system

Abstract

We provide the system. [Solution] A means of receiving instructions from workers via a mobile information terminal, To process the aforementioned instructions, a means for analyzing data using generative artificial intelligence and transmitting the generated results to the operator, Means for visually displaying the generated results on a mobile information terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a manufacturing site, the lack of information technology literacy of employees is an obstacle to promoting digital transformation. As a result, there is a problem that the efficiency of maintenance and production management cannot be sufficiently improved. There is a need to provide means for on-site workers to overcome these problems and improve productivity.

Means for Solving the Problems

[0005] This invention provides a system that receives instructions from workers via a mobile device and processes them using artificial intelligence. This system retrieves relevant data from an equipment database and visually displays the generated results on the mobile device. Furthermore, by providing workers with recommended actions based on the analysis results, it facilitates on-site decision-making and improves productivity.

[0006] A "portable information terminal" refers to an information processing device that is portable, has communication capabilities, and can be operated by the user.

[0007] "Worker" refers to the person or employee who actually performs the work in a manufacturing plant or similar setting.

[0008] "Instructions" refer to commands or requests given by an operator to a system.

[0009] "Generative artificial intelligence" refers to a technology that processes large amounts of data and enables inference and prediction through learning.

[0010] "Data analysis" refers to the process of organizing, comparing, and evaluating received information to derive new insights and information.

[0011] "Generated results" refers to the output produced as a result of the information analyzed by generative artificial intelligence.

[0012] An "equipment database" refers to a database that stores information related to the equipment and facilities used in a manufacturing site.

[0013] "Visual display" refers to showing information on a screen using graphics and text so that users can visually recognize it.

[0014] "Recommended actions" refer to actions or next steps suggested to the user based on the analysis results. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0016] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the signed 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.

[0019] In the following embodiments, the signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed 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, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The system of the present invention allows workers on the factory floor to issue instructions via a portable information terminal, which are then processed using artificial intelligence on a server, and the results are returned to the workers. Specifically, it is implemented in the following manner.

[0037] The user launches a dedicated application on their mobile device and inputs information about a specific task at the work site. For example, they might input something like, "Please check the inspection status of machine A." The device then sends this input to the server.

[0038] Based on the received instructions, the server utilizes generated artificial intelligence to retrieve and analyze necessary information from relevant databases. For example, it analyzes the past inspection history and current operating data of machine A to determine the deterioration status of parts and whether or not there are any abnormalities.

[0039] Once the analysis is complete, the server sends the results back to the user's mobile device. These results are displayed visually as a detailed report. Furthermore, if necessary, specific actions are suggested, such as "We recommend replacing part X this month."

[0040] For example, to determine the need for parts replacement, workers input data, and a server analyzes it and provides immediate feedback, enabling efficient maintenance work. In this way, field workers can smoothly carry out their daily tasks by receiving support from AI generated using mobile devices, without requiring any special knowledge of information technology.

[0041] The implementation of this system will improve work efficiency on the manufacturing floor, enabling proactive troubleshooting and rapid response. Furthermore, the accumulation and utilization of on-site data will accelerate the promotion of digital transformation for further productivity improvements.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user launches a dedicated application on their mobile device, enters their authentication information, and logs in. Upon successful authentication, the main menu is displayed.

[0045] Step 2:

[0046] The user inputs instructions related to the task. For example, they might give voice or text instructions such as "Check the operating status of machine A."

[0047] Step 3:

[0048] The terminal receives user instructions and analyzes them using a natural language processing engine. After analysis, it converts the instructions into a format for sending to the server.

[0049] Step 4:

[0050] The terminal sends the analyzed instruction data to the server. This data includes the user's requests and related information.

[0051] Step 5:

[0052] The server accesses relevant databases based on the received data and retrieves the necessary data. For example, it might retrieve past operating data and maintenance records for machine A.

[0053] Step 6:

[0054] The server receives the acquired data and passes it to an artificial intelligence (AI) that performs data analysis based on its instructions. The AI ​​detects specific patterns and anomalies and generates the necessary insights.

[0055] Step 7:

[0056] The server organizes the analysis results and generates information in a user-friendly format. This includes reports in text and graphic formats.

[0057] Step 8:

[0058] The server sends the generated information to the terminal.

[0059] Step 9:

[0060] The device presents the received information to the user. The user then uses this information to understand the situation and decide on necessary actions.

[0061] Step 10:

[0062] If the user needs more detailed information or wants to give new instructions, they can enter the instructions again and repeat the process.

[0063] (Example 1)

[0064] 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."

[0065] In modern manufacturing environments, it is essential to proactively detect equipment deterioration and malfunctions and perform maintenance efficiently. However, traditional systems often relied on manual information gathering and analysis, making quick decision-making difficult. Furthermore, the need for specialized knowledge made it difficult for on-site workers to make decisions independently, leading to increased reliance on external experts.

[0066] 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.

[0067] In this invention, the server includes means for receiving instructions from an operator via a portable computer, means for analyzing information using a generative intelligence system and transmitting the generated results to the operator, and means for visually displaying the generated results on the portable computer. This enables field workers to efficiently monitor the status of equipment and take necessary actions quickly without requiring special knowledge.

[0068] A "portable computing device" is a portable computing device used by users to input, send, and receive information.

[0069] An "operator" refers to a worker who uses a system to provide specific instructions and receives the results.

[0070] An "instruction" refers to the content of a request or operation that an operator makes to a system.

[0071] A "generative intelligence system" is a system that uses artificial intelligence technology to perform data analysis based on received information and generate results.

[0072] "Information" refers to data sent to the server and data sets obtained from databases used for analysis.

[0073] "Analysis" is the process of processing and interpreting data to derive specific conclusions or results.

[0074] "Results" refers to the collective output generated based on analysis by a generative intelligence system.

[0075] This invention provides a system that enables workers in a manufacturing environment to use a portable computing device to process specific instructions using a generative intelligence system and obtain results quickly.

[0076] Users input instructions at the worksite using a dedicated application installed on a portable calculator. This device can be easily operated via a touch panel or voice input. For example, it is possible to input requests such as, "Please check the inspection status of machine A."

[0077] The terminal receives user input and transmits it to the server via the network. The server consists of high-performance computing equipment and can communicate with various databases via the network connection. The server has a generative AI model implemented on it, which acquires and analyzes data based on the received instructions.

[0078] The server extracts data from the database, such as past inspection history and current operating data for machine A, and performs analysis to determine its deterioration status and whether there are any abnormalities. A generative intelligence system is used for the analysis, performing pattern recognition and predictive analysis of the data using advanced algorithms.

[0079] Once the analysis is complete, the server returns the results to the user. This information is displayed as visual graphics and reports on the portable computing device, making it easy for the operator to understand. Furthermore, based on the analysis results, specific actions such as "replace part X before the next maintenance" can be suggested.

[0080] For example, if a user enters "Please check the condition of the conveyor belt of machine A" into the terminal, the server analyzes this information and immediately provides specific instructions such as "The conveyor belt must be replaced within three months."

[0081] An example of a prompt message that might be input to the generating AI model is, "Analyze the current state of machine A and past inspection data, evaluate its deterioration, and propose the next action." Based on this prompt, the server can perform the necessary analysis and present accurate results to the user.

[0082] This system allows on-site workers to efficiently manage and maintain equipment without requiring special technical knowledge or the intervention of external experts.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user launches a dedicated application for the portable calculator and inputs the task they want to check at the work site. This task input can be done via text or voice. The user inputs instructions such as, "Please check the inspection status of machine A." This input data is then sent to the next process.

[0086] Step 2:

[0087] The terminal receives instructions from the user, encrypts them, and sends them to the server over the network. Information security is a key consideration here, ensuring that data is safely transmitted to the server without being leaked externally. The input is the instruction data, and the output is the data sent to the server.

[0088] Step 3:

[0089] The server retrieves necessary information from relevant databases based on instruction data received from the terminal. The server executes database queries such as "Inspection history of machine A and its parts" and "Current operating data" to extract the required data. The input is instruction data, and the output is relevant data. In this process, the server uses a high-speed processing algorithm to efficiently retrieve the data.

[0090] Step 4:

[0091] The server inputs the acquired data into a generating AI model for data analysis. This analysis predicts the deterioration status and abnormalities of parts based on the acquired operational data and inspection history. The input is related data, and the output is the analysis results. Here, the advanced algorithms of the generating AI model are utilized to identify parts that are highly likely to deteriorate.

[0092] Step 5:

[0093] The server generates a visual report based on the analysis results and sends it to the terminal. This report includes specific recommendations, such as "replace part X before the next maintenance." The input is the analysis results, and the output is the report data. The report is formatted in graphs and charts to make it easy to understand visually.

[0094] Step 6:

[0095] The terminal receives reports from the server and displays them on the user's application screen. The user can review these reports and take necessary actions quickly. Input is the report data, and output is the displayed content. The terminal provides an intuitive interface, allowing the user to easily decide on their next action.

[0096] (Application Example 1)

[0097] 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."

[0098] In industrial settings, preventing equipment and facility failures and performing maintenance efficiently is crucial. Conventional systems struggled to immediately detect signs of failure or abnormality, relying heavily on the experience of skilled technicians. This resulted in decreased production efficiency and unexpected downtime. This invention aims to solve these problems and provide a system that enables faster and more efficient maintenance without burdening on-site operators.

[0099] 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.

[0100] In this invention, the server includes means for receiving instructions from an operator via a mobile information terminal, means for analyzing information using generative artificial intelligence to process the instructions and transmitting the generated results to the operator, and means for providing the next recommended maintenance action based on the analysis results. This enables the operator to perform appropriate maintenance quickly and improve production efficiency without relying on special expertise.

[0101] A "portable information terminal" is an information processing device that can be carried by a worker and allows for the input of instructions and the display of results.

[0102] "Operator" refers to a person who performs maintenance and monitoring of equipment and facilities on-site.

[0103] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to analyze information and make decisions based on the input.

[0104] "Analyzing information" refers to the process of deriving some kind of insight or result based on collected data.

[0105] "Sending the generated results" refers to the process of delivering the judgments and suggestions obtained through analysis to the inputter.

[0106] "Recommended maintenance actions" refer to specific action guidelines proposed for the maintenance and preservation of equipment and machinery based on the analysis results.

[0107] "Based on analysis results" means deciding on the next steps or actions based on the results of information analysis.

[0108] The system implementing this invention provides a means for operators to efficiently manage equipment maintenance tasks in factories and manufacturing sites using a portable information terminal. The operator inputs specific instructions via the portable information terminal, and these instructions are transmitted to a server via data communication.

[0109] The server uses a generated AI model to analyze historical maintenance data and real-time operational data of equipment and devices. Leading AI frameworks such as TENSORFLOW® and PyTorch can be used for the analysis. The analyzed data forms the basis for determining the equipment's condition and potential anomalies. Based on the analysis results, the server recommends appropriate maintenance actions to the operators.

[0110] The results and recommendations generated by the server are sent to a mobile device and displayed in a report format that operators can visually review. This system enables operators to perform equipment maintenance efficiently and quickly without relying on specialized knowledge.

[0111] A concrete example is the maintenance management of dough forming equipment in a bread factory. When an operator inputs a command into a terminal, such as "Check the current status of the dough forming equipment," the system analyzes the command and provides a recommendation, such as "This equipment will require lubrication maintenance in three months." This helps prevent equipment failures and minimizes line downtime.

[0112] Furthermore, the following example of a prompt can be applied to effectively analyze a generative AI model: "Analyze the maintenance status of the dough forming machine and suggest the next necessary actions." This prompt clearly indicates what kind of judgment is required of the generative AI and helps to obtain appropriate results.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user uses a mobile device to input specific instructions regarding equipment maintenance. The input is in text format and includes prompts such as "Check the current status of the dough forming machine." This input is performed using the form input function on the mobile device.

[0116] Step 2:

[0117] The terminal sends the input instructions to the server. Protocols such as HTTP and WebSocket are used for data communication. The data output from the terminal includes the input instructions. Specifically, this step involves packetizing the instructions and sending the data over the network to the server's analysis process.

