system

Abstract

We provide the system. [Solution] A terminal device for receiving work instructions, A speech recognition means that converts received audio data into text data, An image processing means that analyzes the operation content from the received screen data, A generation means for generating a business manual using the text data and the image data, A means of saving the generated work manual and referring to it when performing the work again, A system including control means for automatically executing tasks based on saved work manuals.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional business handovers, creating a work manual requires a great deal of time and effort, and it is particularly difficult to create in small and medium-sized enterprises with a shortage of manpower. Furthermore, due to the difficulty in continuing operations in the event of employee retirement or transfer, it was necessary to establish an efficient and sustainable business operation method.

Means for Solving the Problems

[0005] This invention provides a system in which an AI recognizes the content of work instructions given only once via voice and screen operation and automatically generates a work manual. This reduces the burden of creating work manuals and enables the AI ​​to automatically perform subsequent tasks based on the generated manual. This system includes a terminal device for receiving work instructions, voice recognition means, image processing means, generation means, storage means, and control means, and effectively solves the problem through a combination of these.

[0006] A "terminal device" is a device that allows users to give work instructions using voice and screen operations.

[0007] "Speech recognition means" refers to technology that converts speech data into text data.

[0008] "Image processing means" refers to technology that analyzes user actions from received screen data.

[0009] "Generation method" refers to the function that creates a business manual using the analyzed text data and image data.

[0010] "Storage method" refers to the function of recording the generated business manual for later reference and implementation.

[0011] "Control means" refers to a function that automatically executes tasks based on saved work manuals. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]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.

Modes for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the labeled 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.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled 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.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] The system of the present invention begins with a user communicating work instructions to an AI via a terminal device using voice and screen operations. When the user explains the work procedure using the terminal device, the terminal simultaneously records the operations and voice. The voice data is transmitted from the terminal to a server, where it is converted into text data using speech recognition means. At the same time, the captured video of the screen recorded by the terminal is analyzed by the server's image processing means.

[0034] After analysis, the server combines this data using a generation mechanism and automatically generates a visually appealing business manual. This manual can also be manually edited by the user. The generated business manual is recorded in a cloud-based storage system and referenced when performing future tasks.

[0035] In the future, when a user requests a similar task, the server uses its control mechanisms, based on stored task manuals, to send instructions to the terminal for the AI ​​to automatically perform the task. This includes business processes such as screen input, form completion, and data submission. For example, if a user explains the expense claim procedure, the system will automate the relevant data entry and allow the user to submit the necessary documents online in the future.

[0036] Thus, this invention significantly reduces the time and effort required for handing over tasks, enabling the continuous operation of business. It is a system that contributes to increased efficiency and automation of operations, particularly in fields where labor shortages are a major problem.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The user operates the terminal device, giving voice instructions for tasks and performing screen operations. The terminal records the voice and starts capturing the screen.

[0040] Step 2:

[0041] After the user completes the explanation of the work procedure, the terminal sends the recorded audio data and captured screen video data to the server.

[0042] Step 3:

[0043] The server processes the received audio data through a speech recognition system, converting the audio data into text data. Appropriate punctuation is then inserted into the converted text, and it is formatted into an easily understandable format.

[0044] Step 4:

[0045] The server processes video data frame by frame and uses image processing technology to analyze user actions. It identifies key actions and input points and extracts related image data.

[0046] Step 5:

[0047] The server uses a generation mechanism to integrate text converted from speech with image data obtained through analysis, creating a visually easy-to-understand operational manual. This manual details procedures, precautions, and necessary tasks.

[0048] Step 6:

[0049] The generated business manuals are saved to a storage device on the server and made available for future reference via cloud storage.

[0050] Step 7:

[0051] When a user requests automation of a task for future tasks, the server, based on the stored task manual, uses control mechanisms to deliver instructions to the terminal for the AI ​​to execute the task. The terminal then performs the task according to the automation script.

[0052] (Example 1)

[0053] 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."

[0054] In today's world, where efficiency and automation are paramount, effectively recording work instructions, sharing information, and utilizing it for future tasks is a challenge. In particular, methods that simultaneously record voice instructions and screen operations, and visually present work procedures, are crucial.

[0055] 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.

[0056] In this invention, the server includes means for receiving work instructions using an information processing device, recognition means for converting voice information into text information, recognition means for analyzing operation content from screen information, and creation means for creating work instruction manuals using the text information and screen information. This enables integrated recording of work instructions and automation of operations.

[0057] An "information processing device" is a mechanical or electronic device used to receive work instructions and has the function of acquiring voice information and screen information.

[0058] "Recognition means" refers to technical methods or devices for converting audio information into text information or for analyzing operation content from screen information.

[0059] "Creation means" refers to a function or device for creating a work instruction manual using the aforementioned text information and screen information.

[0060] "Recording means" refers to a technical method or device for saving the created work instruction manual on an information recording medium and referring to it when performing the work in the future.

[0061] "Transmission means" refers to a method or device for transmitting audio information and screen information together to a computer.

[0062] "Management means" refers to a control function or device for automatically executing tasks based on stored work instruction manuals.

[0063] "Editing means" refers to functions or devices that allow users to manually modify or change the created work instruction manuals.

[0064] The embodiments for carrying out the present invention are shown below.

[0065] This system achieves business automation by combining three elements: users, terminals, and servers.

[0066] Users input work instructions via voice through a terminal device, and also perform screen operations during this process. The terminal is equipped with input devices, recording devices, and display devices necessary to simultaneously record this voice data and screen operations.

[0067] Next, the terminal sends the recorded audio data to the server. The server converts the audio data into text data using speech recognition software. Widely used speech recognition software is employed for this process. In parallel, the screen capture data sent from the terminal is analyzed on the server side using an image processing library. This analysis specifically identifies the steps the user took on the screen.

[0068] Subsequently, the server uses a generative AI model to integrate these voice-text and image data to create a work instruction manual. This manual is presented to the user in a visually easy-to-understand format and saved to cloud storage for later reference. This generation process allows users to efficiently document work procedures. An example of a prompt message might be, "Starting the expense claim process."

[0069] In the future, when a user performs the same task, the server will use the stored task instructions to automate the entire process. This allows the user to complete the task efficiently without having to perform cumbersome operations again.

[0070] In this way, the present invention enables the achievement of increased efficiency and automation in business operations, and is particularly effective in fields where labor shortages are a problem.

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

[0072] Step 1:

[0073] The user inputs work instructions by voice and simultaneously performs related screen operations on the terminal. The input voice data is captured by the terminal's microphone, and the screen operations are recorded as screen captures. The input for this step consists of voice instructions and on-screen actions, and the output consists of audio and video files. Each file is temporarily stored in the terminal's storage.

[0074] Step 2:

[0075] The terminal sends the acquired audio file to the server, which processes the audio data into text data. On the server side, this audio file is analyzed using speech recognition technology and the corresponding text data is output. When using Google® Cloud Speech-to-Text, the audio is appropriately converted to text by the speech language model, and this converted text is recorded on the server.

[0076] Step 3:

[0077] Similarly, captured video files sent from the terminal are analyzed on the server using image processing technology to determine the operation procedures. The video file is taken as input, and the on-screen operations are identified frame by frame. For example, buttons clicked by the user and the contents of forms entered are identified, and this information is output in text format. An image recognition library is used for this process.

[0078] Step 4:

[0079] The server uses a generative AI model to create work instruction manuals by combining text converted from speech with operation details analyzed through image processing. Inputs are speech text and operation text, and the output is an instruction manual containing visualized work procedures. This generative AI model utilizes Text-to-Image and Text-to-Text technologies to organize information in a way that users can intuitively understand.

[0080] Step 5:

[0081] The generated work instructions are saved to cloud storage by the server and prepared for future reference and automation. The server uploads the generated files to the cloud and manages the link to the storage location. The output of this step is an online access link to the work instructions, making them accessible to users at any time.

[0082] Step 6:

[0083] The next time a user issues the same work instruction, the server will send an automated task execution control command to the terminal based on the saved work instruction document. Based on this information, the server will generate an automated script and instruct the terminal to execute it. The output is the automated execution of the process requested by the user, resulting in efficient work processing that does not require manual intervention.

[0084] (Application Example 1)

[0085] 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."

[0086] In manufacturing environments, there is a need to streamline the handover and setup of work procedures and the operation of maintenance tasks. However, manual handover and task execution are time-consuming and labor-intensive. Furthermore, since these processes depend on the skills of the workers, there is a concern that variations may occur depending on the task. To solve this problem, there is a need for a system that enables effective recording and automation of work procedures.

[0087] 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.

[0088] In this invention, the server includes an information processing device for receiving work instructions, a speech recognition means for converting received voice information into text information, and an image analysis means for analyzing the procedure content from received operation information. This enables efficient recording of work procedures and automation in the manufacturing process.

[0089] An "information processing device" is a device that receives voice information and operation information and transmits work instructions.

[0090] "Speech recognition means" refers to a technology that has the function of analyzing received speech information and converting it into text information.

[0091] "Image analysis means" refers to a method for analyzing the procedure content from operation information and extracting necessary information.

