

[0682] (Example 2)

[0683] 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".

[0684] In today's digital environment, advanced integrated management functions and flexible responses that respond to user emotions are required for users to efficiently manage various data processing devices and improve the user experience. However, conventional systems are insufficient in managing the status of individual devices and optimizing interactions that take user emotions into consideration, and therefore do not provide sufficient convenience for users.

[0685] 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.

[0686] In this invention, the server includes means for providing an integrated management function that aggregates and displays the operating status and performance of multiple data processing devices, means for efficiently exchanging and sharing information between data processing devices using an information generation device, and means for analyzing the user's emotional state using an emotion recognition device and adjusting the operation of the data processing devices according to the user's emotions. This enables the user to grasp the status of the data processing devices in real time and to provide optimal interaction and information presentation according to their individual emotions.

[0687] The "integrated management function" is a function that centrally aggregates and displays the operating status and performance of multiple data processing devices, allowing users to intuitively understand the status of each device.

[0688] An "input device" is a device or interface that can receive commands from a user in natural language format.

[0689] An "information generation device" is a system component that is responsible for the process of efficiently exchanging and sharing information between data processing devices and has the function of converting natural language into a system-executable format.

[0690] An "emotion recognition device" is a machine that analyzes a user's emotional state from their voice or text and adjusts the operation of related systems based on that analysis.

[0691] A "data processing system" is a collection of hardware and software designed to perform specific tasks or operations.

[0692] In this invention, users can input tasks in natural language format through a terminal. These tasks are expressed as prompts, such as "Prepare materials for next week's project meeting." The terminal receives this input and sends it to the server.

[0693] The server uses a specific generative AI model to analyze user input and interpret the instructions. This AI model has the ability to convert the input natural language into a machine-executable format. For example, if the analyzed content is "document creation," the appropriate document creation software will be selected, and the task will be made concrete.

[0694] Furthermore, the server incorporates an emotion recognition device that analyzes the user's emotional state. This device grasps the user's emotions through voice input and text analysis, and adjusts the operation of the data processing device accordingly. As a result, flexible interaction tailored to the user's emotional state becomes possible.

[0695] Specifically, if the system detects that a user is experiencing stress, the server simplifies the output information from the data processing device and provides it in a format that is easy for the user to understand. In this way, the present invention functions as a system that streamlines user task management and provides support tailored to the user's emotional state.

[0696] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0697] Step 1:

[0698] The user inputs tasks in natural language format using a terminal. This includes specific prompts such as, "Prepare materials for next week's project meeting." The input text is processed directly as data on the terminal and converted into the format required for the next step.

[0699] Step 2:

[0700] The terminal sends the text data entered by the user to the server. The server receives this input and begins processing it with a generative AI model. This model performs natural language analysis, analyzing the input text and converting it into a command that the system can execute. For example, it might be converted into a specific command such as "create a document."

[0701] Step 3:

[0702] Based on the analysis results, the server assigns tasks to appropriate data processing devices. Specifically, it launches document creation software according to the analyzed instructions, selects the necessary templates, and automatically prepares materials for a meeting. In this process, information obtained from the generative AI model is used to perform data processing.

[0703] Step 4:

[0704] The server is equipped with an emotion recognition device that analyzes the user's emotions. This device takes in text and voice data entered by the user and estimates their emotional state from it. For example, if it is estimated that the user is feeling stressed, the data processing device adjusts its operation and presents the information in a format that is easy for the user to understand.

[0705] Step 5:

[0706] Finally, the server aggregates all processing results and generates a report for the user. This report includes not only the task execution details but also the sentiment analysis results. The report is easy for the user to understand, for example, visually displaying task progress and changes in sentiment.

[0707] (Application Example 2)

[0708] 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".

[0709] In today's living environment, flexible responses that respond to users' emotional states are required. However, conventional systems are insufficient in recognizing user emotions, limiting their ability to improve the user experience. To solve this problem, a system is needed that can appropriately recognize user emotions and utilize that information in real time.

[0710] 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.

[0711] In this invention, the server includes means for estimating the user's emotional state using an emotion recognition engine, means for adjusting the operation of an information processing device based on the emotional state, and means for providing suggestions and feedback based on the user's emotions. This enables the provision of appropriate suggestions and feedback in real time that correspond to the user's emotions, thereby improving the user experience.

[0712] An "information processing device" is a computing device that analyzes input information and generates specific results.

[0713] An "integrated control surface" is an interface that aggregates and visually presents the status and performance of multiple information processing devices.