[0118] Step 3:

[0119] The server analyzes the received instructions and activates a generative artificial intelligence model. The AI ​​model uses TensorFlow or PyTorch to process relevant equipment data based on the instructions. At this stage, the input is user instruction data, and the output is predictions and judgments about the equipment's status. Specifically, this analysis generates recommended actions such as, "The next maintenance should be performed in three months."

[0120] Step 4:

[0121] The server sends the analysis results to the mobile device. The input here is the previously generated analysis result, and the output is a visual display of the results on the device. The data is sent to the device in a highly readable report format.

[0122] Step 5:

[0123] The terminal displays the received results to the user in a visual format. The output is visualized on the terminal's display as graphs and text information. Specifically, the terminal displays the analysis results in tabular or Gantt chart format, making it easy for the user to understand the next maintenance action to take.

[0124] 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.

[0125] This invention is a system for supporting the production activities of workers in a manufacturing site. It receives instructions from workers via a mobile information terminal and incorporates an emotion recognition engine to provide feedback according to the user's emotional state.

[0126] Users launch a dedicated application using a mobile device and input instructions for their work via voice or text. At this time, the device uses its built-in emotion recognition engine to estimate the user's emotions from their voice and input patterns. This process detects whether the worker is experiencing anxiety or stress.

[0127] The device sends data about the user's emotional state to the server along with instructions. The server uses generative artificial intelligence to analyze the data based on the user's instructions and generate optimized results that take the user's emotional state into account. Such results provide even better decision-making support in specific situations.

[0128] For example, if a worker is under stress, the system can provide information with simplified procedures and special notes. This allows the worker to quickly access the necessary information without confusion.

[0129] The results generated by the server are sent back to the terminal and presented to the user visually as graphics and text. At this time, the terminal can customize the results based on the user's emotional state and adjust the color scheme and interface to reduce visual stress.

[0130] This allows users to receive information in a format best suited to their current situation and take action as needed. For example, a worker who is feeling stressed just before replacing a part could be presented with simplified instructions, enabling them to respond appropriately in a shorter amount of time.

[0131] This embodiment can prevent unexpected productivity drops and errors, and promote efficient work in the manufacturing environment. By integrating with an emotion engine, it is possible to achieve digital transformation that is more in line with human intuition, and to enhance the convenience and flexibility of information technology in the workplace.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user launches a dedicated application on their mobile device, enters their authentication information, and logs into the system. The application verifies the login information and displays the main menu.

[0135] Step 2:

[0136] Users input work-related instructions into the terminal via voice or text. This input information is temporarily stored within the terminal to clarify the worker's intent.

[0137] Step 3:

[0138] The device processes the input instructions and uses its built-in emotion recognition engine to analyze the user's current emotional state. Specifically, it determines stress and anxiety from factors such as voice tone and input speed.

[0139] Step 4:

[0140] The device sends the received instructions and emotional state data to the server. Here, a package is formed containing the instructions and user emotional metrics.

[0141] Step 5:

[0142] The server analyzes the instructions and uses generative artificial intelligence to retrieve the necessary information from relevant databases. Simultaneously, it optimizes data prioritization and processing methods based on sentiment metrics.

[0143] Step 6:

[0144] Based on the analysis results generated by the server, customized information tailored to the user's emotions is constructed. For example, a simple instruction manual is generated for highly anxious users, while a detailed document is generated for calm users.

[0145] Step 7:

[0146] The server generates optimization information and sends it to the terminal. The information sent includes visually adjusted content.

[0147] Step 8:

[0148] The device provides the user with the information it receives. The device adjusts the interface's color scheme and layout according to the user's emotional state, presenting it in a user-friendly format.

[0149] Step 9:

[0150] The user performs the task based on the information provided, giving additional instructions or asking new questions as needed. This completes the work cycle.

[0151] (Example 2)

[0152] 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".

[0153] In manufacturing environments, it is essential to respond quickly and accurately to worker instructions while also providing support that takes into account the worker's emotional state. However, conventional systems have struggled to integrate the processing of work instructions with feedback based on the worker's emotional state, leading to challenges such as decreased work efficiency and increased stress.

[0154] 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.

[0155] In this invention, the server includes means for receiving instructions from a worker via an information processing device, means for using an emotion recognition engine to recognize the worker's emotional state, and means for analyzing data using generative artificial intelligence based on the instructions and emotional state to generate optimized results. This makes it possible to provide information optimized according to the worker's emotional state, thereby improving work efficiency and reducing stress.

[0156] An "information processing device" is a device used for inputting, processing, and outputting data, and has the function of receiving and processing instructions from an operator.

[0157] An "emotion recognition engine" is a software or hardware component that estimates and analyzes a user's emotional state from their voice or text data.

[0158] "Generative artificial intelligence" is an artificial intelligence technology that can analyze data and automatically generate optimized results to support user decision-making.

[0159] "Optimized results" refer to the output of data processing that has been adjusted to provide more effective and efficient work support, taking into account relevant information and emotional states.

[0160] "Visual display" refers to a method of presenting processed information or results to the user in an easily understandable way using a display or similar device.

[0161] In implementing this invention, the user first uses a dedicated application installed on a mobile device. The user inputs instructions via voice or text to provide work instructions. The device has an emotion recognition engine that estimates the user's emotional state from their voice and input patterns. This engine analyzes the user's emotional state in real time, and the device sends the data to a server based on the results.

[0162] The server analyzes data received via the information processing device using a generation AI model. This takes into account the user's instructions and emotional state to generate optimized results. These generated results are used to provide the worker with the necessary information and instructions. For example, if the worker is feeling stressed, the server can simplify the work procedure.

[0163] The results generated on the server are sent back to the terminal and presented to the user visually. At this point, the terminal can adjust the display format based on the user's emotional state and optimize the interface design so that the user can receive the information without stress.

[0164] As a concrete example, consider a scenario where the terminal receives a voice command from the user, such as "Confirm the next task," and detects signs of anxiety. In this case, the server determines that "the procedure should be simplified to alleviate anxiety" and sends instructions based on that decision to the terminal. The terminal then presents this information to the user using calming colors to support the worker's next action.

[0165] A concrete example of a prompt message would be, "Please consider ways to simplify the parts replacement procedure presented when the user's stress level is high." This invention will improve work efficiency and reduce worker stress at the worksite.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The user launches the application using a mobile device and inputs work instructions. The input data is in either voice or text format. This input data is sent to the emotion recognition engine built into the device. For example, the worker might verbally say, "Tell me the procedure for replacing part A."

[0169] Step 2:

[0170] The device uses an emotion recognition engine to analyze the emotional state of input voice and text. Here, voice data is analyzed by a speech processing algorithm, and text data by a pattern recognition algorithm, to estimate the user's emotional state. If the emotional state is determined to be "tension," the engine processes this information as data. The output is the estimated emotional state.

[0171] Step 3:

[0172] The terminal transmits user instructions and estimated emotional state data to the server. The input consists of user-provided instruction data and emotional state data. Specifically, the terminal transmits "the procedure for replacing part A and the user's tension level" to the server using a secure communication method.

[0173] Step 4:

[0174] The server analyzes the received data using a generative AI model. The input consists of user instructions and emotional state data. In this process, the AI ​​integrates the data and simulates various options to determine the most appropriate action for the instructions. The output is an improved work procedure, for example, a simplified procedure.

[0175] Step 5:

[0176] The server sends back the feedback data generated as a result of the optimization to the terminal. The input is the work procedure determined by the AI. Specifically, the server sends the "simplified procedure" to the terminal.

[0177] Step 6:

[0178] The terminal presents the user with feedback received from the server. The input is feedback data sent from the server. Based on this data, the terminal adjusts the interface display according to the user's emotional state. The output is a visual presentation, showing the user a shortened parts replacement procedure in a calming color scheme.

[0179] (Application Example 2)

[0180] 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".

[0181] In modern manufacturing environments, workers' emotional states can affect productivity and accuracy. However, traditional systems have struggled to provide flexible responses that take workers' emotions into account, as well as appropriate feedback. Furthermore, even in the operation of automated machinery, the lack of support to reduce worker stress and anxiety has made it difficult to achieve effective production activities.

[0182] 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.

[0183] In this invention, the server includes means for receiving instructions from the user via a mobile information terminal, means for analyzing data using an artificial intelligence engine to generate optimized results that take into account the user's emotional state, means for visually displaying the generated results and adjusting the interface based on the emotional state, and means for transmitting instructions to enable coordination with automated machinery. This makes it possible to provide optimal feedback that responds to the user's emotions and improve the productivity of workers.

[0184] A "portable information terminal" is an information processing device that a user can carry with them, capable of inputting and displaying data.

[0185] "User" refers to an individual or person responsible for performing tasks using this system.

[0186] An "instruction" is an operational command or request that a user gives to a system.

[0187] An "artificial intelligence engine" is a system element in which a computer simulates human cognitive abilities, analyzes data, and derives optimal results.

[0188] "Analyzing data" refers to the methods used to process collected information and extract meaningful insights.

[0189] "Emotional state" refers to the emotional state the user is experiencing at that particular time.

[0190] "Generating optimized results" is the process of creating the most appropriate feedback and instructions based on the data obtained and the user's emotions.

[0191] "Visually displaying" means presenting information in a way that users can recognize visually.

[0192] "Adjusting the interface" means setting up a user-friendly screen layout and operating system based on the user's emotions and circumstances.

[0193] An "automated machine" is a machine or equipment that is set up to perform some or all of a task automatically.

[0194] "Interlocking" refers to the process where multiple elements function in conjunction with each other and operate in a coordinated manner.

[0195] A "database" is a collection of data that is systematically gathered, stored, and made accessible.

[0196] To implement this invention, the server receives instructions from the user via a mobile device. The received instructions include text input and voice input, and accordingly, an artificial intelligence engine is incorporated to analyze the user's emotional state. The artificial intelligence engine analyzes emotions from the input voice and text data, retrieves relevant information in conjunction with a database, and performs the necessary processing.

[0197] The server takes the user's emotional state into consideration and uses a generative AI model to generate optimal feedback and operational instructions based on the user's requests. These generated results are visually displayed on the user's mobile device, and the interface is adjusted according to the user's emotional state.

[0198] After the user confirms the information on their mobile device, instructions are sent to automated machinery as needed, and the relevant tasks begin. This process allows users to receive information in a way that is optimal to their emotional state and circumstances, enabling them to work quickly and effectively.

[0199] As a concrete example, when a machine operator verbally instructs that "a part is not working properly," the terminal recognizes the user's level of anxiety, and the server uses an AI model to generate a simplified procedural guide, adjusting the colors and layout before displaying it on the terminal. An example of a prompt message would be as follows:

[0200] Example of a prompt:

[0201] "When users are feeling stressed, simplify the assembly process and provide clear, easy-to-understand instructions. The instructions should be short and concise, based on the following guidelines. Strive to enhance the user's sense of security: {User Instructions}"

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The terminal receives instructions from the user in the form of voice or text data. These instructions serve as the starting point for the system's processing. The input data is either voice or text, and the terminal prepares it for passing on to the emotion recognition engine.

[0205] Step 2:

[0206] The device sends voice or text data to an emotion recognition engine to analyze the user's emotional state. The input is voice or text data, and the output is data indicating the user's emotions. The emotion recognition engine analyzes the tone of voice and word choice to infer emotional states such as stress, relief, or confusion.

[0207] Step 3:

[0208] The server receives instructions and sentiment data sent from the terminal and uses a generative AI model to generate user-optimized feedback. Input includes instruction data and sentiment data, and output is customized feedback based on the sentiment. Based on this prompt, the AI ​​creates actionable instructions or simplified procedures.

[0209] Step 4:

[0210] The server sends the generated feedback to the terminal, which then displays it visually to the user. The input is user-optimized feedback, and the output is a visually adjusted information display. Specifically, the interface layout and color scheme are adjusted to take the user's emotions into consideration.