[0092] "Generation means" refers to a process that automatically creates work procedure manuals using audio information and procedural information.

[0093] A "recording mechanism" is a system for saving generated work procedure manuals and managing them so that they can be easily referenced later.

[0094] "Control means" refers to methods or devices for automatically executing tasks based on recorded work procedures.

[0095] "Manufacturing process control means" refers to technology that applies operational procedures to setting up and maintaining a manufacturing line and has the function of automating these tasks.

[0096] "Communication means" refers to technology for packaging voice information and procedural information and transmitting it to an information processing device.

[0097] "Revision means" refers to methods or functions that enable users to edit and modify the generated work procedure manuals.

[0098] The system that realizes this application example aims to automate work procedures in manufacturing sites. The system includes an information processing device that receives various types of information, a speech recognition means that converts voice information into text information, and an image analysis means that analyzes the procedure content from operation information.

[0099] First, the terminal receives work instructions from the factory. The user uses an information processing device such as a smartphone or tablet to operate the screen while explaining the manufacturing line setup and maintenance procedures by voice. This voice data and operation information are recorded in the terminal and converted into text data by a voice recognition system.

[0100] Following this, the server analyzes the voice data and operation information in the cloud. It processes the data using a speech recognition API (e.g., Google Speech-to-Text) and an image processing library (e.g., OpenCV). From the analyzed information, the generation system automatically creates a work procedure manual.

[0101] This work procedure manual is recorded and referenced for future work execution. The recording means uses a cloud storage service to store the work procedure manual. The control means allows for the automatic control of the manufacturing process based on the recorded work procedure manual.

[0102] As a concrete example, when workers on a factory painting line set parameters such as paint type, application speed, and drying time, this system allows for efficient recording of the work procedure, enabling automatic settings for subsequent uses.

[0103] As an example of a prompt message to the generating AI model, you could give the instruction, "Analyze this audio data and video of screen operations, and generate a procedural manual for future automated setup of the painting line."

[0104] In this way, we provide a system that improves work efficiency in manufacturing sites, compensates for labor shortages, and enables tasks to be performed with high precision.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The user uses an information processing device to perform screen operations while explaining work instructions verbally. Specifically, the user speaks into the microphone of their smartphone and simultaneously operates the touchscreen. The input consists of voice data and operation information, which are recorded on the device.

[0108] Step 2:

[0109] The device sends the recorded audio data to a speech recognition API, which converts it into text data. Specifically, the device calls a speech recognition service in the cloud (e.g., Google Speech-to-Text) and uploads the audio file. The output is text data. This processing makes the audio information into a format that can be analyzed.

[0110] Step 3:

[0111] The server analyzes the recorded operation information using image analysis tools. Specifically, the server uses an image processing library (e.g., OpenCV) to analyze what operations were performed from the captured screen operations. The input is operation information, and the output is data detailing the procedure. This digitizes the flow of operations.

[0112] Step 4:

[0113] The server integrates the analyzed text data and procedure data, and automatically generates a business procedure manual using a generation method. Specifically, the server utilizes a generation AI model to combine both sets of data into a format instructed by prompts. The generated business procedure manual is then output. This creates a visualized procedure manual.

[0114] Step 5:

[0115] The server saves the generated work procedure manuals to cloud storage using a recording mechanism. Specifically, the server uploads and saves the data to the storage service via an API. The input is the work procedure manual, and the output is a file stored in the cloud. The manual is retained for future reference.

[0116] Step 6:

[0117] In subsequent instances, when a user performs a similar task, the server will refer to the recorded work procedure manual and automatically execute the task using control mechanisms. For example, the equipment settings on a manufacturing line will be automatically configured. The input is the saved work procedure manual, and the output is the configured manufacturing equipment. This enables efficient and consistent task execution.

[0118] 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.

[0119] The system of the present invention not only records the process of a user providing work instructions to an AI via a terminal device using voice and screen operations, but also has the function of simultaneously recognizing the user's emotional state. When a user explains a task on the terminal, the terminal captures the voice and screen, and in addition, uses emotion recognition means to analyze the user's facial expressions and tone of voice to acquire emotional data.

[0120] The captured audio data is sent to the server and converted into text data by the server's speech recognition system. Simultaneously, screen data is analyzed by the server's image processing system, and image data related to user actions is extracted. Furthermore, the emotional data analyzed by the emotion engine is considered when generating the operational manual.

[0121] The server uses a generation mechanism to combine text converted from speech, analyzed image data, and emotion data to generate a work manual in a format appropriate to the user's current emotional state. For example, if the user is confused, the explanation can be made more detailed, and additional support information can be included in the manual. The generated work manual and emotion data are stored in the cloud and used for future work execution and as internal user feedback.

[0122] The next time a user requests a similar task, the server will send optimized tasks to the terminal based on stored manuals and emotional data, and the AI ​​will automatically perform the tasks. For example, if a user feels anxious about a particular procedure, the system can automatically generate a more reassuring script based on past emotional data and use it when performing the task.

[0123] Thus, by considering user emotions, this invention not only improves operational efficiency but also provides a user-friendly environment. It appropriately utilizes emotional information to improve the continuity and accuracy of operations.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The user uses a terminal device to explain work instructions through voice and screen operations. During this process, the terminal records the user's voice and captures the screen. Furthermore, the terminal uses its built-in camera and microphone to analyze the user's facial expressions and voice tone, and emotional data is acquired using emotion recognition technology.

[0127] Step 2:

[0128] The device packages the recorded audio data, screen capture data, and analyzed emotion data, and sends them to the server.

[0129] Step 3:

[0130] The server converts the received audio data into text data using speech recognition technology. Appropriate punctuation is inserted into the text data, and it is organized into work instructions.

[0131] Step 4:

[0132] The server analyzes the received screen capture data frame by frame, uses image processing to analyze the user's actions, and extracts image data of important operation steps.

[0133] Step 5:

[0134] Based on the results of emotion recognition, the server adjusts the content of the work manual according to the user's psychological state indicated by the analyzed emotion data. For example, if the user is feeling anxious, the manual will be adjusted to include more detailed procedural explanations and additional supplementary information.

[0135] Step 6:

[0136] The server uses generation tools to combine analyzed text data, image data, and sentiment data to generate a business manual. This manual will reflect the user's psychological state.

[0137] Step 7:

[0138] The generated work manuals and emotional data are recorded on a server storage system and managed on the cloud. This information will be available for reference when performing tasks in the future.

[0139] Step 8:

[0140] When a user instructs the next task to be performed, the server delivers an optimized work process to the terminal based on stored work manuals and sentiment data. The terminal then executes an AI-powered automated script to perform the task. During this process, the user experience is optimized based on past sentiment data.

[0141] (Example 2)

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

[0143] Conventional work instruction systems generated work guidelines without considering the user's emotional state, thus failing to adequately improve user understanding and confidence. Furthermore, because tasks were automated based solely on past operation data, optimization that considered the user's emotional aspects was not achieved. This resulted in shortcomings in user experience and operational efficiency.

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

[0145] In this invention, the server includes speech recognition means for converting speech information into text information, image processing means for analyzing operation content from image information, and emotion recognition means for acquiring emotion information from facial expressions and tone of voice. This enables the generation of work guidelines that take into account the user's emotional state and optimization based on past emotion information.

[0146] An "information processing device" is a device used by users to input work instructions and is equipped with the function of processing and transmitting data such as voice and images.

[0147] "Speech recognition means" refers to a technical means that has the function of converting received speech information into text information.

[0148] "Image processing means" refers to a technical means that analyzes the operation content from received image information and acquires it as data.

[0149] An "emotion recognition tool" is a tool that uses a user's facial expressions and tone of voice to identify their emotions and acquire that information.

[0150] "Generation means" refers to means equipped with the function of generating appropriate business guidelines using text information, image information, and emotional information.

[0151] "Storage methods" refer to technical means that enable the creation of business guidelines to be stored in a database or cloud, and referenced in the future.

[0152] A "control means" is a technical means that has the function of automatically performing tasks based on stored work guidelines.

[0153] "Transmission means" refers to means equipped with the function of packaging audio and image information and transmitting it to a network-connected device.

[0154] An "editing tool" is a means that allows users to modify or change the generated business guidelines.

[0155] The system of this invention is designed to support and streamline the process by which users input work instructions. This system generates personalized work guidelines tailored to the user through the acquisition and analysis of voice data, image data, and emotion data.

[0156] First, the user inputs work instructions by voice using a terminal. The terminal captures this voice and converts it into text information using speech recognition technology. In this process, for example, a commonly used speech recognition API is used as the speech recognition software.

[0157] Next, the user's screen operations are also recorded by the device. Screen capture software is used to acquire image data. This ensures a detailed record of the user's specific steps.

[0158] Furthermore, the device uses emotion recognition to analyze the user's facial expressions and tone of voice to acquire emotional data. This process utilizes an emotion analysis engine, which can determine the user's emotional state from changes in facial expressions and tone of voice.

[0159] All of this data is sent to a server, which uses a generation mechanism to generate business guidelines that encompass this data. Using a generation AI model, voice, image, and emotion data are integrated to create customized business guidelines that respond to the user's emotional state.