[0714] An "input / output device" is a device that receives instructions in natural language format from a user and presents the results to the user.

[0715] A "generation processing device" is a device that efficiently exchanges information between information processing devices and generates results.

[0716] "Dynamic allocation of computing resources" refers to the act of appropriately allocating and optimizing computing power according to the workload of an information processing device.

[0717] An "emotion recognition engine" is a device or function that estimates a user's emotional state from their voice or input.

[0718] "Suggestions and feedback" refers to the act of providing appropriate actions or information and responding based on the user's input or status.

[0719] To realize this invention, the system comprises multiple information processing devices and a program that manages them in an integrated manner. The main hardware includes information processing devices, user-operated input / output devices, and an integrated control surface for aggregating and visualizing information. The software needs to include a speech recognition engine, a natural language processing engine, and an emotion recognition engine.

[0720] The server receives instructions from the user in natural language format via input / output devices. These instructions are interpreted by a natural language processing engine and converted into a format that can be executed by the information processing device. The server uses an emotion recognition engine to estimate the user's emotional state and adjusts the operation of the information processing device accordingly. This allows the server to provide the user with suggestions and feedback based on their emotional state.

[0721] As a concrete example, suppose a user inputs the voice command "I want to relax." In this case, the emotion recognition engine analyzes the tone of voice to determine if the user is experiencing stress. Based on this information, the server instructs the information processing unit to take actions such as playing relaxing music and dimming the lights.

[0722] An example of a prompt for a generative AI model is a question like, "The user wants to relax. What kind of entertainment or environmental adjustments should be offered?" In this way, emotion recognition can be utilized to provide flexible responses that are sensitive to the user's emotions.

[0723] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0724] Step 1:

[0725] The user inputs natural language instructions into the input / output device. For example, they might input the voice command "I want to relax." Upon receiving this input, the terminal converts the voice data into text data, making it ready for natural language processing.

[0726] Step 2:

[0727] Text data sent from the terminal is analyzed by a natural language processing engine on the server. Here, the intent of the instruction is interpreted, and the meaning "I want to relax" is extracted. The interpreted data is then converted into an instruction format that can be executed by the information processing device.

[0728] Step 3:

[0729] The server uses an emotion recognition engine to estimate the user's emotional state from their voice tone and expressions. In this step, text and audio data are taken as input, emotion analysis is performed, and the result "the user is stressed" is output.

[0730] Step 4:

[0731] Based on the analyzed emotional state and user intent, the server instructs the information processing unit to take specific actions. For example, it generates commands to play relaxation music or soften the lighting.

[0732] Step 5:

[0733] The information processing device performs actual operations based on commands from the server. For example, it may start a music player and play selected relaxation music, or adjust the brightness of a lighting device.

[0734] Step 6:

[0735] The server aggregates the overall processing results and provides feedback to the user in an easy-to-understand format, either on screen or via audio. Here, it confirms that the user's requested actions have been performed and considers any additional actions that may be necessary.

[0736] Through this series of steps, flexible responses that take user emotions into consideration become possible.

[0737] 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.

[0738] 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.

[0739] 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 robot 414.

[0740] 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.

[0741] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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."

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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 as being incorporated by reference.

[0758] The following is further disclosed regarding the embodiments described above.

[0759] (Claim 1)

[0760] Means for providing an integrated control surface that aggregates and displays the status and performance of multiple knowledge processing devices,

[0761] A means for assigning tasks to the plurality of knowledge processing devices using an input device capable of receiving instructions from the user in natural language format,

[0762] A means for efficiently exchanging and sharing information between the knowledge processing devices using a generation processing device,

[0763] Means for dynamically allocating and adjusting computing resources based on the workload of the knowledge processing device,

[0764] A means for aggregating and analyzing data collected from the aforementioned multiple knowledge processing devices and generating a results report,

[0765] A system that includes this.

[0766] (Claim 2)

[0767] The system according to claim 1, wherein the integrated control surface is configured to visually display the progress of each knowledge processing device so that the user can intuitively understand the overall situation.

[0768] (Claim 3)

[0769] The system according to claim 1, wherein the generation processing device uses natural language interpretation technology to convert user input into a form that can be executed by the knowledge processing device.