[0211] Step 5:

[0212] The user makes necessary decisions based on the displayed feedback and sends instructions from the terminal to the automated machine. This automatically starts or adjusts the work. The input is the user's action, and the output is the instruction to start the machine's operation. Finally, the machine operates according to the new instructions.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] The system of the present invention allows workers on the factory floor to issue instructions via a portable information terminal, which are then processed using artificial intelligence on a server, and the results are returned to the workers. Specifically, it is implemented in the following manner.

[0230] The user launches a dedicated application on their mobile device and inputs information about a specific task at the work site. For example, they might input something like, "Please check the inspection status of machine A." The device then sends this input to the server.

[0231] Based on the received instructions, the server utilizes generated artificial intelligence to retrieve and analyze necessary information from relevant databases. For example, it analyzes the past inspection history and current operating data of machine A to determine the deterioration status of parts and whether or not there are any abnormalities.

[0232] Once the analysis is complete, the server sends the results back to the user's mobile device. These results are displayed visually as a detailed report. Furthermore, if necessary, specific actions are suggested, such as "We recommend replacing part X this month."

[0233] For example, to determine the need for parts replacement, workers input data, and a server analyzes it and provides immediate feedback, enabling efficient maintenance work. In this way, field workers can smoothly carry out their daily tasks by receiving support from AI generated using mobile devices, without requiring any special knowledge of information technology.

[0234] The implementation of this system will improve work efficiency on the manufacturing floor, enabling proactive troubleshooting and rapid response. Furthermore, the accumulation and utilization of on-site data will accelerate the promotion of digital transformation for further productivity improvements.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The user launches a dedicated application on their mobile device, enters their authentication information, and logs in. Upon successful authentication, the main menu is displayed.

[0238] Step 2:

[0239] The user inputs instructions related to the task. For example, they might give voice or text instructions such as "Check the operating status of machine A."

[0240] Step 3:

[0241] The terminal receives user instructions and analyzes them using a natural language processing engine. After analysis, it converts the instructions into a format for sending to the server.

[0242] Step 4:

[0243] The terminal sends the analyzed instruction data to the server. This data includes the user's requests and related information.

[0244] Step 5:

[0245] The server accesses relevant databases based on the received data and retrieves the necessary data. For example, it might retrieve past operating data and maintenance records for machine A.

[0246] Step 6:

[0247] The server receives the acquired data and passes it to an artificial intelligence (AI) that performs data analysis based on its instructions. The AI ​​detects specific patterns and anomalies and generates the necessary insights.

[0248] Step 7:

[0249] The server organizes the analysis results and generates information in a user-friendly format. This includes reports in text and graphic formats.

[0250] Step 8:

[0251] The server sends the generated information to the terminal.

[0252] Step 9:

[0253] The device presents the received information to the user. The user then uses this information to understand the situation and decide on necessary actions.

[0254] Step 10:

[0255] If the user needs more detailed information or wants to give new instructions, they can enter the instructions again and repeat the process.

[0256] (Example 1)

[0257] 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."

[0258] In modern manufacturing environments, it is essential to proactively detect equipment deterioration and malfunctions and perform maintenance efficiently. However, traditional systems often relied on manual information gathering and analysis, making quick decision-making difficult. Furthermore, the need for specialized knowledge made it difficult for on-site workers to make decisions independently, leading to increased reliance on external experts.

[0259] 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.

[0260] In this invention, the server includes means for receiving instructions from an operator via a portable computer, means for analyzing information using a generative intelligence system and transmitting the generated results to the operator, and means for visually displaying the generated results on the portable computer. This enables field workers to efficiently monitor the status of equipment and take necessary actions quickly without requiring special knowledge.

[0261] A "portable computing device" is a portable computing device used by users to input, send, and receive information.

[0262] An "operator" refers to a worker who uses a system to provide specific instructions and receives the results.

[0263] An "instruction" refers to the content of a request or operation that an operator makes to a system.

[0264] A "generative intelligence system" is a system that uses artificial intelligence technology to perform data analysis based on received information and generate results.

[0265] "Information" refers to data sent to the server and data sets obtained from databases used for analysis.

[0266] "Analysis" is the process of processing and interpreting data to derive specific conclusions or results.

[0267] "Results" refers to the collective output generated based on analysis by a generative intelligence system.

[0268] This invention provides a system that enables workers in a manufacturing environment to use a portable computing device to process specific instructions using a generative intelligence system and obtain results quickly.

[0269] Users input instructions at the worksite using a dedicated application installed on a portable calculator. This device can be easily operated via a touch panel or voice input. For example, it is possible to input requests such as, "Please check the inspection status of machine A."

[0270] The terminal receives user input and transmits it to the server via the network. The server consists of high-performance computing equipment and can communicate with various databases via the network connection. The server has a generative AI model implemented on it, which acquires and analyzes data based on the received instructions.

[0271] The server extracts data from the database, such as past inspection history and current operating data for machine A, and performs analysis to determine its deterioration status and whether there are any abnormalities. A generative intelligence system is used for the analysis, performing pattern recognition and predictive analysis of the data using advanced algorithms.

[0272] Once the analysis is complete, the server returns the results to the user. This information is displayed as visual graphics and reports on the portable computing device, making it easy for the operator to understand. Furthermore, based on the analysis results, specific actions such as "replace part X before the next maintenance" can be suggested.

[0273] For example, if a user enters "Please check the condition of the conveyor belt of machine A" into the terminal, the server analyzes this information and immediately provides specific instructions such as "The conveyor belt must be replaced within three months."

[0274] An example of a prompt message that might be input to the generating AI model is, "Analyze the current state of machine A and past inspection data, evaluate its deterioration, and propose the next action." Based on this prompt, the server can perform the necessary analysis and present accurate results to the user.

[0275] This system allows on-site workers to efficiently manage and maintain equipment without requiring special technical knowledge or the intervention of external experts.

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The user launches a dedicated application for the portable calculator and inputs the task they want to check at the work site. This task input can be done via text or voice. The user inputs instructions such as, "Please check the inspection status of machine A." This input data is then sent to the next process.

[0279] Step 2:

[0280] The terminal receives the instruction input by the user, encrypts it, and transmits it to the server via the network. Here, considering information security, the data is safely passed to the server without leakage to the outside. The input is the data of the instruction content, and the output is the transmission data to the server.

[0281] Step 3:

[0282] Based on the instruction data received from the terminal, the server retrieves the necessary information from the relevant database. The server executes database queries such as "inspection history of Machine A and its parts" and "current operating data" to extract the required data. The input is the instruction data, and the output is the relevant data. At this time, the server uses a high-speed processing algorithm to efficiently retrieve the data.

[0283] Step 4:

[0284] The server inputs the retrieved data into the generative AI model for data analysis. In data analysis, based on the retrieved operating data and inspection history, the degradation status and abnormalities of the parts are predicted. The input is the relevant data, and the output is the analysis result. Here, the advanced algorithm of the generative AI model is utilized to identify parts with a high likelihood of degradation.

[0285] Step 5:

[0286] Based on the analysis result, the server generates a visual report and transmits it to the terminal. This report includes specific recommendations, such as actions like "Replace part X by the next maintenance". The input is the analysis result, and the output is the report data. At this time, the report is formatted in a visual and easy-to-understand manner in the form of graphs and charts.

[0287] Step 6:

[0288] The terminal receives reports from the server and displays them on the user's application screen. The user can review these reports and take necessary actions quickly. Input is the report data, and output is the displayed content. The terminal provides an intuitive interface, allowing the user to easily decide on their next action.

[0289] (Application Example 1)

[0290] 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."

[0291] In industrial settings, preventing equipment and facility failures and performing maintenance efficiently is crucial. Conventional systems struggled to immediately detect signs of failure or abnormality, relying heavily on the experience of skilled technicians. This resulted in decreased production efficiency and unexpected downtime. This invention aims to solve these problems and provide a system that enables faster and more efficient maintenance without burdening on-site operators.

[0292] 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.

[0293] In this invention, the server includes means for receiving instructions from an operator via a mobile information terminal, means for analyzing information using generative artificial intelligence to process the instructions and transmitting the generated results to the operator, and means for providing the next recommended maintenance action based on the analysis results. This enables the operator to perform appropriate maintenance quickly and improve production efficiency without relying on special expertise.

[0294] A "portable information terminal" is an information processing device that can be carried by a worker and allows for the input of instructions and the display of results.

[0295] "Operator" refers to a person who performs maintenance and monitoring of equipment and facilities on-site.

[0296] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to analyze information and make decisions based on the input.

[0297] "Analyzing information" refers to the process of deriving some kind of insight or result based on collected data.

[0298] "Sending the generated results" refers to the process of delivering the judgments and suggestions obtained through analysis to the inputter.

[0299] "Recommended maintenance actions" refer to specific action guidelines proposed for the maintenance and preservation of equipment and machinery based on the analysis results.

[0300] "Based on analysis results" means deciding on the next steps or actions based on the results of information analysis.

[0301] The system implementing this invention provides a means for operators to efficiently manage equipment maintenance tasks in factories and manufacturing sites using a portable information terminal. The operator inputs specific instructions via the portable information terminal, and these instructions are transmitted to a server via data communication.

[0302] The server uses a generative AI model to analyze historical maintenance data and real-time operational data of equipment and devices. Leading AI frameworks such as TensorFlow and PyTorch can be used for the analysis. The analyzed data forms the basis for determining the equipment's condition and potential anomalies. Based on the analysis results, the server recommends appropriate maintenance actions to operators.

[0303] The results and recommendations generated by the server are sent to the mobile information terminal and displayed in a report format that can be visually confirmed by the operator. With this system, the operator can perform equipment maintenance efficiently and quickly without relying on special expertise.

[0304] As a specific example, maintenance management of the dough forming device in a bread factory can be cited. When the operator inputs an instruction such as "Check the current state of the dough forming device" into the terminal, the system analyzes based on that instruction and provides a recommendation that "This device will require lubrication maintenance in the next three months." This can prevent device failures and minimize line stoppage time.

[0305] Also, as an example of a prompt sentence for effectively analyzing the generated AI model, the following can be applied: "Analyze the maintenance status of the dough forming device and propose the next necessary actions." This prompt clearly indicates what kind of judgment is required for the generated AI and helps to obtain appropriate results.

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The user uses the mobile information terminal to input specific instructions regarding equipment maintenance. The input is in text format and a prompt sentence such as "Check the current state of the dough forming device" is described. This input is carried out using the form input function on the mobile information terminal.

[0309] Step 2:

[0310] The terminal sends the input instructions to the server. Protocols such as HTTP and WebSocket are used for data communication. The data output from the terminal includes the input instructions. Specifically, this step involves packetizing the instructions and sending the data over the network to the server's analysis process.

[0311] Step 3:

[0312] The server analyzes the received instructions and activates a generative artificial intelligence model. The AI ​​model uses TensorFlow or PyTorch to process relevant equipment data based on the instructions. At this stage, the input is user instruction data, and the output is predictions and judgments about the equipment's status. Specifically, this analysis generates recommended actions such as, "The next maintenance should be performed in three months."

[0313] Step 4:

[0314] The server sends the analysis results to the mobile device. The input here is the previously generated analysis result, and the output is a visual display of the results on the device. The data is sent to the device in a highly readable report format.

[0315] Step 5:

[0316] The terminal displays the received results to the user in a visual format. The output is visualized on the terminal's display as graphs and text information. Specifically, the terminal displays the analysis results in tabular or Gantt chart format, making it easy for the user to understand the next maintenance action to take.

[0317] 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.

[0318] This invention is a system for supporting the production activities of workers in a manufacturing site. It receives instructions from workers via a mobile information terminal and incorporates an emotion recognition engine to provide feedback according to the user's emotional state.

[0319] Users launch a dedicated application using a mobile device and input instructions for their work via voice or text. At this time, the device uses its built-in emotion recognition engine to estimate the user's emotions from their voice and input patterns. This process detects whether the worker is experiencing anxiety or stress.