[0160] For example, if a user feels anxious while learning the procedures for migrating to a new system, the system can take that emotional data into account and generate operational guidelines that include detailed explanations and supplementary information. It can also provide less stressful operating procedures that reduce user anxiety by comparing them with past records.

[0161] An example of a prompt message might be, "Please create instructions to alleviate any anxiety you may feel about the installation process for new software." This allows for flexible adaptation of work guidelines to the specific situation.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The user inputs work instructions by voice using a terminal. The terminal captures the input voice and saves it as audio data. The input is the user's voice, which is converted into text information using speech recognition software. Specifically, the terminal's microphone is used to collect voice in real time and send it to a recognition API. The output is text data.

[0165] Step 2:

[0166] The terminal records the screen operations performed by the user while they are receiving work instructions. The terminal uses a screen capture function to acquire the entire sequence of operations as image data. The input is the user's screen operation actions, which are captured as snapshots and analyzed by an image processing algorithm. The output is image data that provides a detailed record of the user's operation steps.

[0167] Step 3:

[0168] The device uses emotion recognition to analyze the user's facial expressions and voice tone. This is done using data collected through the camera and microphone. The input consists of the user's facial expression data and voice tone, which are identified as emotional states by the emotion analysis engine. Specifically, it analyzes changes in facial expressions and voice tone to generate emotion data. The output is data indicating the user's emotional state.

[0169] Step 4:

[0170] The device sends collected audio, image, and emotion data to the server. This transmission process, which includes data packaging, is securely transferred to the server over the network. The input is all data collected by the device, and the output is the packaged, integrated data sent to the server.

[0171] Step 5:

[0172] The server uses the received data to generate business guidelines through a generative AI model. The input consists of text data, image data, and sentiment data collected on the server, which are integrated to create customized guidelines. The output is business guidelines that reflect the user's current emotional state. Based on the prompt text, the generative AI model suggests appropriate guidance content.

[0173] Step 6:

[0174] The server saves the generated business guidelines to the cloud. The input is the generated business guidelines, which are recorded in the database for future reference using a storage method. Specifically, this involves writing to the data storage system. The output is the business guidelines stored in the cloud.

[0175] Step 7:

[0176] The next time a work instruction is given, the server will send the most suitable work task to the terminal based on the stored work guidelines. The input consists of past work guidelines and sentiment data stored in the cloud, and new work guidelines are generated and sent based on this. The output is instructions for performing the work adapted to the user. The script is adjusted according to the user's emotional state.

[0177] (Application Example 2)

[0178] 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".

[0179] Conventional business support systems automate basic instructions without considering user emotions, resulting in instructions that don't match the user's emotional state and fail to be fully effective. Furthermore, it was difficult for users to properly edit instructional materials. For home robots to provide users with comfortable and personalized services, an approach that considers user emotions is necessary. Therefore, there is a need for a system that automatically provides appropriate instruction tailored to the user's emotions, while also allowing users to easily edit instructional materials themselves.

[0180] 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.

[0181] In this invention, the server includes an information input device for receiving work instructions, a language recognition means for converting received voice information into text information, a video processing means for analyzing operation content from received screen information, an emotion recognition means for analyzing the user's emotional state and adjusting the content of work instruction materials accordingly, a recording means for saving the generated work instruction materials and referring to them when performing work in the future, a management and control means for automatically performing work based on the saved work instruction materials, and a modification means for allowing the user to adjust the generated work instruction materials. This enables work instruction that reflects the user's emotions, allows the user to easily edit the work instruction materials, and makes the services provided by the home robot even more user-friendly and effective.

[0182] An "information input device" is a device used to receive work instructions and emotional states from users.

[0183] "Language recognition means" refers to technology that has the function of analyzing received audio information and converting it into textual information.

[0184] "Image processing means" refers to technology used to analyze the operation content based on the received screen information.

[0185] "Emotion recognition means" refers to technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state.

[0186] "Generation means" refers to technology for creating work instruction materials using text information and screen information.

[0187] "Recording means" refers to a function for saving generated work instruction materials so that they can be referenced later.

[0188] "Management and control means" refers to a function that automatically performs tasks based on stored work instruction materials.

[0189] "Modification means" refers to a function that allows users to adjust and edit the generated work instruction materials.

[0190] Embodiments of this invention relate to a user assistance system using a home robot. The system integrates an information input device, a language recognition function, an image processing function, an emotion recognition function, a generation function, a recording function, a management and control function, and a modification function. The user inputs work instructions by voice through the home robot, and the information input device captures the voice and screen information.

[0191] This information is converted into text using language recognition means, and then image processing means analyze the screen information and extract the operation content. Subsequently, emotion recognition means analyze the user's facial expressions and tone of voice to identify their emotional state. This series of data is then transmitted to the cloud by a server.

[0192] The server uses a generation function to create work instruction materials that combine text information, operation details, and emotional states. These materials are saved using a recording function and automatically used for subsequent work executions by a management control function. Users can edit the generated materials as needed using a modification function.

[0193] As a concrete example, when a home robot cleans, it can provide a message tailored to the user's emotions while they rest, such as, "Thank you for your hard work. I'll finish cleaning while you rest, so please relax." In this way, the system can provide optimal support that responds to the user's emotions.

[0194] An example of a prompt message is as follows:

[0195] "Design the emotional responses for a robot that helps with household chores. Think of suggestions for when the user is relaxed and support options for when they are busy."

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The terminal receives voice commands from the user. The input is the user's voice data. The terminal acquires this voice data through the microphone and temporarily stores it in its memory.

[0199] Step 2:

[0200] The terminal converts received audio data into text information using language recognition means. The input is audio data, and the output is corresponding text data. Speech recognition software is used to analyze the audio signal and convert it into text format.

[0201] Step 3:

[0202] The terminal captures user screen operations and analyzes them using video processing equipment. The input is screen data, and the output is data indicating the operations performed. An image analysis algorithm is used to identify changes in the screen and extract the operations performed.

[0203] Step 4:

[0204] The device uses a camera to record the user's facial expressions and analyzes them using emotion recognition technology. The input is video data of the user, and the output is data indicating their emotional state. It analyzes facial features and infers emotions from facial expressions.

[0205] Step 5:

[0206] The server receives text data, operation data, and emotion data sent from the terminal. This data is then integrated and stored in the cloud.

[0207] Step 6:

[0208] The server uses a generation function to combine received data and generate work instruction materials. Inputs include text data, operation data, and sentiment data, while output is work instruction materials. The data is integrated and formatted into a set of instructions.

[0209] Step 7:

[0210] The server records the generated work instruction materials and uses its management and control functions to automatically utilize them during the next work execution. These materials will be referenced when giving the next instructions.

[0211] Step 8:

[0212] Users can edit and adjust work instruction materials as needed through the editing function. Users can also review and modify the materials using the interface.

[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 begins with a user communicating work instructions to an AI via a terminal device using voice and screen operations. When the user explains the work procedure using the terminal device, the terminal simultaneously records the operations and voice. The voice data is transmitted from the terminal to a server, where it is converted into text data using speech recognition means. At the same time, the captured video of the screen recorded by the terminal is analyzed by the server's image processing means.

[0230] After analysis, the server combines this data using a generation mechanism and automatically generates a visually appealing business manual. This manual can also be manually edited by the user. The generated business manual is recorded in a cloud-based storage system and referenced when performing future tasks.

[0231] In the future, when a user requests a similar task, the server uses its control mechanisms, based on stored task manuals, to send instructions to the terminal for the AI ​​to automatically perform the task. This includes business processes such as screen input, form completion, and data submission. For example, if a user explains the expense claim procedure, the system will automate the relevant data entry and allow the user to submit the necessary documents online in the future.

[0232] Thus, this invention significantly reduces the time and effort required for handing over tasks, enabling the continuous operation of business. It is a system that contributes to increased efficiency and automation of operations, particularly in fields where labor shortages are a major problem.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The user operates the terminal device, giving voice instructions for tasks and performing screen operations. The terminal records the voice and starts capturing the screen.

[0236] Step 2:

[0237] After the user completes the explanation of the work procedure, the terminal sends the recorded audio data and captured screen video data to the server.

[0238] Step 3:

[0239] The server processes the received audio data through a speech recognition system, converting the audio data into text data. Appropriate punctuation is then inserted into the converted text, and it is formatted into an easily understandable format.

[0240] Step 4:

[0241] The server processes video data frame by frame and uses image processing technology to analyze user actions. It identifies key actions and input points and extracts related image data.

[0242] Step 5:

[0243] The server uses a generation mechanism to integrate text converted from speech with image data obtained through analysis, creating a visually easy-to-understand operational manual. This manual details procedures, precautions, and necessary tasks.

[0244] Step 6:

[0245] The generated business manuals are saved to a storage device on the server and made available for future reference via cloud storage.

[0246] Step 7:

[0247] When a user requests automation of a task for future tasks, the server, based on the stored task manual, uses control mechanisms to deliver instructions to the terminal for the AI ​​to execute the task. The terminal then performs the task according to the automation script.

[0248] (Example 1)

[0249] 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."