[0770] "Example 1"

[0771] (Claim 1)

[0772] A means for providing an integrated control screen that aggregates and displays the status and performance of multiple information processing devices,

[0773] A means for assigning tasks to the multiple information processing devices using an input device capable of receiving instructions from the user in natural language format,

[0774] A means for efficiently exchanging and sharing information between the information processing devices using a generation processing device,

[0775] Means for dynamically allocating and adjusting computing resources based on the workload of the information processing device,

[0776] A means for aggregating and analyzing data collected from the aforementioned multiple information processing devices and generating a report of the results,

[0777] A means of using a generative AI model to analyze user instructions in natural language and convert them into an executable format,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, wherein the integrated control screen is configured to visually display the progress of each information processing device, so that the user can intuitively understand the overall situation.

[0781] (Claim 3)

[0782] The system according to claim 1, wherein the generating AI model uses natural language interpretation technology to convert user input into a form that can be executed by the information processing device.

[0783] "Application Example 1"

[0784] (Claim 1)

[0785] A means for providing an integrated control screen that aggregates and displays the status and performance of multiple information processing devices,

[0786] A means for assigning tasks to the multiple information processing devices using an input device capable of receiving instructions from the user in natural language format,

[0787] A means for efficiently exchanging and sharing information between the information processing devices using a generation processing function,

[0788] Means for dynamically allocating and adjusting computing resources based on the workload of the information processing device,

[0789] A means for aggregating and analyzing data collected from the aforementioned multiple information processing devices and generating a results report,

[0790] A means for analyzing natural language instructions for urban management and automatically optimizing traffic flow, urban infrastructure monitoring, and resource consumption analysis,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, wherein the integrated control screen is configured to visually display the progress of each information processing device, so that the user can intuitively understand the overall situation.

[0794] (Claim 3)

[0795] The system according to claim 1, wherein the generation processing function uses natural language interpretation technology to convert user input into a form that can be executed by the information processing device.

[0796] "Example 2 of combining an emotion engine"

[0797] (Claim 1)

[0798] A means for providing an integrated management function that aggregates and displays the operating status and performance of multiple data processing devices,

[0799] A means for assigning tasks to the multiple data processing devices using an input device capable of receiving commands from the user in natural language format,

[0800] A means for efficiently exchanging and sharing information between data processing devices using an information generation device,

[0801] A means for analyzing the user's emotional state using an emotion recognition device and adjusting the operation of the data processing device according to the user's emotions,

[0802] A means for aggregating and analyzing data collected from the aforementioned multiple data processing devices and generating a results report,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, wherein the integrated management function is configured to visually display the progress status of each data processing device so that the user can intuitively understand the overall situation.

[0806] (Claim 3)

[0807] The information generation device uses natural language interpretation technology to convert user input into a form that can be executed by the data processing device, according to claim 1.

[0808] "Application example 2 when combining with an emotional engine"

[0809] (Claim 1)

[0810] Means for providing an integrated control surface that aggregates and displays the status and performance of multiple information processing devices,

[0811] A means for assigning tasks to the multiple information processing devices using an input / output device capable of receiving instructions from the user in natural language format,

[0812] A means for efficiently exchanging and sharing information between the information processing devices using a generation processing device,

[0813] Means for dynamically allocating and adjusting computing resources based on the workload of the information processing device,

[0814] A means for aggregating and analyzing information collected from the aforementioned multiple information processing devices and generating a results report,

[0815] A means for estimating the user's emotional state using an emotion recognition engine and adjusting the operation of the information processing device based on that emotional state,

[0816] A means for generating actions to provide suggestions and feedback based on user emotions,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein the integrated control surface is configured to visually display the progress of each information processing device, so that the user can intuitively understand the overall situation.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein the generation processing device uses natural language interpretation technology to convert user input into a form that can be executed by the information processing device. [Explanation of Symbols]

[0822] 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. Means for providing an integrated control surface that aggregates and displays the status and performance of multiple knowledge processing devices, A means for assigning tasks to the plurality of knowledge processing devices using an input device capable of receiving instructions from the user in natural language format, A means for efficiently exchanging and sharing information between the knowledge processing devices using a generation processing device, Means for dynamically allocating and adjusting computing resources based on the workload of the knowledge processing device, A means for aggregating and analyzing data collected from the aforementioned multiple knowledge processing devices and generating a results report, A system that includes this.

2. The system according to claim 1, wherein the integrated control surface is configured to visually display the progress of each knowledge processing device so that the user can intuitively understand the overall situation.

3. The system according to claim 1, wherein the generation processing device uses natural language interpretation technology to convert user input into a form that can be executed by the knowledge processing device.

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