[0320] The device sends data about the user's emotional state to the server along with instructions. The server uses generative artificial intelligence to analyze the data based on the user's instructions and generate optimized results that take the user's emotional state into account. Such results provide even better decision-making support in specific situations.

[0321] For example, if a worker is under stress, the system can provide information with simplified procedures and special notes. This allows the worker to quickly access the necessary information without confusion.

[0322] The results generated by the server are sent back to the terminal and presented to the user visually as graphics and text. At this time, the terminal can customize the results based on the user's emotional state and adjust the color scheme and interface to reduce visual stress.

[0323] This allows users to receive information in a format best suited to their current situation and take action as needed. For example, a worker who is feeling stressed just before replacing a part could be presented with simplified instructions, enabling them to respond appropriately in a shorter amount of time.

[0324] This embodiment can prevent unexpected productivity drops and errors, and promote efficient work in the manufacturing environment. By integrating with an emotion engine, it is possible to achieve digital transformation that is more in line with human intuition, and to enhance the convenience and flexibility of information technology in the workplace.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The user launches a dedicated application on their mobile device, enters their authentication information, and logs into the system. The application verifies the login information and displays the main menu.

[0328] Step 2:

[0329] Users input work-related instructions into the terminal via voice or text. This input information is temporarily stored within the terminal to clarify the worker's intent.

[0330] Step 3:

[0331] The device processes the input instructions and uses its built-in emotion recognition engine to analyze the user's current emotional state. Specifically, it determines stress and anxiety from factors such as voice tone and input speed.

[0332] Step 4:

[0333] The device sends the received instructions and emotional state data to the server. Here, a package is formed containing the instructions and user emotional metrics.

[0334] Step 5:

[0335] The server analyzes the instructions and uses generative artificial intelligence to retrieve the necessary information from relevant databases. Simultaneously, it optimizes data prioritization and processing methods based on sentiment metrics.

[0336] Step 6:

[0337] Based on the analysis results generated by the server, customized information tailored to the user's emotions is constructed. For example, a simple instruction manual is generated for highly anxious users, while a detailed document is generated for calm users.

[0338] Step 7:

[0339] The server generates optimization information and sends it to the terminal. The information sent includes visually adjusted content.

[0340] Step 8:

[0341] The device provides the user with the information it receives. The device adjusts the interface's color scheme and layout according to the user's emotional state, presenting it in a user-friendly format.

[0342] Step 9:

[0343] The user performs the task based on the information provided, giving additional instructions or asking new questions as needed. This completes the work cycle.

[0344] (Example 2)

[0345] 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".

[0346] In manufacturing environments, it is essential to respond quickly and accurately to worker instructions while also providing support that takes into account the worker's emotional state. However, conventional systems have struggled to integrate the processing of work instructions with feedback based on the worker's emotional state, leading to challenges such as decreased work efficiency and increased stress.

[0347] 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.

[0348] In this invention, the server includes means for receiving instructions from a worker via an information processing device, means for using an emotion recognition engine to recognize the worker's emotional state, and means for analyzing data using generative artificial intelligence based on the instructions and emotional state to generate optimized results. This makes it possible to provide information optimized according to the worker's emotional state, thereby improving work efficiency and reducing stress.

[0349] An "information processing device" is a device used for inputting, processing, and outputting data, and has the function of receiving and processing instructions from an operator.

[0350] An "emotion recognition engine" is a software or hardware component that estimates and analyzes a user's emotional state from their voice or text data.

[0351] "Generative artificial intelligence" is an artificial intelligence technology that can analyze data and automatically generate optimized results to support user decision-making.

[0352] "Optimized results" refer to the output of data processing that has been adjusted to provide more effective and efficient work support, taking into account relevant information and emotional states.

[0353] "Visual display" refers to a method of presenting processed information or results to the user in an easily understandable way using a display or similar device.

[0354] In implementing this invention, the user first uses a dedicated application installed on a mobile device. The user inputs instructions via voice or text to provide work instructions. The device has an emotion recognition engine that estimates the user's emotional state from their voice and input patterns. This engine analyzes the user's emotional state in real time, and the device sends the data to a server based on the results.

[0355] The server analyzes data received via the information processing device using a generation AI model. This takes into account the user's instructions and emotional state to generate optimized results. These generated results are used to provide the worker with the necessary information and instructions. For example, if the worker is feeling stressed, the server can simplify the work procedure.

[0356] The results generated on the server are sent back to the terminal and presented to the user visually. At this point, the terminal can adjust the display format based on the user's emotional state and optimize the interface design so that the user can receive the information without stress.

[0357] As a concrete example, consider a scenario where the terminal receives a voice command from the user, such as "Confirm the next task," and detects signs of anxiety. In this case, the server determines that "the procedure should be simplified to alleviate anxiety" and sends instructions based on that decision to the terminal. The terminal then presents this information to the user using calming colors to support the worker's next action.

[0358] A concrete example of a prompt message would be, "Please consider ways to simplify the parts replacement procedure presented when the user's stress level is high." This invention will improve work efficiency and reduce worker stress at the worksite.

[0359] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0360] Step 1:

[0361] The user launches the application using a mobile device and inputs work instructions. The input data is in either voice or text format. This input data is sent to the emotion recognition engine built into the device. For example, the worker might verbally say, "Tell me the procedure for replacing part A."

[0362] Step 2:

[0363] The device uses an emotion recognition engine to analyze the emotional state of input voice and text. Here, voice data is analyzed by a speech processing algorithm, and text data by a pattern recognition algorithm, to estimate the user's emotional state. If the emotional state is determined to be "tension," the engine processes this information as data. The output is the estimated emotional state.

[0364] Step 3:

[0365] The terminal transmits user instructions and estimated emotional state data to the server. The input consists of user-provided instruction data and emotional state data. Specifically, the terminal transmits "the procedure for replacing part A and the user's tension level" to the server using a secure communication method.

[0366] Step 4:

[0367] The server analyzes the received data using a generative AI model. The input consists of user instructions and emotional state data. In this process, the AI ​​integrates the data and simulates various options to determine the most appropriate action for the instructions. The output is an improved work procedure, for example, a simplified procedure.

[0368] Step 5:

[0369] The server sends back the feedback data generated as a result of the optimization to the terminal. The input is the work procedure determined by the AI. Specifically, the server sends the "simplified procedure" to the terminal.

[0370] Step 6:

[0371] The terminal presents the user with feedback received from the server. The input is feedback data sent from the server. Based on this data, the terminal adjusts the interface display according to the user's emotional state. The output is a visual presentation, showing the user a shortened parts replacement procedure in a calming color scheme.

[0372] (Application Example 2)

[0373] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0374] In modern manufacturing environments, workers' emotional states can affect productivity and accuracy. However, traditional systems have struggled to provide flexible responses that take workers' emotions into account, as well as appropriate feedback. Furthermore, even in the operation of automated machinery, the lack of support to reduce worker stress and anxiety has made it difficult to achieve effective production activities.

[0375] 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.

[0376] In this invention, the server includes means for receiving instructions from the user via a mobile information terminal, means for analyzing data using an artificial intelligence engine to generate optimized results that take into account the user's emotional state, means for visually displaying the generated results and adjusting the interface based on the emotional state, and means for transmitting instructions to enable coordination with automated machinery. This makes it possible to provide optimal feedback that responds to the user's emotions and improve the productivity of workers.

[0377] A "portable information terminal" is an information processing device that a user can carry with them, capable of inputting and displaying data.

[0378] "User" refers to an individual or person responsible for performing tasks using this system.

[0379] An "instruction" is an operational command or request that a user gives to a system.

[0380] An "artificial intelligence engine" is a system element in which a computer simulates human cognitive abilities, analyzes data, and derives optimal results.

[0381] "Analyzing data" refers to the methods used to process collected information and extract meaningful insights.

[0382] "Emotional state" refers to the emotional state the user is experiencing at that particular time.

[0383] "Generating optimized results" is the process of creating the most appropriate feedback and instructions based on the data obtained and the user's emotions.

[0384] "Visually displaying" means presenting information in a way that users can recognize visually.

[0385] "Adjusting the interface" means setting up a user-friendly screen layout and operating system based on the user's emotions and circumstances.

[0386] An "automated machine" is a machine or equipment that is set up to perform some or all of a task automatically.

[0387] "Interlocking" refers to the process where multiple elements function in conjunction with each other and operate in a coordinated manner.

[0388] A "database" is a collection of data that is systematically gathered, stored, and made accessible.

[0389] To implement this invention, the server receives instructions from the user via a mobile device. The received instructions include text input and voice input, and accordingly, an artificial intelligence engine is incorporated to analyze the user's emotional state. The artificial intelligence engine analyzes emotions from the input voice and text data, retrieves relevant information in conjunction with a database, and performs the necessary processing.

[0390] The server takes the user's emotional state into consideration and uses a generative AI model to generate optimal feedback and operational instructions based on the user's requests. These generated results are visually displayed on the user's mobile device, and the interface is adjusted according to the user's emotional state.

[0391] After the user confirms the information on their mobile device, instructions are sent to automated machinery as needed, and the relevant tasks begin. This process allows users to receive information in a way that is optimal to their emotional state and circumstances, enabling them to work quickly and effectively.

[0392] As a concrete example, when a machine operator verbally instructs that "a part is not working properly," the terminal recognizes the user's level of anxiety, and the server uses an AI model to generate a simplified procedural guide, adjusting the colors and layout before displaying it on the terminal. An example of a prompt message would be as follows:

[0393] Example of a prompt:

[0394] "When users are feeling stressed, simplify the assembly process and provide clear, easy-to-understand instructions. The instructions should be short and concise, based on the following guidelines. Strive to enhance the user's sense of security: {User Instructions}"

[0395] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0396] Step 1:

[0397] The terminal receives instructions from the user in the form of voice or text data. These instructions serve as the starting point for the system's processing. The input data is either voice or text, and the terminal prepares it for passing on to the emotion recognition engine.

[0398] Step 2:

[0399] The device sends voice or text data to an emotion recognition engine to analyze the user's emotional state. The input is voice or text data, and the output is data indicating the user's emotions. The emotion recognition engine analyzes the tone of voice and word choice to infer emotional states such as stress, relief, or confusion.

[0400] Step 3:

[0401] The server receives instructions and sentiment data sent from the terminal and uses a generative AI model to generate user-optimized feedback. Input includes instruction data and sentiment data, and output is customized feedback based on the sentiment. Based on this prompt, the AI ​​creates actionable instructions or simplified procedures.

[0402] Step 4:

[0403] The server sends the generated feedback to the terminal, which then displays it visually to the user. The input is user-optimized feedback, and the output is a visually adjusted information display. Specifically, the interface layout and color scheme are adjusted to take the user's emotions into consideration.

[0404] Step 5:

[0405] The user makes necessary decisions based on the displayed feedback and sends instructions from the terminal to the automated machine. This automatically starts or adjusts the work. The input is the user's action, and the output is the instruction to start the machine's operation. Finally, the machine operates according to the new instructions.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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".

[0422] The system of the present invention allows workers on the factory floor to issue instructions via a portable information terminal, which are then processed using artificial intelligence on a server, and the results are returned to the workers. Specifically, it is implemented in the following manner.

[0423] The user launches a dedicated application on their mobile device and inputs information about a specific task at the work site. For example, they might input something like, "Please check the inspection status of machine A." The device then sends this input to the server.

[0424] Based on the received instructions, the server utilizes generated artificial intelligence to retrieve and analyze necessary information from relevant databases. For example, it analyzes the past inspection history and current operating data of machine A to determine the deterioration status of parts and whether or not there are any abnormalities.

[0425] Once the analysis is complete, the server sends the results back to the user's mobile device. These results are displayed visually as a detailed report. Furthermore, if necessary, specific actions are suggested, such as "We recommend replacing part X this month."

[0426] For example, to determine the need for parts replacement, workers input data, and a server analyzes it and provides immediate feedback, enabling efficient maintenance work. In this way, field workers can smoothly carry out their daily tasks by receiving support from AI generated using mobile devices, without requiring any special knowledge of information technology.