[0250] In today's world, where efficiency and automation are paramount, effectively recording work instructions, sharing information, and utilizing it for future tasks is a challenge. In particular, methods that simultaneously record voice instructions and screen operations, and visually present work procedures, are crucial.

[0251] 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.

[0252] In this invention, the server includes means for receiving work instructions using an information processing device, recognition means for converting voice information into text information, recognition means for analyzing operation content from screen information, and creation means for creating work instruction manuals using the text information and screen information. This enables integrated recording of work instructions and automation of operations.

[0253] An "information processing device" is a mechanical or electronic device used to receive work instructions and has the function of acquiring voice information and screen information.

[0254] "Recognition means" refers to technical methods or devices for converting audio information into text information or for analyzing operation content from screen information.

[0255] "Creation means" refers to a function or device for creating a work instruction manual using the aforementioned text information and screen information.

[0256] "Recording means" refers to a technical method or device for saving the created work instruction manual on an information recording medium and referring to it when performing the work in the future.

[0257] "Transmission means" refers to a method or device for transmitting audio information and screen information together to a computer.

[0258] "Management means" refers to a control function or device for automatically executing tasks based on stored work instruction manuals.

[0259] "Editing means" refers to functions or devices that allow users to manually modify or change the created work instruction manuals.

[0260] The embodiments for carrying out the present invention are shown below.

[0261] This system achieves business automation by combining three elements: users, terminals, and servers.

[0262] Users input work instructions via voice through a terminal device, and also perform screen operations during this process. The terminal is equipped with input devices, recording devices, and display devices necessary to simultaneously record this voice data and screen operations.

[0263] Next, the terminal sends the recorded audio data to the server. The server converts the audio data into text data using speech recognition software. Widely used speech recognition software is employed for this process. In parallel, the screen capture data sent from the terminal is analyzed on the server side using an image processing library. This analysis specifically identifies the steps the user took on the screen.

[0264] Subsequently, the server uses a generative AI model to integrate these voice-text and image data to create a work instruction manual. This manual is presented to the user in a visually easy-to-understand format and saved to cloud storage for later reference. This generation process allows users to efficiently document work procedures. An example of a prompt message might be, "Starting the expense claim process."

[0265] In the future, when a user performs the same task, the server will use the stored task instructions to automate the entire process. This allows the user to complete the task efficiently without having to perform cumbersome operations again.

[0266] In this way, the present invention enables the achievement of increased efficiency and automation in business operations, and is particularly effective in fields where labor shortages are a problem.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The user inputs work instructions by voice and simultaneously performs related screen operations on the terminal. The input voice data is captured by the terminal's microphone, and the screen operations are recorded as screen captures. The input for this step consists of voice instructions and on-screen actions, and the output consists of audio and video files. Each file is temporarily stored in the terminal's storage.

[0270] Step 2:

[0271] The device sends the acquired audio file to the server, which processes the audio data into text data. On the server side, this audio file is analyzed using speech recognition technology and the corresponding text data is output. When using Google Cloud Speech-to-Text, the audio is appropriately converted to text by the speech language model, and this converted text is recorded on the server.

[0272] Step 3:

[0273] Similarly, captured video files sent from the terminal are analyzed on the server using image processing technology to determine the operation procedures. The video file is taken as input, and the on-screen operations are identified frame by frame. For example, buttons clicked by the user and the contents of forms entered are identified, and this information is output in text format. An image recognition library is used for this process.

[0274] Step 4:

[0275] The server uses a generative AI model to create work instruction manuals by combining text converted from speech with operation details analyzed through image processing. Inputs are speech text and operation text, and the output is an instruction manual containing visualized work procedures. This generative AI model utilizes Text-to-Image and Text-to-Text technologies to organize information in a way that users can intuitively understand.

[0276] Step 5:

[0277] The generated work instructions are saved to cloud storage by the server and prepared for future reference and automation. The server uploads the generated files to the cloud and manages the link to the storage location. The output of this step is an online access link to the work instructions, making them accessible to users at any time.

[0278] Step 6:

[0279] The next time a user issues the same work instruction, the server will send an automated task execution control command to the terminal based on the saved work instruction document. Based on this information, the server will generate an automated script and instruct the terminal to execute it. The output is the automated execution of the process requested by the user, resulting in efficient work processing that does not require manual intervention.

[0280] (Application Example 1)

[0281] 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."

[0282] In a manufacturing site, there is a need to transfer and set up business procedures and improve the efficiency of maintenance work. However, there are problems such as manual transfer and business execution requiring time and labor. In addition, since it depends on the skills of the workers, there is concern that variations may occur depending on the work. To solve this problem, it is necessary to provide a system that enables effective recording and automation of business procedures.

[0283] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes an information processing apparatus for receiving business instructions, a voice recognition means for converting the received voice information into text information, and an image analysis means for analyzing the procedure content from the received operation information. As a result, efficient recording of business procedures and automation in the manufacturing process become possible.

[0285] The "information processing apparatus" is a device that receives voice information and operation information and transmits business instructions.

[0286] The "voice recognition means" is a technology having a function of analyzing the received voice information and converting it into text information.

[0287] The "image analysis means" is a method for analyzing the procedure content from operation information and extracting necessary information.

[0288] The "generation means" is a process for automatically creating a business procedure manual using voice information and procedure information.

[0289] The "recording means" is a mechanism for storing the generated business procedure manual and managing it so that it can be easily referred to later.

[0290] The "control means" is a method or device for automatically implementing business based on the recorded business procedure manual.

[0291] "Manufacturing process control means" refers to technology that applies operational procedures to setting up and maintaining a manufacturing line and has the function of automating these tasks.

[0292] "Communication means" refers to technology for packaging voice information and procedural information and transmitting it to an information processing device.

[0293] "Revision means" refers to methods or functions that enable users to edit and modify the generated work procedure manuals.

[0294] The system that realizes this application example aims to automate work procedures in manufacturing sites. The system includes an information processing device that receives various types of information, a speech recognition means that converts voice information into text information, and an image analysis means that analyzes the procedure content from operation information.

[0295] First, the terminal receives work instructions from the factory. The user uses an information processing device such as a smartphone or tablet to operate the screen while explaining the manufacturing line setup and maintenance procedures by voice. This voice data and operation information are recorded in the terminal and converted into text data by a voice recognition system.

[0296] Following this, the server analyzes the voice data and operation information in the cloud. It processes the data using a speech recognition API (e.g., Google Speech-to-Text) and an image processing library (e.g., OpenCV). From the analyzed information, the generation system automatically creates a work procedure manual.

[0297] This work procedure manual is recorded and referenced for future work execution. The recording means uses a cloud storage service to store the work procedure manual. The control means allows for the automatic control of the manufacturing process based on the recorded work procedure manual.

[0298] As a concrete example, when workers on a factory painting line set parameters such as paint type, application speed, and drying time, this system allows for efficient recording of the work procedure, enabling automatic settings for subsequent uses.

[0299] As an example of a prompt message to the generating AI model, you could give the instruction, "Analyze this audio data and video of screen operations, and generate a procedural manual for future automated setup of the painting line."

[0300] In this way, we provide a system that improves work efficiency in manufacturing sites, compensates for labor shortages, and enables tasks to be performed with high precision.

[0301] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0302] Step 1:

[0303] The user uses an information processing device to perform screen operations while explaining work instructions verbally. Specifically, the user speaks into the microphone of their smartphone and simultaneously operates the touchscreen. The input consists of voice data and operation information, which are recorded on the device.

[0304] Step 2:

[0305] The device sends the recorded audio data to a speech recognition API, which converts it into text data. Specifically, the device calls a speech recognition service in the cloud (e.g., Google Speech-to-Text) and uploads the audio file. The output is text data. This processing makes the audio information into a format that can be analyzed.

[0306] Step 3:

[0307] The server analyzes the recorded operation information using image analysis means. Specifically, the server uses an image processing library (e.g., OpenCV) to analyze what operations were performed from the captured screen operations. The input is the operation information, and the output is the data of the procedure content. Thereby, the flow of operations is digitized.

[0308] Step 4:

[0309] The server integrates the analyzed text data and the procedure content data and automatically generates a business procedure manual using generation means. Specifically, the server utilizes a generation AI model to combine both data in the form instructed by the prompt sentence. The generated business procedure manual becomes the output. Thereby, a visualized procedure manual is created.

[0310] Step 5:

[0311] The server saves the generated business procedure manual to cloud storage using recording means. Specifically, the server uploads and saves the data to the storage service via the API. The input is the business procedure manual, and the output is the file saved on the cloud. The procedure manual is retained for future reference.

[0312] Step 6:

[0313] In subsequent times, when the user performs the same business, the server refers to the recorded business procedure manual and automatically executes the business using control means. As a specific example, the equipment settings of the manufacturing line are automatically performed. The input is the saved business procedure manual, and the output is the set manufacturing equipment. Efficient and consistent business execution becomes possible.

[0314] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0315] The system of the present invention not only records the process of a user providing work instructions to an AI via a terminal device using voice and screen operations, but also has the function of simultaneously recognizing the user's emotional state. When a user explains a task on the terminal, the terminal captures the voice and screen, and in addition, uses emotion recognition means to analyze the user's facial expressions and tone of voice to acquire emotional data.