[0427] The implementation of this system will improve work efficiency on the manufacturing floor, enabling proactive troubleshooting and rapid response. Furthermore, the accumulation and utilization of on-site data will accelerate the promotion of digital transformation for further productivity improvements.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The user launches a dedicated application on their mobile device, enters their authentication information, and logs in. Upon successful authentication, the main menu is displayed.

[0431] Step 2:

[0432] The user inputs instructions related to the task. For example, they might give voice or text instructions such as "Check the operating status of machine A."

[0433] Step 3:

[0434] The terminal receives user instructions and analyzes them using a natural language processing engine. After analysis, it converts the instructions into a format for sending to the server.

[0435] Step 4:

[0436] The terminal sends the analyzed instruction data to the server. This data includes the user's requests and related information.

[0437] Step 5:

[0438] The server accesses relevant databases based on the received data and retrieves the necessary data. For example, it might retrieve past operating data and maintenance records for machine A.

[0439] Step 6:

[0440] The server receives the acquired data and passes it to an artificial intelligence (AI) that performs data analysis based on its instructions. The AI ​​detects specific patterns and anomalies and generates the necessary insights.

[0441] Step 7:

[0442] The server organizes the analysis results and generates information in a user-friendly format. This includes reports in text and graphic formats.

[0443] Step 8:

[0444] The server sends the generated information to the terminal.

[0445] Step 9:

[0446] The device presents the received information to the user. The user then uses this information to understand the situation and decide on necessary actions.

[0447] Step 10:

[0448] If the user needs more detailed information or wants to give new instructions, they can enter the instructions again and repeat the process.

[0449] (Example 1)

[0450] 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."

[0451] In modern manufacturing environments, it is essential to proactively detect equipment deterioration and malfunctions and perform maintenance efficiently. However, traditional systems often relied on manual information gathering and analysis, making quick decision-making difficult. Furthermore, the need for specialized knowledge made it difficult for on-site workers to make decisions independently, leading to increased reliance on external experts.

[0452] 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.

[0453] In this invention, the server includes means for receiving instructions from an operator via a portable computer, means for analyzing information using a generative intelligence system and transmitting the generated results to the operator, and means for visually displaying the generated results on the portable computer. This enables field workers to efficiently monitor the status of equipment and take necessary actions quickly without requiring special knowledge.

[0454] A "portable computing device" is a portable computing device used by users to input, send, and receive information.

[0455] An "operator" refers to a worker who uses a system to provide specific instructions and receives the results.

[0456] An "instruction" refers to the content of a request or operation that an operator makes to a system.

[0457] A "generative intelligence system" is a system that uses artificial intelligence technology to perform data analysis based on received information and generate results.

[0458] "Information" refers to data sent to the server and data sets obtained from databases used for analysis.

[0459] "Analysis" is the process of processing and interpreting data to derive specific conclusions or results.

[0460] "Results" refers to the collective output generated based on analysis by a generative intelligence system.

[0461] This invention provides a system that enables workers in a manufacturing environment to use a portable computing device to process specific instructions using a generative intelligence system and obtain results quickly.

[0462] Users input instructions at the worksite using a dedicated application installed on a portable calculator. This device can be easily operated via a touch panel or voice input. For example, it is possible to input requests such as, "Please check the inspection status of machine A."

[0463] The terminal receives user input and transmits it to the server via the network. The server consists of high-performance computing equipment and can communicate with various databases via the network connection. The server has a generative AI model implemented on it, which acquires and analyzes data based on the received instructions.

[0464] The server extracts data from the database, such as past inspection history and current operating data for machine A, and performs analysis to determine its deterioration status and whether there are any abnormalities. A generative intelligence system is used for the analysis, performing pattern recognition and predictive analysis of the data using advanced algorithms.

[0465] Once the analysis is complete, the server returns the results to the user. This information is displayed as visual graphics and reports on the portable computing device, making it easy for the operator to understand. Furthermore, based on the analysis results, specific actions such as "replace part X before the next maintenance" can be suggested.

[0466] For example, if a user enters "Please check the condition of the conveyor belt of machine A" into the terminal, the server analyzes this information and immediately provides specific instructions such as "The conveyor belt must be replaced within three months."

[0467] An example of a prompt message that might be input to the generating AI model is, "Analyze the current state of machine A and past inspection data, evaluate its deterioration, and propose the next action." Based on this prompt, the server can perform the necessary analysis and present accurate results to the user.

[0468] This system allows on-site workers to efficiently manage and maintain equipment without requiring special technical knowledge or the intervention of external experts.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] The user launches a dedicated application for the portable calculator and inputs the task they want to check at the work site. This task input can be done via text or voice. The user inputs instructions such as, "Please check the inspection status of machine A." This input data is then sent to the next process.

[0472] Step 2:

[0473] The terminal receives instructions from the user, encrypts them, and sends them to the server over the network. Information security is a key consideration here, ensuring that data is safely transmitted to the server without being leaked externally. The input is the instruction data, and the output is the data sent to the server.

[0474] Step 3:

[0475] The server retrieves necessary information from relevant databases based on instruction data received from the terminal. The server executes database queries such as "Inspection history of machine A and its parts" and "Current operating data" to extract the required data. The input is instruction data, and the output is relevant data. In this process, the server uses a high-speed processing algorithm to efficiently retrieve the data.

[0476] Step 4:

[0477] The server inputs the acquired data into a generating AI model for data analysis. This analysis predicts the deterioration status and abnormalities of parts based on the acquired operational data and inspection history. The input is related data, and the output is the analysis results. Here, the advanced algorithms of the generating AI model are utilized to identify parts that are highly likely to deteriorate.

[0478] Step 5:

[0479] The server generates a visual report based on the analysis results and sends it to the terminal. This report includes specific recommendations, such as "replace part X before the next maintenance." The input is the analysis results, and the output is the report data. The report is formatted in graphs and charts to make it easy to understand visually.

[0480] Step 6:

[0481] The terminal receives reports from the server and displays them on the user's application screen. The user can review these reports and take necessary actions quickly. Input is the report data, and output is the displayed content. The terminal provides an intuitive interface, allowing the user to easily decide on their next action.

[0482] (Application Example 1)

[0483] 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."

[0484] In industrial settings, preventing equipment and facility failures and performing maintenance efficiently is crucial. Conventional systems struggled to immediately detect signs of failure or abnormality, relying heavily on the experience of skilled technicians. This resulted in decreased production efficiency and unexpected downtime. This invention aims to solve these problems and provide a system that enables faster and more efficient maintenance without burdening on-site operators.

[0485] 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.

[0486] In this invention, the server includes means for receiving instructions from an operator via a mobile information terminal, means for analyzing information using generative artificial intelligence to process the instructions and transmitting the generated results to the operator, and means for providing the next recommended maintenance action based on the analysis results. This enables the operator to perform appropriate maintenance quickly and improve production efficiency without relying on special expertise.

[0487] A "portable information terminal" is an information processing device that can be carried by a worker and allows for the input of instructions and the display of results.

[0488] "Operator" refers to a person who performs maintenance and monitoring of equipment and facilities on-site.

[0489] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to analyze information and make decisions based on the input.

[0490] "Analyzing information" refers to the process of deriving some kind of insight or result based on collected data.

[0491] "Sending the generated results" refers to the process of delivering the judgments and suggestions obtained through analysis to the inputter.

[0492] "Recommended maintenance actions" refer to specific action guidelines proposed for the maintenance and preservation of equipment and machinery based on the analysis results.

[0493] "Based on analysis results" means deciding on the next steps or actions based on the results of information analysis.

[0494] The system implementing this invention provides a means for operators to efficiently manage equipment maintenance tasks in factories and manufacturing sites using a portable information terminal. The operator inputs specific instructions via the portable information terminal, and these instructions are transmitted to a server via data communication.

[0495] The server uses a generative AI model to analyze historical maintenance data and real-time operational data of equipment and devices. Leading AI frameworks such as TensorFlow and PyTorch can be used for the analysis. The analyzed data forms the basis for determining the equipment's condition and potential anomalies. Based on the analysis results, the server recommends appropriate maintenance actions to operators.

[0496] The results and recommendations generated by the server are sent to a mobile device and displayed in a report format that operators can visually review. This system enables operators to perform equipment maintenance efficiently and quickly without relying on specialized knowledge.

[0497] A concrete example is the maintenance management of dough forming equipment in a bread factory. When an operator inputs a command into a terminal, such as "Check the current status of the dough forming equipment," the system analyzes the command and provides a recommendation, such as "This equipment will require lubrication maintenance in three months." This helps prevent equipment failures and minimizes line downtime.

[0498] Furthermore, the following example of a prompt can be applied to effectively analyze a generative AI model: "Analyze the maintenance status of the dough forming machine and suggest the next necessary actions." This prompt clearly indicates what kind of judgment is required of the generative AI and helps to obtain appropriate results.

[0499] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0500] Step 1:

[0501] The user uses a mobile device to input specific instructions regarding equipment maintenance. The input is in text format and includes prompts such as "Check the current status of the dough forming machine." This input is performed using the form input function on the mobile device.

[0502] Step 2:

[0503] The terminal sends the input instructions to the server. Protocols such as HTTP and WebSocket are used for data communication. The data output from the terminal includes the input instructions. Specifically, this step involves packetizing the instructions and sending the data over the network to the server's analysis process.

[0504] Step 3:

[0505] The server analyzes the received instructions and activates a generative artificial intelligence model. The AI ​​model uses TensorFlow or PyTorch to process relevant equipment data based on the instructions. At this stage, the input is user instruction data, and the output is predictions and judgments about the equipment's status. Specifically, this analysis generates recommended actions such as, "The next maintenance should be performed in three months."

[0506] Step 4:

[0507] The server sends the analysis results to the mobile device. The input here is the previously generated analysis result, and the output is a visual display of the results on the device. The data is sent to the device in a highly readable report format.

[0508] Step 5:

[0509] The terminal displays the received results to the user in a visual format. The output is visualized on the terminal's display as graphs and text information. Specifically, the terminal displays the analysis results in tabular or Gantt chart format, making it easy for the user to understand the next maintenance action to take.

[0510] 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.

[0511] This invention is a system for supporting the production activities of workers in a manufacturing site. It receives instructions from workers via a mobile information terminal and incorporates an emotion recognition engine to provide feedback according to the user's emotional state.

[0512] Users launch a dedicated application using a mobile device and input instructions for their work via voice or text. At this time, the device uses its built-in emotion recognition engine to estimate the user's emotions from their voice and input patterns. This process detects whether the worker is experiencing anxiety or stress.

[0513] The device sends data about the user's emotional state to the server along with instructions. The server uses generative artificial intelligence to analyze the data based on the user's instructions and generate optimized results that take the user's emotional state into account. Such results provide even better decision-making support in specific situations.

[0514] For example, if a worker is under stress, the system can provide information with simplified procedures and special notes. This allows the worker to quickly access the necessary information without confusion.

[0515] The results generated by the server are sent back to the terminal and presented to the user visually as graphics and text. At this time, the terminal can customize the results based on the user's emotional state and adjust the color scheme and interface to reduce visual stress.

[0516] This allows users to receive information in a format best suited to their current situation and take action as needed. For example, a worker who is feeling stressed just before replacing a part could be presented with simplified instructions, enabling them to respond appropriately in a shorter amount of time.

[0517] This embodiment can prevent unexpected productivity drops and errors, and promote efficient work in the manufacturing environment. By integrating with an emotion engine, it is possible to achieve digital transformation that is more in line with human intuition, and to enhance the convenience and flexibility of information technology in the workplace.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The user launches a dedicated application on their mobile device, enters their authentication information, and logs into the system. The application verifies the login information and displays the main menu.

[0521] Step 2:

[0522] Users input work-related instructions into the terminal via voice or text. This input information is temporarily stored within the terminal to clarify the worker's intent.

[0523] Step 3:

[0524] The device processes the input instructions and uses its built-in emotion recognition engine to analyze the user's current emotional state. Specifically, it determines stress and anxiety from factors such as voice tone and input speed.

[0525] Step 4:

[0526] The device sends the received instructions and emotional state data to the server. Here, a package is formed containing the instructions and user emotional metrics.

[0527] Step 5:

[0528] The server analyzes the instructions and uses generative artificial intelligence to retrieve the necessary information from relevant databases. Simultaneously, it optimizes data prioritization and processing methods based on sentiment metrics.

[0529] Step 6:

[0530] Based on the analysis results generated by the server, customized information tailored to the user's emotions is constructed. For example, a simple instruction manual is generated for highly anxious users, while a detailed document is generated for calm users.

[0531] Step 7:

[0532] The server generates optimization information and sends it to the terminal. The information sent includes visually adjusted content.

[0533] Step 8:

[0534] The device provides the user with the information it receives. The device adjusts the interface's color scheme and layout according to the user's emotional state, presenting it in a user-friendly format.

[0535] Step 9:

[0536] The user performs the task based on the information provided, giving additional instructions or asking new questions as needed. This completes the work cycle.

[0537] (Example 2)

[0538] 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."

[0539] In manufacturing environments, it is essential to respond quickly and accurately to worker instructions while also providing support that takes into account the worker's emotional state. However, conventional systems have struggled to integrate the processing of work instructions with feedback based on the worker's emotional state, leading to challenges such as decreased work efficiency and increased stress.

[0540] 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.

[0541] In this invention, the server includes means for receiving instructions from a worker via an information processing device, means for using an emotion recognition engine to recognize the worker's emotional state, and means for analyzing data using generative artificial intelligence based on the instructions and emotional state to generate optimized results. This makes it possible to provide information optimized according to the worker's emotional state, thereby improving work efficiency and reducing stress.

[0542] An "information processing device" is a device used for inputting, processing, and outputting data, and has the function of receiving and processing instructions from an operator.

[0543] An "emotion recognition engine" is a software or hardware component that estimates and analyzes a user's emotional state from their voice or text data.

[0544] "Generative artificial intelligence" is an artificial intelligence technology that can analyze data and automatically generate optimized results to support user decision-making.

[0545] "Optimized results" refer to the output of data processing that has been adjusted to provide more effective and efficient work support, taking into account relevant information and emotional states.

[0546] "Visual display" refers to a method of presenting processed information or results to the user in an easily understandable way using a display or similar device.

[0547] In implementing this invention, the user first uses a dedicated application installed on a mobile device. The user inputs instructions via voice or text to provide work instructions. The device has an emotion recognition engine that estimates the user's emotional state from their voice and input patterns. This engine analyzes the user's emotional state in real time, and the device sends the data to a server based on the results.

[0548] The server analyzes data received via the information processing device using a generation AI model. This takes into account the user's instructions and emotional state to generate optimized results. These generated results are used to provide the worker with the necessary information and instructions. For example, if the worker is feeling stressed, the server can simplify the work procedure.

[0549] The results generated on the server are sent back to the terminal and presented to the user visually. At this point, the terminal can adjust the display format based on the user's emotional state and optimize the interface design so that the user can receive the information without stress.

[0550] As a concrete example, consider a scenario where the terminal receives a voice command from the user, such as "Confirm the next task," and detects signs of anxiety. In this case, the server determines that "the procedure should be simplified to alleviate anxiety" and sends instructions based on that decision to the terminal. The terminal then presents this information to the user using calming colors to support the worker's next action.

[0551] A concrete example of a prompt message would be, "Please consider ways to simplify the parts replacement procedure presented when the user's stress level is high." This invention will improve work efficiency and reduce worker stress at the worksite.

[0552] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0553] Step 1:

[0554] The user launches the application using a mobile device and inputs work instructions. The input data is in either voice or text format. This input data is sent to the emotion recognition engine built into the device. For example, the worker might verbally say, "Tell me the procedure for replacing part A."

[0555] Step 2:

[0556] The device uses an emotion recognition engine to analyze the emotional state of input voice and text. Here, voice data is analyzed by a speech processing algorithm, and text data by a pattern recognition algorithm, to estimate the user's emotional state. If the emotional state is determined to be "tension," the engine processes this information as data. The output is the estimated emotional state.

[0557] Step 3:

[0558] The terminal transmits user instructions and estimated emotional state data to the server. The input consists of user-provided instruction data and emotional state data. Specifically, the terminal transmits "the procedure for replacing part A and the user's tension level" to the server using a secure communication method.

[0559] Step 4:

[0560] The server analyzes the received data using a generative AI model. The input consists of user instructions and emotional state data. In this process, the AI ​​integrates the data and simulates various options to determine the most appropriate action for the instructions. The output is an improved work procedure, for example, a simplified procedure.

[0561] Step 5:

[0562] The server sends back the feedback data generated as a result of the optimization to the terminal. The input is the work procedure determined by the AI. Specifically, the server sends the "simplified procedure" to the terminal.

[0563] Step 6:

[0564] The terminal presents the user with feedback received from the server. The input is feedback data sent from the server. Based on this data, the terminal adjusts the interface display according to the user's emotional state. The output is a visual presentation, showing the user a shortened parts replacement procedure in a calming color scheme.

[0565] (Application Example 2)

[0566] 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."

[0567] In modern manufacturing environments, workers' emotional states can affect productivity and accuracy. However, traditional systems have struggled to provide flexible responses that take workers' emotions into account, as well as appropriate feedback. Furthermore, even in the operation of automated machinery, the lack of support to reduce worker stress and anxiety has made it difficult to achieve effective production activities.

[0568] 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.

[0569] In this invention, the server includes means for receiving instructions from the user via a mobile information terminal, means for analyzing data using an artificial intelligence engine to generate optimized results that take into account the user's emotional state, means for visually displaying the generated results and adjusting the interface based on the emotional state, and means for transmitting instructions to enable coordination with automated machinery. This makes it possible to provide optimal feedback that responds to the user's emotions and improve the productivity of workers.

[0570] A "portable information terminal" is an information processing device that a user can carry with them, capable of inputting and displaying data.

[0571] "User" refers to an individual or person responsible for performing tasks using this system.

[0572] An "instruction" is an operational command or request that a user gives to a system.

[0573] An "artificial intelligence engine" is a system element in which a computer simulates human cognitive abilities, analyzes data, and derives optimal results.

[0574] "Analyzing data" refers to the methods used to process collected information and extract meaningful insights.

[0575] "Emotional state" refers to the emotional state the user is experiencing at that particular time.

[0576] "Generating optimized results" is the process of creating the most appropriate feedback and instructions based on the data obtained and the user's emotions.

[0577] "Visually displaying" means presenting information in a way that users can recognize visually.

[0578] "Adjusting the interface" means setting up a user-friendly screen layout and operating system based on the user's emotions and circumstances.

[0579] An "automated machine" is a machine or equipment that is set up to perform some or all of a task automatically.

[0580] "Interlocking" refers to the process where multiple elements function in conjunction with each other and operate in a coordinated manner.

[0581] A "database" is a collection of data that is systematically gathered, stored, and made accessible.

[0582] To implement this invention, the server receives instructions from the user via a mobile device. The received instructions include text input and voice input, and accordingly, an artificial intelligence engine is incorporated to analyze the user's emotional state. The artificial intelligence engine analyzes emotions from the input voice and text data, retrieves relevant information in conjunction with a database, and performs the necessary processing.

[0583] The server takes the user's emotional state into consideration and uses a generative AI model to generate optimal feedback and operational instructions based on the user's requests. These generated results are visually displayed on the user's mobile device, and the interface is adjusted according to the user's emotional state.

[0584] After the user confirms the information on their mobile device, instructions are sent to automated machinery as needed, and the relevant tasks begin. This process allows users to receive information in a way that is optimal to their emotional state and circumstances, enabling them to work quickly and effectively.

[0585] As a concrete example, when a machine operator verbally instructs that "a part is not working properly," the terminal recognizes the user's level of anxiety, and the server uses an AI model to generate a simplified procedural guide, adjusting the colors and layout before displaying it on the terminal. An example of a prompt message would be as follows:

[0586] Example of a prompt:

[0587] "When users are feeling stressed, simplify the assembly process and provide clear, easy-to-understand instructions. The instructions should be short and concise, based on the following guidelines. Strive to enhance the user's sense of security: {User Instructions}"

[0588] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0589] Step 1:

[0590] The terminal receives instructions from the user in the form of voice or text data. These instructions serve as the starting point for the system's processing. The input data is either voice or text, and the terminal prepares it for passing on to the emotion recognition engine.

[0591] Step 2:

[0592] The device sends voice or text data to an emotion recognition engine to analyze the user's emotional state. The input is voice or text data, and the output is data indicating the user's emotions. The emotion recognition engine analyzes the tone of voice and word choice to infer emotional states such as stress, relief, or confusion.

[0593] Step 3:

[0594] The server receives instructions and sentiment data sent from the terminal and uses a generative AI model to generate user-optimized feedback. Input includes instruction data and sentiment data, and output is customized feedback based on the sentiment. Based on this prompt, the AI ​​creates actionable instructions or simplified procedures.

[0595] Step 4:

[0596] The server sends the generated feedback to the terminal, which then displays it visually to the user. The input is user-optimized feedback, and the output is a visually adjusted information display. Specifically, the interface layout and color scheme are adjusted to take the user's emotions into consideration.

[0597] Step 5:

[0598] The user makes necessary decisions based on the displayed feedback and sends instructions from the terminal to the automated machine. This automatically starts or adjusts the work. The input is the user's action, and the output is the instruction to start the machine's operation. Finally, the machine operates according to the new instructions.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] [Fourth Embodiment]

[0603] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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).

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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".

[0616] The system of the present invention allows workers on the factory floor to issue instructions via a portable information terminal, which are then processed using artificial intelligence on a server, and the results are returned to the workers. Specifically, it is implemented in the following manner.

[0617] The user launches a dedicated application on their mobile device and inputs information about a specific task at the work site. For example, they might input something like, "Please check the inspection status of machine A." The device then sends this input to the server.

[0618] Based on the received instructions, the server utilizes generated artificial intelligence to retrieve and analyze necessary information from relevant databases. For example, it analyzes the past inspection history and current operating data of machine A to determine the deterioration status of parts and whether or not there are any abnormalities.

[0619] Once the analysis is complete, the server sends the results back to the user's mobile device. These results are displayed visually as a detailed report. Furthermore, if necessary, specific actions are suggested, such as "We recommend replacing part X this month."

[0620] For example, to determine the need for parts replacement, workers input data, and a server analyzes it and provides immediate feedback, enabling efficient maintenance work. In this way, field workers can smoothly carry out their daily tasks by receiving support from AI generated using mobile devices, without requiring any special knowledge of information technology.

[0621] The implementation of this system will improve work efficiency on the manufacturing floor, enabling proactive troubleshooting and rapid response. Furthermore, the accumulation and utilization of on-site data will accelerate the promotion of digital transformation for further productivity improvements.

[0622] The following describes the processing flow.

[0623] Step 1:

[0624] The user launches a dedicated application on their mobile device, enters their authentication information, and logs in. Upon successful authentication, the main menu is displayed.

[0625] Step 2:

[0626] The user inputs instructions related to the task. For example, they might give voice or text instructions such as "Check the operating status of machine A."

[0627] Step 3:

[0628] The terminal receives user instructions and analyzes them using a natural language processing engine. After analysis, it converts the instructions into a format for sending to the server.

[0629] Step 4:

[0630] The terminal sends the analyzed instruction data to the server. This data includes the user's requests and related information.

[0631] Step 5:

[0632] The server accesses relevant databases based on the received data and retrieves the necessary data. For example, it might retrieve past operating data and maintenance records for machine A.

[0633] Step 6:

[0634] The server receives the acquired data and passes it to an artificial intelligence (AI) that performs data analysis based on its instructions. The AI ​​detects specific patterns and anomalies and generates the necessary insights.

[0635] Step 7:

[0636] The server organizes the analysis results and generates information in a user-friendly format. This includes reports in text and graphic formats.

[0637] Step 8:

[0638] The server sends the generated information to the terminal.