[0316] The captured audio data is sent to the server and converted into text data by the server's speech recognition system. Simultaneously, screen data is analyzed by the server's image processing system, and image data related to user actions is extracted. Furthermore, the emotional data analyzed by the emotion engine is considered when generating the operational manual.

[0317] The server uses a generation mechanism to combine text converted from speech, analyzed image data, and emotion data to generate a work manual in a format appropriate to the user's current emotional state. For example, if the user is confused, the explanation can be made more detailed, and additional support information can be included in the manual. The generated work manual and emotion data are stored in the cloud and used for future work execution and as internal user feedback.

[0318] The next time a user requests a similar task, the server will send optimized tasks to the terminal based on stored manuals and emotional data, and the AI ​​will automatically perform the tasks. For example, if a user feels anxious about a particular procedure, the system can automatically generate a more reassuring script based on past emotional data and use it when performing the task.

[0319] Thus, by considering user emotions, this invention not only improves operational efficiency but also provides a user-friendly environment. It appropriately utilizes emotional information to improve the continuity and accuracy of operations.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The user uses a terminal device to explain work instructions through voice and screen operations. During this process, the terminal records the user's voice and captures the screen. Furthermore, the terminal uses its built-in camera and microphone to analyze the user's facial expressions and voice tone, and emotional data is acquired using emotion recognition technology.

[0323] Step 2:

[0324] The device packages the recorded audio data, screen capture data, and analyzed emotion data, and sends them to the server.

[0325] Step 3:

[0326] The server converts the received audio data into text data using speech recognition technology. Appropriate punctuation is inserted into the text data, and it is organized into work instructions.

[0327] Step 4:

[0328] The server analyzes the received screen capture data frame by frame, uses image processing to analyze the user's actions, and extracts image data of important operation steps.

[0329] Step 5:

[0330] Based on the results of emotion recognition, the server adjusts the content of the work manual according to the user's psychological state indicated by the analyzed emotion data. For example, if the user is feeling anxious, the manual will be adjusted to include more detailed procedural explanations and additional supplementary information.

[0331] Step 6:

[0332] The server uses generation tools to combine analyzed text data, image data, and sentiment data to generate a business manual. This manual will reflect the user's psychological state.

[0333] Step 7:

[0334] The generated work manuals and emotional data are recorded on a server storage system and managed on the cloud. This information will be available for reference when performing tasks in the future.

[0335] Step 8:

[0336] When a user instructs the next task to be performed, the server delivers an optimized work process to the terminal based on stored work manuals and sentiment data. The terminal then executes an AI-powered automated script to perform the task. During this process, the user experience is optimized based on past sentiment data.

[0337] (Example 2)

[0338] 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".

[0339] Conventional work instruction systems generated work guidelines without considering the user's emotional state, thus failing to adequately improve user understanding and confidence. Furthermore, because tasks were automated based solely on past operation data, optimization that considered the user's emotional aspects was not achieved. This resulted in shortcomings in user experience and operational efficiency.

[0340] 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.

[0341] In this invention, the server includes speech recognition means for converting speech information into text information, image processing means for analyzing operation content from image information, and emotion recognition means for acquiring emotion information from facial expressions and tone of voice. This enables the generation of work guidelines that take into account the user's emotional state and optimization based on past emotion information.

[0342] An "information processing device" is a device used by users to input work instructions and is equipped with the function of processing and transmitting data such as voice and images.

[0343] "Speech recognition means" refers to a technical means that has the function of converting received speech information into text information.

[0344] "Image processing means" refers to a technical means that analyzes the operation content from received image information and acquires it as data.

[0345] An "emotion recognition tool" is a tool that uses a user's facial expressions and tone of voice to identify their emotions and acquire that information.

[0346] "Generation means" refers to means equipped with the function of generating appropriate business guidelines using text information, image information, and emotional information.

[0347] "Storage methods" refer to technical means that enable the creation of business guidelines to be stored in a database or cloud, and referenced in the future.

[0348] A "control means" is a technical means that has the function of automatically performing tasks based on stored work guidelines.

[0349] "Transmission means" refers to means equipped with the function of packaging audio and image information and transmitting it to a network-connected device.

[0350] An "editing tool" is a means that allows users to modify or change the generated business guidelines.

[0351] The system of this invention is designed to support and streamline the process by which users input work instructions. This system generates personalized work guidelines tailored to the user through the acquisition and analysis of voice data, image data, and emotion data.

[0352] First, the user inputs work instructions by voice using a terminal. The terminal captures this voice and converts it into text information using speech recognition technology. In this process, for example, a commonly used speech recognition API is used as the speech recognition software.

[0353] Next, the user's screen operations are also recorded by the device. Screen capture software is used to acquire image data. This ensures a detailed record of the user's specific steps.

[0354] Furthermore, the device uses emotion recognition to analyze the user's facial expressions and tone of voice to acquire emotional data. This process utilizes an emotion analysis engine, which can determine the user's emotional state from changes in facial expressions and tone of voice.

[0355] All of this data is sent to a server, which uses a generation mechanism to generate business guidelines that encompass this data. Using a generation AI model, voice, image, and emotion data are integrated to create customized business guidelines that respond to the user's emotional state.

[0356] For example, if a user feels anxious while learning the procedures for migrating to a new system, the system can take that emotional data into account and generate operational guidelines that include detailed explanations and supplementary information. It can also provide less stressful operating procedures that reduce user anxiety by comparing them with past records.

[0357] An example of a prompt message might be, "Please create instructions to alleviate any anxiety you may feel about the installation process for new software." This allows for flexible adaptation of work guidelines to the specific situation.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] The user inputs work instructions by voice using a terminal. The terminal captures the input voice and saves it as audio data. The input is the user's voice, which is converted into text information using speech recognition software. Specifically, the terminal's microphone is used to collect voice in real time and send it to a recognition API. The output is text data.

[0361] Step 2:

[0362] The terminal records the screen operations performed by the user while they are receiving work instructions. The terminal uses a screen capture function to acquire the entire sequence of operations as image data. The input is the user's screen operation actions, which are captured as snapshots and analyzed by an image processing algorithm. The output is image data that provides a detailed record of the user's operation steps.

[0363] Step 3:

[0364] The device uses emotion recognition to analyze the user's facial expressions and voice tone. This is done using data collected through the camera and microphone. The input consists of the user's facial expression data and voice tone, which are identified as emotional states by the emotion analysis engine. Specifically, it analyzes changes in facial expressions and voice tone to generate emotion data. The output is data indicating the user's emotional state.

[0365] Step 4:

[0366] The device sends collected audio, image, and emotion data to the server. This transmission process, which includes data packaging, is securely transferred to the server over the network. The input is all data collected by the device, and the output is the packaged, integrated data sent to the server.

[0367] Step 5:

[0368] The server uses the received data to generate business guidelines through a generative AI model. The input consists of text data, image data, and sentiment data collected on the server, which are integrated to create customized guidelines. The output is business guidelines that reflect the user's current emotional state. Based on the prompt text, the generative AI model suggests appropriate guidance content.

[0369] Step 6:

[0370] The server saves the generated business guidelines to the cloud. The input is the generated business guidelines, which are recorded in the database for future reference using a storage method. Specifically, this involves writing to the data storage system. The output is the business guidelines stored in the cloud.

[0371] Step 7:

[0372] The next time a work instruction is given, the server will send the most suitable work task to the terminal based on the stored work guidelines. The input consists of past work guidelines and sentiment data stored in the cloud, and new work guidelines are generated and sent based on this. The output is instructions for performing the work adapted to the user. The script is adjusted according to the user's emotional state.

[0373] (Application Example 2)

[0374] 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."

[0375] Conventional business support systems automate basic instructions without considering user emotions, resulting in instructions that don't match the user's emotional state and fail to be fully effective. Furthermore, it was difficult for users to properly edit instructional materials. For home robots to provide users with comfortable and personalized services, an approach that considers user emotions is necessary. Therefore, there is a need for a system that automatically provides appropriate instruction tailored to the user's emotions, while also allowing users to easily edit instructional materials themselves.

[0376] 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.

[0377] In this invention, the server includes an information input device for receiving work instructions, a language recognition means for converting received voice information into text information, a video processing means for analyzing operation content from received screen information, an emotion recognition means for analyzing the user's emotional state and adjusting the content of work instruction materials accordingly, a recording means for saving the generated work instruction materials and referring to them when performing work in the future, a management and control means for automatically performing work based on the saved work instruction materials, and a modification means for allowing the user to adjust the generated work instruction materials. This enables work instruction that reflects the user's emotions, allows the user to easily edit the work instruction materials, and makes the services provided by the home robot even more user-friendly and effective.

[0378] An "information input device" is a device used to receive work instructions and emotional states from users.

[0379] "Language recognition means" refers to technology that has the function of analyzing received audio information and converting it into textual information.

[0380] "Image processing means" refers to technology used to analyze the operation content based on the received screen information.

[0381] "Emotion recognition means" refers to technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state.

[0382] "Generation means" refers to technology for creating work instruction materials using text information and screen information.

[0383] "Recording means" refers to a function for saving generated work instruction materials so that they can be referenced later.

[0384] "Management and control means" refers to a function that automatically performs tasks based on stored work instruction materials.

[0385] "Modification means" refers to a function that allows users to adjust and edit the generated work instruction materials.

[0386] Embodiments of this invention relate to a user assistance system using a home robot. The system integrates an information input device, a language recognition function, an image processing function, an emotion recognition function, a generation function, a recording function, a management and control function, and a modification function. The user inputs work instructions by voice through the home robot, and the information input device captures the voice and screen information.

[0387] This information is converted into text using language recognition means, and then image processing means analyze the screen information and extract the operation content. Subsequently, emotion recognition means analyze the user's facial expressions and tone of voice to identify their emotional state. This series of data is then transmitted to the cloud by a server.

[0388] The server uses a generation function to create work instruction materials that combine text information, operation details, and emotional states. These materials are saved using a recording function and automatically used for subsequent work executions by a management control function. Users can edit the generated materials as needed using a modification function.

[0389] As a concrete example, when a home robot cleans, it can provide a message tailored to the user's emotions while they rest, such as, "Thank you for your hard work. I'll finish cleaning while you rest, so please relax." In this way, the system can provide optimal support that responds to the user's emotions.

[0390] An example of a prompt message is as follows:

[0391] "Design the emotional responses for a robot that helps with household chores. Think of suggestions for when the user is relaxed and support options for when they are busy."

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The terminal receives voice commands from the user. The input is the user's voice data. The terminal acquires this voice data through the microphone and temporarily stores it in its memory.

[0395] Step 2:

[0396] The terminal converts received audio data into text information using language recognition means. The input is audio data, and the output is corresponding text data. Speech recognition software is used to analyze the audio signal and convert it into text format.

[0397] Step 3:

[0398] The terminal captures user screen operations and analyzes them using video processing equipment. The input is screen data, and the output is data indicating the operations performed. An image analysis algorithm is used to identify changes in the screen and extract the operations performed.

[0399] Step 4:

[0400] The device uses a camera to record the user's facial expressions and analyzes them using emotion recognition technology. The input is video data of the user, and the output is data indicating their emotional state. It analyzes facial features and infers emotions from facial expressions.

[0401] Step 5:

[0402] The server receives text data, operation data, and emotion data sent from the terminal. This data is then integrated and stored in the cloud.

[0403] Step 6:

[0404] The server uses a generation function to combine received data and generate work instruction materials. Inputs include text data, operation data, and sentiment data, while output is work instruction materials. The data is integrated and formatted into a set of instructions.

[0405] Step 7:

[0406] The server records the generated work instruction materials and uses its management and control functions to automatically utilize them during the next work execution. These materials will be referenced when giving the next instructions.

[0407] Step 8:

[0408] Users can edit and adjust work instruction materials as needed through the editing function. Users can also review and modify the materials using the interface.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] [Third Embodiment]

[0413] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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".

[0425] The system of the present invention begins with a user communicating work instructions to an AI via a terminal device using voice and screen operations. When the user explains the work procedure using the terminal device, the terminal simultaneously records the operations and voice. The voice data is transmitted from the terminal to a server, where it is converted into text data using speech recognition means. At the same time, the captured video of the screen recorded by the terminal is analyzed by the server's image processing means.

[0426] After analysis, the server combines this data using a generation mechanism and automatically generates a visually appealing business manual. This manual can also be manually edited by the user. The generated business manual is recorded in a cloud-based storage system and referenced when performing future tasks.

[0427] In the future, when a user requests a similar task, the server uses its control mechanisms, based on stored task manuals, to send instructions to the terminal for the AI ​​to automatically perform the task. This includes business processes such as screen input, form completion, and data submission. For example, if a user explains the expense claim procedure, the system will automate the relevant data entry and allow the user to submit the necessary documents online in the future.

[0428] Thus, this invention significantly reduces the time and effort required for handing over tasks, enabling the continuous operation of business. It is a system that contributes to increased efficiency and automation of operations, particularly in fields where labor shortages are a major problem.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] The user operates the terminal device, giving voice instructions for tasks and performing screen operations. The terminal records the voice and starts capturing the screen.

[0432] Step 2:

[0433] After the user completes the explanation of the work procedure, the terminal sends the recorded audio data and captured screen video data to the server.

[0434] Step 3:

[0435] The server processes the received audio data through a speech recognition system, converting the audio data into text data. Appropriate punctuation is then inserted into the converted text, and it is formatted into an easily understandable format.

[0436] Step 4:

[0437] The server processes video data frame by frame and uses image processing technology to analyze user actions. It identifies key actions and input points and extracts related image data.

[0438] Step 5:

[0439] The server uses a generation mechanism to integrate text converted from speech with image data obtained through analysis, creating a visually easy-to-understand operational manual. This manual details procedures, precautions, and necessary tasks.

[0440] Step 6:

[0441] The generated business manuals are saved to a storage device on the server and made available for future reference via cloud storage.

[0442] Step 7:

[0443] When a user requests automation of a task for future tasks, the server, based on the stored task manual, uses control mechanisms to deliver instructions to the terminal for the AI ​​to execute the task. The terminal then performs the task according to the automation script.

[0444] (Example 1)

[0445] 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."

[0446] In today's world, where efficiency and automation are paramount, effectively recording work instructions, sharing information, and utilizing it for future tasks is a challenge. In particular, methods that simultaneously record voice instructions and screen operations, and visually present work procedures, are crucial.

[0447] 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.

[0448] In this invention, the server includes means for receiving work instructions using an information processing device, recognition means for converting voice information into text information, recognition means for analyzing operation content from screen information, and creation means for creating work instruction manuals using the text information and screen information. This enables integrated recording of work instructions and automation of operations.

[0449] An "information processing device" is a mechanical or electronic device used to receive work instructions and has the function of acquiring voice information and screen information.

[0450] "Recognition means" refers to technical methods or devices for converting audio information into text information or for analyzing operation content from screen information.

[0451] "Creation means" refers to a function or device for creating a work instruction manual using the aforementioned text information and screen information.

[0452] "Recording means" refers to a technical method or device for saving the created work instruction manual on an information recording medium and referring to it when performing the work in the future.

[0453] "Transmission means" refers to a method or device for transmitting audio information and screen information together to a computer.

[0454] "Management means" refers to a control function or device for automatically executing tasks based on stored work instruction manuals.

[0455] "Editing means" refers to functions or devices that allow users to manually modify or change the created work instruction manuals.

[0456] The embodiments for carrying out the present invention are shown below.

[0457] This system achieves business automation by combining three elements: users, terminals, and servers.

[0458] Users input work instructions via voice through a terminal device, and also perform screen operations during this process. The terminal is equipped with input devices, recording devices, and display devices necessary to simultaneously record this voice data and screen operations.

[0459] Next, the terminal sends the recorded audio data to the server. The server converts the audio data into text data using speech recognition software. Widely used speech recognition software is employed for this process. In parallel, the screen capture data sent from the terminal is analyzed on the server side using an image processing library. This analysis specifically identifies the steps the user took on the screen.

[0460] Subsequently, the server uses a generative AI model to integrate these voice-text and image data to create a work instruction manual. This manual is presented to the user in a visually easy-to-understand format and saved to cloud storage for later reference. This generation process allows users to efficiently document work procedures. An example of a prompt message might be, "Starting the expense claim process."

[0461] In the future, when a user performs the same task, the server will use the stored task instructions to automate the entire process. This allows the user to complete the task efficiently without having to perform cumbersome operations again.

[0462] In this way, the present invention enables the achievement of increased efficiency and automation in business operations, and is particularly effective in fields where labor shortages are a problem.

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] The user inputs work instructions by voice and simultaneously performs related screen operations on the terminal. The input voice data is captured by the terminal's microphone, and the screen operations are recorded as screen captures. The input for this step consists of voice instructions and on-screen actions, and the output consists of audio and video files. Each file is temporarily stored in the terminal's storage.

[0466] Step 2:

[0467] The device sends the acquired audio file to the server, which processes the audio data into text data. On the server side, this audio file is analyzed using speech recognition technology and the corresponding text data is output. When using Google Cloud Speech-to-Text, the audio is appropriately converted to text by the speech language model, and this converted text is recorded on the server.

[0468] Step 3:

[0469] Similarly, captured video files sent from the terminal are analyzed on the server using image processing technology to determine the operation procedures. The video file is taken as input, and the on-screen operations are identified frame by frame. For example, buttons clicked by the user and the contents of forms entered are identified, and this information is output in text format. An image recognition library is used for this process.

[0470] Step 4:

[0471] The server uses a generative AI model to create work instruction manuals by combining text converted from speech with operation details analyzed through image processing. Inputs are speech text and operation text, and the output is an instruction manual containing visualized work procedures. This generative AI model utilizes Text-to-Image and Text-to-Text technologies to organize information in a way that users can intuitively understand.

[0472] Step 5:

[0473] The generated work instructions are saved to cloud storage by the server and prepared for future reference and automation. The server uploads the generated files to the cloud and manages the link to the storage location. The output of this step is an online access link to the work instructions, making them accessible to users at any time.