[0639] Step 9:

[0640] The device presents the received information to the user. The user then uses this information to understand the situation and decide on necessary actions.

[0641] Step 10:

[0642] If the user needs more detailed information or wants to give new instructions, they can enter the instructions again and repeat the process.

[0643] (Example 1)

[0644] 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".

[0645] In modern manufacturing environments, it is essential to proactively detect equipment deterioration and malfunctions and perform maintenance efficiently. However, traditional systems often relied on manual information gathering and analysis, making quick decision-making difficult. Furthermore, the need for specialized knowledge made it difficult for on-site workers to make decisions independently, leading to increased reliance on external experts.

[0646] 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.

[0647] In this invention, the server includes means for receiving instructions from an operator via a portable computer, means for analyzing information using a generative intelligence system and transmitting the generated results to the operator, and means for visually displaying the generated results on the portable computer. This enables field workers to efficiently monitor the status of equipment and take necessary actions quickly without requiring special knowledge.

[0648] A "portable computing device" is a portable computing device used by users to input, send, and receive information.

[0649] An "operator" refers to a worker who uses a system to provide specific instructions and receives the results.

[0650] An "instruction" refers to the content of a request or operation that an operator makes to a system.

[0651] A "generative intelligence system" is a system that uses artificial intelligence technology to perform data analysis based on received information and generate results.

[0652] "Information" refers to data sent to the server and data sets obtained from databases used for analysis.

[0653] "Analysis" is the process of processing and interpreting data to derive specific conclusions or results.

[0654] "Results" refers to the collective output generated based on analysis by a generative intelligence system.

[0655] This invention provides a system that enables workers in a manufacturing environment to use a portable computing device to process specific instructions using a generative intelligence system and obtain results quickly.

[0656] Users input instructions at the worksite using a dedicated application installed on a portable calculator. This device can be easily operated via a touch panel or voice input. For example, it is possible to input requests such as, "Please check the inspection status of machine A."

[0657] The terminal receives user input and transmits it to the server via the network. The server consists of high-performance computing equipment and can communicate with various databases via the network connection. The server has a generative AI model implemented on it, which acquires and analyzes data based on the received instructions.

[0658] The server extracts data from the database, such as past inspection history and current operating data for machine A, and performs analysis to determine its deterioration status and whether there are any abnormalities. A generative intelligence system is used for the analysis, performing pattern recognition and predictive analysis of the data using advanced algorithms.

[0659] Once the analysis is complete, the server returns the results to the user. This information is displayed as visual graphics and reports on the portable computing device, making it easy for the operator to understand. Furthermore, based on the analysis results, specific actions such as "replace part X before the next maintenance" can be suggested.

[0660] For example, if a user enters "Please check the condition of the conveyor belt of machine A" into the terminal, the server analyzes this information and immediately provides specific instructions such as "The conveyor belt must be replaced within three months."

[0661] An example of a prompt message that might be input to the generating AI model is, "Analyze the current state of machine A and past inspection data, evaluate its deterioration, and propose the next action." Based on this prompt, the server can perform the necessary analysis and present accurate results to the user.

[0662] This system allows on-site workers to efficiently manage and maintain equipment without requiring special technical knowledge or the intervention of external experts.

[0663] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0664] Step 1:

[0665] The user launches a dedicated application for the portable calculator and inputs the task they want to check at the work site. This task input can be done via text or voice. The user inputs instructions such as, "Please check the inspection status of machine A." This input data is then sent to the next process.

[0666] Step 2:

[0667] The terminal receives instructions from the user, encrypts them, and sends them to the server over the network. Information security is a key consideration here, ensuring that data is safely transmitted to the server without being leaked externally. The input is the instruction data, and the output is the data sent to the server.

[0668] Step 3:

[0669] The server retrieves necessary information from relevant databases based on instruction data received from the terminal. The server executes database queries such as "Inspection history of machine A and its parts" and "Current operating data" to extract the required data. The input is instruction data, and the output is relevant data. In this process, the server uses a high-speed processing algorithm to efficiently retrieve the data.

[0670] Step 4:

[0671] The server inputs the acquired data into a generating AI model for data analysis. This analysis predicts the deterioration status and abnormalities of parts based on the acquired operational data and inspection history. The input is related data, and the output is the analysis results. Here, the advanced algorithms of the generating AI model are utilized to identify parts that are highly likely to deteriorate.

[0672] Step 5:

[0673] The server generates a visual report based on the analysis results and sends it to the terminal. This report includes specific recommendations, such as "replace part X before the next maintenance." The input is the analysis results, and the output is the report data. The report is formatted in graphs and charts to make it easy to understand visually.

[0674] Step 6:

[0675] The terminal receives reports from the server and displays them on the user's application screen. The user can review these reports and take necessary actions quickly. Input is the report data, and output is the displayed content. The terminal provides an intuitive interface, allowing the user to easily decide on their next action.

[0676] (Application Example 1)

[0677] 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".

[0678] In industrial settings, preventing equipment and facility failures and performing maintenance efficiently is crucial. Conventional systems struggled to immediately detect signs of failure or abnormality, relying heavily on the experience of skilled technicians. This resulted in decreased production efficiency and unexpected downtime. This invention aims to solve these problems and provide a system that enables faster and more efficient maintenance without burdening on-site operators.

[0679] 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.

[0680] In this invention, the server includes means for receiving instructions from an operator via a mobile information terminal, means for analyzing information using generative artificial intelligence to process the instructions and transmitting the generated results to the operator, and means for providing the next recommended maintenance action based on the analysis results. This enables the operator to perform appropriate maintenance quickly and improve production efficiency without relying on special expertise.

[0681] A "portable information terminal" is an information processing device that can be carried by a worker and allows for the input of instructions and the display of results.

[0682] "Operator" refers to a person who performs maintenance and monitoring of equipment and facilities on-site.

[0683] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to analyze information and make decisions based on the input.

[0684] "Analyzing information" refers to the process of deriving some kind of insight or result based on collected data.

[0685] "Sending the generated results" refers to the process of delivering the judgments and suggestions obtained through analysis to the inputter.

[0686] "Recommended maintenance actions" refer to specific action guidelines proposed for the maintenance and preservation of equipment and machinery based on the analysis results.

[0687] "Based on analysis results" means deciding on the next steps or actions based on the results of information analysis.

[0688] The system implementing this invention provides a means for operators to efficiently manage equipment maintenance tasks in factories and manufacturing sites using a portable information terminal. The operator inputs specific instructions via the portable information terminal, and these instructions are transmitted to a server via data communication.

[0689] The server uses a generative AI model to analyze historical maintenance data and real-time operational data of equipment and devices. Leading AI frameworks such as TensorFlow and PyTorch can be used for the analysis. The analyzed data forms the basis for determining the equipment's condition and potential anomalies. Based on the analysis results, the server recommends appropriate maintenance actions to operators.

[0690] The results and recommendations generated by the server are sent to a mobile device and displayed in a report format that operators can visually review. This system enables operators to perform equipment maintenance efficiently and quickly without relying on specialized knowledge.

[0691] A concrete example is the maintenance management of dough forming equipment in a bread factory. When an operator inputs a command into a terminal, such as "Check the current status of the dough forming equipment," the system analyzes the command and provides a recommendation, such as "This equipment will require lubrication maintenance in three months." This helps prevent equipment failures and minimizes line downtime.

[0692] Furthermore, the following example of a prompt can be applied to effectively analyze a generative AI model: "Analyze the maintenance status of the dough forming machine and suggest the next necessary actions." This prompt clearly indicates what kind of judgment is required of the generative AI and helps to obtain appropriate results.

[0693] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0694] Step 1:

[0695] The user uses a mobile device to input specific instructions regarding equipment maintenance. The input is in text format and includes prompts such as "Check the current status of the dough forming machine." This input is performed using the form input function on the mobile device.

[0696] Step 2:

[0697] The terminal sends the input instructions to the server. Protocols such as HTTP and WebSocket are used for data communication. The data output from the terminal includes the input instructions. Specifically, this step involves packetizing the instructions and sending the data over the network to the server's analysis process.

[0698] Step 3:

[0699] The server analyzes the received instructions and activates a generative artificial intelligence model. The AI ​​model uses TensorFlow or PyTorch to process relevant equipment data based on the instructions. At this stage, the input is user instruction data, and the output is predictions and judgments about the equipment's status. Specifically, this analysis generates recommended actions such as, "The next maintenance should be performed in three months."

[0700] Step 4:

[0701] The server sends the analysis results to the mobile device. The input here is the previously generated analysis result, and the output is a visual display of the results on the device. The data is sent to the device in a highly readable report format.

[0702] Step 5:

[0703] The terminal displays the received results to the user in a visual format. The output is visualized on the terminal's display as graphs and text information. Specifically, the terminal displays the analysis results in tabular or Gantt chart format, making it easy for the user to understand the next maintenance action to take.

[0704] 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.

[0705] This invention is a system for supporting the production activities of workers in a manufacturing site. It receives instructions from workers via a mobile information terminal and incorporates an emotion recognition engine to provide feedback according to the user's emotional state.

[0706] Users launch a dedicated application using a mobile device and input instructions for their work via voice or text. At this time, the device uses its built-in emotion recognition engine to estimate the user's emotions from their voice and input patterns. This process detects whether the worker is experiencing anxiety or stress.

[0707] The device sends data about the user's emotional state to the server along with instructions. The server uses generative artificial intelligence to analyze the data based on the user's instructions and generate optimized results that take the user's emotional state into account. Such results provide even better decision-making support in specific situations.

[0708] For example, if a worker is under stress, the system can provide information with simplified procedures and special notes. This allows the worker to quickly access the necessary information without confusion.

[0709] The results generated by the server are sent back to the terminal and presented to the user visually as graphics and text. At this time, the terminal can customize the results based on the user's emotional state and adjust the color scheme and interface to reduce visual stress.

[0710] This allows users to receive information in a format best suited to their current situation and take action as needed. For example, a worker who is feeling stressed just before replacing a part could be presented with simplified instructions, enabling them to respond appropriately in a shorter amount of time.

[0711] This embodiment can prevent unexpected productivity drops and errors, and promote efficient work in the manufacturing environment. By integrating with an emotion engine, it is possible to achieve digital transformation that is more in line with human intuition, and to enhance the convenience and flexibility of information technology in the workplace.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] The user launches a dedicated application on their mobile device, enters their authentication information, and logs into the system. The application verifies the login information and displays the main menu.

[0715] Step 2:

[0716] Users input work-related instructions into the terminal via voice or text. This input information is temporarily stored within the terminal to clarify the worker's intent.

[0717] Step 3:

[0718] The device processes the input instructions and uses its built-in emotion recognition engine to analyze the user's current emotional state. Specifically, it determines stress and anxiety from factors such as voice tone and input speed.

[0719] Step 4:

[0720] The device sends the received instructions and emotional state data to the server. Here, a package is formed containing the instructions and user emotional metrics.

[0721] Step 5:

[0722] The server analyzes the instructions and uses generative artificial intelligence to retrieve the necessary information from relevant databases. Simultaneously, it optimizes data prioritization and processing methods based on sentiment metrics.

[0723] Step 6:

[0724] Based on the analysis results generated by the server, customized information tailored to the user's emotions is constructed. For example, a simple instruction manual is generated for highly anxious users, while a detailed document is generated for calm users.

[0725] Step 7:

[0726] The server generates optimization information and sends it to the terminal. The information sent includes visually adjusted content.

[0727] Step 8:

[0728] The device provides the user with the information it receives. The device adjusts the interface's color scheme and layout according to the user's emotional state, presenting it in a user-friendly format.

[0729] Step 9:

[0730] The user performs the task based on the information provided, giving additional instructions or asking new questions as needed. This completes the work cycle.

[0731] (Example 2)

[0732] 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".

[0733] In manufacturing environments, it is essential to respond quickly and accurately to worker instructions while also providing support that takes into account the worker's emotional state. However, conventional systems have struggled to integrate the processing of work instructions with feedback based on the worker's emotional state, leading to challenges such as decreased work efficiency and increased stress.