[0474] Step 6:

[0475] The next time a user issues the same work instruction, the server will send an automated task execution control command to the terminal based on the saved work instruction document. Based on this information, the server will generate an automated script and instruct the terminal to execute it. The output is the automated execution of the process requested by the user, resulting in efficient work processing that does not require manual intervention.

[0476] (Application Example 1)

[0477] 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."

[0478] In manufacturing environments, there is a need to streamline the handover and setup of work procedures and the operation of maintenance tasks. However, manual handover and task execution are time-consuming and labor-intensive. Furthermore, since these processes depend on the skills of the workers, there is a concern that variations may occur depending on the task. To solve this problem, there is a need for a system that enables effective recording and automation of work procedures.

[0479] 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.

[0480] In this invention, the server includes an information processing device for receiving work instructions, a speech recognition means for converting received voice information into text information, and an image analysis means for analyzing the procedure content from received operation information. This enables efficient recording of work procedures and automation in the manufacturing process.

[0481] An "information processing device" is a device that receives voice information and operation information and transmits work instructions.

[0482] "Speech recognition means" refers to a technology that has the function of analyzing received speech information and converting it into text information.

[0483] "Image analysis means" refers to a method for analyzing the procedure content from operation information and extracting necessary information.

[0484] "Generation means" refers to a process that automatically creates work procedure manuals using audio information and procedural information.

[0485] A "recording mechanism" is a system for saving generated work procedure manuals and managing them so that they can be easily referenced later.

[0486] "Control means" refers to methods or devices for automatically executing tasks based on recorded work procedures.

[0487] "Manufacturing process control means" refers to technology that applies operational procedures to setting up and maintaining a manufacturing line and has the function of automating these tasks.

[0488] "Communication means" refers to technology for packaging voice information and procedural information and transmitting it to an information processing device.

[0489] "Revision means" refers to methods or functions that enable users to edit and modify the generated work procedure manuals.

[0490] The system that realizes this application example aims to automate work procedures in manufacturing sites. The system includes an information processing device that receives various types of information, a speech recognition means that converts voice information into text information, and an image analysis means that analyzes the procedure content from operation information.

[0491] First, the terminal receives work instructions from the factory. The user uses an information processing device such as a smartphone or tablet to operate the screen while explaining the manufacturing line setup and maintenance procedures by voice. This voice data and operation information are recorded in the terminal and converted into text data by a voice recognition system.

[0492] Following this, the server analyzes the voice data and operation information in the cloud. It processes the data using a speech recognition API (e.g., Google Speech-to-Text) and an image processing library (e.g., OpenCV). From the analyzed information, the generation system automatically creates a work procedure manual.

[0493] This work procedure manual is recorded and referenced for future work execution. The recording means uses a cloud storage service to store the work procedure manual. The control means allows for the automatic control of the manufacturing process based on the recorded work procedure manual.

[0494] As a concrete example, when workers on a factory painting line set parameters such as paint type, application speed, and drying time, this system allows for efficient recording of the work procedure, enabling automatic settings for subsequent uses.

[0495] As an example of a prompt message to the generating AI model, you could give the instruction, "Analyze this audio data and video of screen operations, and generate a procedural manual for future automated setup of the painting line."

[0496] In this way, we provide a system that improves work efficiency in manufacturing sites, compensates for labor shortages, and enables tasks to be performed with high precision.

[0497] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0498] Step 1:

[0499] The user uses an information processing device to perform screen operations while explaining work instructions verbally. Specifically, the user speaks into the microphone of their smartphone and simultaneously operates the touchscreen. The input consists of voice data and operation information, which are recorded on the device.

[0500] Step 2:

[0501] The device sends the recorded audio data to a speech recognition API, which converts it into text data. Specifically, the device calls a speech recognition service in the cloud (e.g., Google Speech-to-Text) and uploads the audio file. The output is text data. This processing makes the audio information into a format that can be analyzed.

[0502] Step 3:

[0503] The server analyzes the recorded operation information using image analysis tools. Specifically, the server uses an image processing library (e.g., OpenCV) to analyze what operations were performed from the captured screen operations. The input is operation information, and the output is data detailing the procedure. This digitizes the flow of operations.

[0504] Step 4:

[0505] The server integrates the analyzed text data and procedure data, and automatically generates a business procedure manual using a generation method. Specifically, the server utilizes a generation AI model to combine both sets of data into a format instructed by prompts. The generated business procedure manual is then output. This creates a visualized procedure manual.

[0506] Step 5:

[0507] The server saves the generated work procedure manuals to cloud storage using a recording mechanism. Specifically, the server uploads and saves the data to the storage service via an API. The input is the work procedure manual, and the output is a file stored in the cloud. The manual is retained for future reference.

[0508] Step 6:

[0509] In subsequent instances, when a user performs a similar task, the server will refer to the recorded work procedure manual and automatically execute the task using control mechanisms. For example, the equipment settings on a manufacturing line will be automatically configured. The input is the saved work procedure manual, and the output is the configured manufacturing equipment. This enables efficient and consistent task execution.

[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] The system of the present invention not only records the process of a user providing work instructions to an AI via a terminal device using voice and screen operations, but also has the function of simultaneously recognizing the user's emotional state. When a user explains a task on the terminal, the terminal captures the voice and screen, and in addition, uses emotion recognition means to analyze the user's facial expressions and tone of voice to acquire emotional data.

[0512] The captured audio data is sent to the server and converted into text data by the server's speech recognition system. Simultaneously, screen data is analyzed by the server's image processing system, and image data related to user actions is extracted. Furthermore, the emotional data analyzed by the emotion engine is considered when generating the operational manual.

[0513] The server uses a generation mechanism to combine text converted from speech, analyzed image data, and emotion data to generate a work manual in a format appropriate to the user's current emotional state. For example, if the user is confused, the explanation can be made more detailed, and additional support information can be included in the manual. The generated work manual and emotion data are stored in the cloud and used for future work execution and as internal user feedback.

[0514] The next time a user requests a similar task, the server will send optimized tasks to the terminal based on stored manuals and emotional data, and the AI ​​will automatically perform the tasks. For example, if a user feels anxious about a particular procedure, the system can automatically generate a more reassuring script based on past emotional data and use it when performing the task.

[0515] Thus, by considering user emotions, this invention not only improves operational efficiency but also provides a user-friendly environment. It appropriately utilizes emotional information to improve the continuity and accuracy of operations.

[0516] The following describes the processing flow.

[0517] Step 1:

[0518] The user uses a terminal device to explain work instructions through voice and screen operations. During this process, the terminal records the user's voice and captures the screen. Furthermore, the terminal uses its built-in camera and microphone to analyze the user's facial expressions and voice tone, and emotional data is acquired using emotion recognition technology.

[0519] Step 2:

[0520] The device packages the recorded audio data, screen capture data, and analyzed emotion data, and sends them to the server.

[0521] Step 3:

[0522] The server converts the received audio data into text data using speech recognition technology. Appropriate punctuation is inserted into the text data, and it is organized into work instructions.

[0523] Step 4:

[0524] The server analyzes the received screen capture data frame by frame, uses image processing to analyze the user's actions, and extracts image data of important operation steps.

[0525] Step 5:

[0526] Based on the results of emotion recognition, the server adjusts the content of the work manual according to the user's psychological state indicated by the analyzed emotion data. For example, if the user is feeling anxious, the manual will be adjusted to include more detailed procedural explanations and additional supplementary information.

[0527] Step 6:

[0528] The server uses generation tools to combine analyzed text data, image data, and sentiment data to generate a business manual. This manual will reflect the user's psychological state.

[0529] Step 7:

[0530] The generated work manuals and emotional data are recorded on a server storage system and managed on the cloud. This information will be available for reference when performing tasks in the future.

[0531] Step 8:

[0532] When a user instructs the next task to be performed, the server delivers an optimized work process to the terminal based on stored work manuals and sentiment data. The terminal then executes an AI-powered automated script to perform the task. During this process, the user experience is optimized based on past sentiment data.

[0533] (Example 2)

[0534] 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."

[0535] Conventional work instruction systems generated work guidelines without considering the user's emotional state, thus failing to adequately improve user understanding and confidence. Furthermore, because tasks were automated based solely on past operation data, optimization that considered the user's emotional aspects was not achieved. This resulted in shortcomings in user experience and operational efficiency.

[0536] 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.

[0537] In this invention, the server includes speech recognition means for converting speech information into text information, image processing means for analyzing operation content from image information, and emotion recognition means for acquiring emotion information from facial expressions and tone of voice. This enables the generation of work guidelines that take into account the user's emotional state and optimization based on past emotion information.

[0538] An "information processing device" is a device used by users to input work instructions and is equipped with the function of processing and transmitting data such as voice and images.

[0539] "Speech recognition means" refers to a technical means that has the function of converting received speech information into text information.

[0540] "Image processing means" refers to a technical means that analyzes the operation content from received image information and acquires it as data.

[0541] An "emotion recognition tool" is a tool that uses a user's facial expressions and tone of voice to identify their emotions and acquire that information.

[0542] "Generation means" refers to means equipped with the function of generating appropriate business guidelines using text information, image information, and emotional information.