[0734] 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.

[0735] In this invention, the server includes means for receiving instructions from a worker via an information processing device, means for using an emotion recognition engine to recognize the worker's emotional state, and means for analyzing data using generative artificial intelligence based on the instructions and emotional state to generate optimized results. This makes it possible to provide information optimized according to the worker's emotional state, thereby improving work efficiency and reducing stress.

[0736] An "information processing device" is a device used for inputting, processing, and outputting data, and has the function of receiving and processing instructions from an operator.

[0737] An "emotion recognition engine" is a software or hardware component that estimates and analyzes a user's emotional state from their voice or text data.

[0738] "Generative artificial intelligence" is an artificial intelligence technology that can analyze data and automatically generate optimized results to support user decision-making.

[0739] "Optimized results" refer to the output of data processing that has been adjusted to provide more effective and efficient work support, taking into account relevant information and emotional states.

[0740] "Visual display" refers to a method of presenting processed information or results to the user in an easily understandable way using a display or similar device.

[0741] In implementing this invention, the user first uses a dedicated application installed on a mobile device. The user inputs instructions via voice or text to provide work instructions. The device has an emotion recognition engine that estimates the user's emotional state from their voice and input patterns. This engine analyzes the user's emotional state in real time, and the device sends the data to a server based on the results.

[0742] The server analyzes data received via the information processing device using a generation AI model. This takes into account the user's instructions and emotional state to generate optimized results. These generated results are used to provide the worker with the necessary information and instructions. For example, if the worker is feeling stressed, the server can simplify the work procedure.

[0743] The results generated on the server are sent back to the terminal and presented to the user visually. At this point, the terminal can adjust the display format based on the user's emotional state and optimize the interface design so that the user can receive the information without stress.

[0744] As a concrete example, consider a scenario where the terminal receives a voice command from the user, such as "Confirm the next task," and detects signs of anxiety. In this case, the server determines that "the procedure should be simplified to alleviate anxiety" and sends instructions based on that decision to the terminal. The terminal then presents this information to the user using calming colors to support the worker's next action.

[0745] A concrete example of a prompt message would be, "Please consider ways to simplify the parts replacement procedure presented when the user's stress level is high." This invention will improve work efficiency and reduce worker stress at the worksite.

[0746] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0747] Step 1:

[0748] The user launches the application using a mobile device and inputs work instructions. The input data is in either voice or text format. This input data is sent to the emotion recognition engine built into the device. For example, the worker might verbally say, "Tell me the procedure for replacing part A."

[0749] Step 2:

[0750] The device uses an emotion recognition engine to analyze the emotional state of input voice and text. Here, voice data is analyzed by a speech processing algorithm, and text data by a pattern recognition algorithm, to estimate the user's emotional state. If the emotional state is determined to be "tension," the engine processes this information as data. The output is the estimated emotional state.

[0751] Step 3:

[0752] The terminal transmits user instructions and estimated emotional state data to the server. The input consists of user-provided instruction data and emotional state data. Specifically, the terminal transmits "the procedure for replacing part A and the user's tension level" to the server using a secure communication method.

[0753] Step 4:

[0754] The server analyzes the received data using a generative AI model. The input consists of user instructions and emotional state data. In this process, the AI ​​integrates the data and simulates various options to determine the most appropriate action for the instructions. The output is an improved work procedure, for example, a simplified procedure.

[0755] Step 5:

[0756] The server sends back the feedback data generated as a result of the optimization to the terminal. The input is the work procedure determined by the AI. Specifically, the server sends the "simplified procedure" to the terminal.

[0757] Step 6:

[0758] The terminal presents the user with feedback received from the server. The input is feedback data sent from the server. Based on this data, the terminal adjusts the interface display according to the user's emotional state. The output is a visual presentation, showing the user a shortened parts replacement procedure in a calming color scheme.

[0759] (Application Example 2)

[0760] 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".

[0761] In modern manufacturing environments, workers' emotional states can affect productivity and accuracy. However, traditional systems have struggled to provide flexible responses that take workers' emotions into account, as well as appropriate feedback. Furthermore, even in the operation of automated machinery, the lack of support to reduce worker stress and anxiety has made it difficult to achieve effective production activities.

[0762] 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.

[0763] In this invention, the server includes means for receiving instructions from the user via a mobile information terminal, means for analyzing data using an artificial intelligence engine to generate optimized results that take into account the user's emotional state, means for visually displaying the generated results and adjusting the interface based on the emotional state, and means for transmitting instructions to enable coordination with automated machinery. This makes it possible to provide optimal feedback that responds to the user's emotions and improve the productivity of workers.

[0764] A "portable information terminal" is an information processing device that a user can carry with them, capable of inputting and displaying data.

[0765] "User" refers to an individual or person responsible for performing tasks using this system.

[0766] An "instruction" is an operational command or request that a user gives to a system.

[0767] An "artificial intelligence engine" is a system element in which a computer simulates human cognitive abilities, analyzes data, and derives optimal results.

[0768] "Analyzing data" refers to the methods used to process collected information and extract meaningful insights.

[0769] "Emotional state" refers to the emotional state the user is experiencing at that particular time.

[0770] "Generating optimized results" is the process of creating the most appropriate feedback and instructions based on the data obtained and the user's emotions.

[0771] "Visually displaying" means presenting information in a way that users can recognize visually.

[0772] "Adjusting the interface" means setting up a user-friendly screen layout and operating system based on the user's emotions and circumstances.

[0773] An "automated machine" is a machine or equipment that is set up to perform some or all of a task automatically.

[0774] "Interlocking" refers to the process where multiple elements function in conjunction with each other and operate in a coordinated manner.

[0775] A "database" is a collection of data that is systematically gathered, stored, and made accessible.

[0776] To implement this invention, the server receives instructions from the user via a mobile device. The received instructions include text input and voice input, and accordingly, an artificial intelligence engine is incorporated to analyze the user's emotional state. The artificial intelligence engine analyzes emotions from the input voice and text data, retrieves relevant information in conjunction with a database, and performs the necessary processing.

[0777] The server takes the user's emotional state into consideration and uses a generative AI model to generate optimal feedback and operational instructions based on the user's requests. These generated results are visually displayed on the user's mobile device, and the interface is adjusted according to the user's emotional state.

[0778] After the user confirms the information on their mobile device, instructions are sent to automated machinery as needed, and the relevant tasks begin. This process allows users to receive information in a way that is optimal to their emotional state and circumstances, enabling them to work quickly and effectively.

[0779] As a concrete example, when a machine operator verbally instructs that "a part is not working properly," the terminal recognizes the user's level of anxiety, and the server uses an AI model to generate a simplified procedural guide, adjusting the colors and layout before displaying it on the terminal. An example of a prompt message would be as follows:

[0780] Example of a prompt:

[0781] "When users are feeling stressed, simplify the assembly process and provide clear, easy-to-understand instructions. The instructions should be short and concise, based on the following guidelines. Strive to enhance the user's sense of security: {User Instructions}"

[0782] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0783] Step 1:

[0784] The terminal receives instructions from the user in the form of voice or text data. These instructions serve as the starting point for the system's processing. The input data is either voice or text, and the terminal prepares it for passing on to the emotion recognition engine.

[0785] Step 2:

[0786] The device sends voice or text data to an emotion recognition engine to analyze the user's emotional state. The input is voice or text data, and the output is data indicating the user's emotions. The emotion recognition engine analyzes the tone of voice and word choice to infer emotional states such as stress, relief, or confusion.

[0787] Step 3:

[0788] The server receives instructions and sentiment data sent from the terminal and uses a generative AI model to generate user-optimized feedback. Input includes instruction data and sentiment data, and output is customized feedback based on the sentiment. Based on this prompt, the AI ​​creates actionable instructions or simplified procedures.

[0789] Step 4:

[0790] The server sends the generated feedback to the terminal, which then displays it visually to the user. The input is user-optimized feedback, and the output is a visually adjusted information display. Specifically, the interface layout and color scheme are adjusted to take the user's emotions into consideration.

[0791] Step 5:

[0792] The user makes necessary decisions based on the displayed feedback and sends instructions from the terminal to the automated machine. This automatically starts or adjusts the work. The input is the user's action, and the output is the instruction to start the machine's operation. Finally, the machine operates according to the new instructions.

[0793] 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.

[0794] 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.

[0795] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0796] 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.

[0797] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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."

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0814] The following is further disclosed regarding the embodiments described above.

[0815] (Claim 1)

[0816] A means of receiving instructions from workers via a mobile information terminal,

[0817] To process the aforementioned instructions, a means for analyzing data using generative artificial intelligence and transmitting the generated results to the operator,

[0818] Means for visually displaying the generated results on a mobile information terminal,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, further comprising means for communicating with an equipment database at a work site and obtaining relevant data based on instructions.

[0822] (Claim 3)

[0823] The system according to claim 1, further comprising means for providing the worker with the following recommended actions based on the analysis results.

[0824] "Example 1"

[0825] (Claim 1)

[0826] A means of receiving instructions from the operator via a portable computing device,

[0827] To process the aforementioned instructions, means for analyzing information using a generative intelligence system and transmitting the generated results to the operator,

[0828] Means for visually displaying the generated results on a portable computer,

[0829] A means of analyzing data acquired using generative artificial intelligence to evaluate the deterioration status of parts,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, further comprising means for communicating with equipment information resources at a work site and obtaining relevant information based on instructions.

[0833] (Claim 3)

[0834] The system according to claim 1, further comprising means for providing the operator with the following recommended actions based on the analysis results.

[0835] "Application Example 1"

[0836] (Claim 1)

[0837] A means of receiving instructions from the operator via a mobile information terminal,

[0838] To process the aforementioned instructions, means for analyzing information using generative artificial intelligence and transmitting the generated results to the operator,

[0839] Means for visually displaying the generated results on a mobile information terminal,

[0840] Based on the analysis results, a means to provide the following recommended maintenance actions,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, further comprising means for communicating with stored information of operating equipment and obtaining relevant information based on instructions.

[0844] (Claim 3)

[0845] The system according to claim 1, further comprising means for providing the operator with the following recommended actions based on information analysis.

[0846] "Example 2 of combining an emotion engine"

[0847] (Claim 1)

[0848] In order to receive instructions from workers, a means is used via an information processing device,

[0849] A means of using an emotion recognition engine to recognize the emotional state of a worker,

[0850] A means for analyzing data using generative artificial intelligence based on the aforementioned instructions and emotional state, and for generating optimized results,

[0851] The generated results are visually displayed on an information processing device, and the display content is customized according to the emotional state.

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, further comprising means for obtaining relevant information via a communication network in order to provide equipment information.

[0855] (Claim 3)

[0856] The system according to claim 1, further comprising means for providing support information suitable for the worker based on emotion recognition and data analysis results.

[0857] "Application example 2 when combining with an emotional engine"

[0858] (Claim 1)

[0859] A means of receiving instructions from the user via a mobile device,

[0860] To process the aforementioned instructions, the means includes an artificial intelligence engine to analyze data and generate optimized results that take into account the user's emotional state,

[0861] The generated results are visually displayed on a mobile information terminal in a user-friendly format, and the interface is adjusted based on the emotional state.

[0862] A means for transmitting instructions to control the operation of an automated machine in conjunction with it,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, further comprising means for communicating with a workplace equipment database and obtaining relevant information based on instructions.

[0866] (Claim 3)

[0867] The system according to claim 1, further comprising means for providing the following recommended actions based on the analysis results and the user's emotional state. [Explanation of symbols]

[0868] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving instructions from workers via a mobile information terminal, To process the aforementioned instructions, a means for analyzing data using generative artificial intelligence and transmitting the generated results to the operator, Means for visually displaying the generated results on a mobile information terminal, A system that includes this.

2. The system according to claim 1, further comprising means for communicating with an equipment database at a work site and obtaining relevant data based on instructions.

3. The system according to claim 1, further comprising means for providing the worker with the following recommended actions based on the analysis results.

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