[0543] "Storage methods" refer to technical means that enable the creation of business guidelines to be stored in a database or cloud, and referenced in the future.

[0544] A "control means" is a technical means that has the function of automatically performing tasks based on stored work guidelines.

[0545] "Transmission means" refers to means equipped with the function of packaging audio and image information and transmitting it to a network-connected device.

[0546] An "editing tool" is a means that allows users to modify or change the generated business guidelines.

[0547] The system of this invention is designed to support and streamline the process by which users input work instructions. This system generates personalized work guidelines tailored to the user through the acquisition and analysis of voice data, image data, and emotion data.

[0548] First, the user inputs work instructions by voice using a terminal. The terminal captures this voice and converts it into text information using speech recognition technology. In this process, for example, a commonly used speech recognition API is used as the speech recognition software.

[0549] Next, the user's screen operations are also recorded by the device. Screen capture software is used to acquire image data. This ensures a detailed record of the user's specific steps.

[0550] Furthermore, the device uses emotion recognition to analyze the user's facial expressions and tone of voice to acquire emotional data. This process utilizes an emotion analysis engine, which can determine the user's emotional state from changes in facial expressions and tone of voice.

[0551] All of this data is sent to a server, which uses a generation mechanism to generate business guidelines that encompass this data. Using a generation AI model, voice, image, and emotion data are integrated to create customized business guidelines that respond to the user's emotional state.

[0552] For example, if a user feels anxious while learning the procedures for migrating to a new system, the system can take that emotional data into account and generate operational guidelines that include detailed explanations and supplementary information. It can also provide less stressful operating procedures that reduce user anxiety by comparing them with past records.

[0553] An example of a prompt message might be, "Please create instructions to alleviate any anxiety you may feel about the installation process for new software." This allows for flexible adaptation of work guidelines to the specific situation.

[0554] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0555] Step 1:

[0556] The user inputs work instructions by voice using a terminal. The terminal captures the input voice and saves it as audio data. The input is the user's voice, which is converted into text information using speech recognition software. Specifically, the terminal's microphone is used to collect voice in real time and send it to a recognition API. The output is text data.

[0557] Step 2:

[0558] The terminal records the screen operations performed by the user while they are receiving work instructions. The terminal uses a screen capture function to acquire the entire sequence of operations as image data. The input is the user's screen operation actions, which are captured as snapshots and analyzed by an image processing algorithm. The output is image data that provides a detailed record of the user's operation steps.

[0559] Step 3:

[0560] The device uses emotion recognition to analyze the user's facial expressions and voice tone. This is done using data collected through the camera and microphone. The input consists of the user's facial expression data and voice tone, which are identified as emotional states by the emotion analysis engine. Specifically, it analyzes changes in facial expressions and voice tone to generate emotion data. The output is data indicating the user's emotional state.

[0561] Step 4:

[0562] The device sends collected audio, image, and emotion data to the server. This transmission process, which includes data packaging, is securely transferred to the server over the network. The input is all data collected by the device, and the output is the packaged, integrated data sent to the server.

[0563] Step 5:

[0564] The server uses the received data to generate business guidelines through a generative AI model. The input consists of text data, image data, and sentiment data collected on the server, which are integrated to create customized guidelines. The output is business guidelines that reflect the user's current emotional state. Based on the prompt text, the generative AI model suggests appropriate guidance content.

[0565] Step 6:

[0566] The server saves the generated business guidelines to the cloud. The input is the generated business guidelines, which are recorded in the database for future reference using a storage method. Specifically, this involves writing to the data storage system. The output is the business guidelines stored in the cloud.

[0567] Step 7:

[0568] The next time a work instruction is given, the server will send the most suitable work task to the terminal based on the stored work guidelines. The input consists of past work guidelines and sentiment data stored in the cloud, and new work guidelines are generated and sent based on this. The output is instructions for performing the work adapted to the user. The script is adjusted according to the user's emotional state.

[0569] (Application Example 2)

[0570] 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."

[0571] Conventional business support systems automate basic instructions without considering user emotions, resulting in instructions that don't match the user's emotional state and fail to be fully effective. Furthermore, it was difficult for users to properly edit instructional materials. For home robots to provide users with comfortable and personalized services, an approach that considers user emotions is necessary. Therefore, there is a need for a system that automatically provides appropriate instruction tailored to the user's emotions, while also allowing users to easily edit instructional materials themselves.

[0572] 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.

[0573] In this invention, the server includes an information input device for receiving work instructions, a language recognition means for converting received voice information into text information, a video processing means for analyzing operation content from received screen information, an emotion recognition means for analyzing the user's emotional state and adjusting the content of work instruction materials accordingly, a recording means for saving the generated work instruction materials and referring to them when performing work in the future, a management and control means for automatically performing work based on the saved work instruction materials, and a modification means for allowing the user to adjust the generated work instruction materials. This enables work instruction that reflects the user's emotions, allows the user to easily edit the work instruction materials, and makes the services provided by the home robot even more user-friendly and effective.

[0574] An "information input device" is a device used to receive work instructions and emotional states from users.

[0575] "Language recognition means" refers to technology that has the function of analyzing received audio information and converting it into textual information.

[0576] "Image processing means" refers to technology used to analyze the operation content based on the received screen information.

[0577] "Emotion recognition means" refers to technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state.

[0578] "Generation means" refers to technology for creating work instruction materials using text information and screen information.

[0579] "Recording means" refers to a function for saving generated work instruction materials so that they can be referenced later.

[0580] "Management and control means" refers to a function that automatically performs tasks based on stored work instruction materials.

[0581] "Modification means" refers to a function that allows users to adjust and edit the generated work instruction materials.

[0582] Embodiments of this invention relate to a user assistance system using a home robot. The system integrates an information input device, a language recognition function, an image processing function, an emotion recognition function, a generation function, a recording function, a management and control function, and a modification function. The user inputs work instructions by voice through the home robot, and the information input device captures the voice and screen information.

[0583] This information is converted into text using language recognition means, and then image processing means analyze the screen information and extract the operation content. Subsequently, emotion recognition means analyze the user's facial expressions and tone of voice to identify their emotional state. This series of data is then transmitted to the cloud by a server.

[0584] The server uses a generation function to create work instruction materials that combine text information, operation details, and emotional states. These materials are saved using a recording function and automatically used for subsequent work executions by a management control function. Users can edit the generated materials as needed using a modification function.

[0585] As a concrete example, when a home robot cleans, it can provide a message tailored to the user's emotions while they rest, such as, "Thank you for your hard work. I'll finish cleaning while you rest, so please relax." In this way, the system can provide optimal support that responds to the user's emotions.

[0586] An example of a prompt message is as follows:

[0587] "Design the emotional responses for a robot that helps with household chores. Think of suggestions for when the user is relaxed and support options for when they are busy."

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

[0589] Step 1:

[0590] The terminal receives voice commands from the user. The input is the user's voice data. The terminal acquires this voice data through the microphone and temporarily stores it in its memory.

[0591] Step 2:

[0592] The terminal converts received audio data into text information using language recognition means. The input is audio data, and the output is corresponding text data. Speech recognition software is used to analyze the audio signal and convert it into text format.

[0593] Step 3:

[0594] The terminal captures user screen operations and analyzes them using video processing equipment. The input is screen data, and the output is data indicating the operations performed. An image analysis algorithm is used to identify changes in the screen and extract the operations performed.

[0595] Step 4:

[0596] The device uses a camera to record the user's facial expressions and analyzes them using emotion recognition technology. The input is video data of the user, and the output is data indicating their emotional state. It analyzes facial features and infers emotions from facial expressions.

[0597] Step 5:

[0598] The server receives text data, operation data, and emotion data sent from the terminal. This data is then integrated and stored in the cloud.

[0599] Step 6:

[0600] The server uses a generation function to combine received data and generate work instruction materials. Inputs include text data, operation data, and sentiment data, while output is work instruction materials. The data is integrated and formatted into a set of instructions.

[0601] Step 7:

[0602] The server records the generated work instruction materials and uses its management and control functions to automatically utilize them during the next work execution. These materials will be referenced when giving the next instructions.

[0603] Step 8:

[0604] Users can edit and adjust work instruction materials as needed through the editing function. Users can also review and modify the materials using the interface.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] [Fourth Embodiment]

[0609] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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".

[0622] The system of the present invention begins with a user communicating work instructions to an AI via a terminal device using voice and screen operations. When the user explains the work procedure using the terminal device, the terminal simultaneously records the operations and voice. The voice data is transmitted from the terminal to a server, where it is converted into text data using speech recognition means. At the same time, the captured video of the screen recorded by the terminal is analyzed by the server's image processing means.

[0623] After analysis, the server combines this data using a generation mechanism and automatically generates a visually appealing business manual. This manual can also be manually edited by the user. The generated business manual is recorded in a cloud-based storage system and referenced when performing future tasks.

[0624] In the future, when a user requests a similar task, the server uses its control mechanisms, based on stored task manuals, to send instructions to the terminal for the AI ​​to automatically perform the task. This includes business processes such as screen input, form completion, and data submission. For example, if a user explains the expense claim procedure, the system will automate the relevant data entry and allow the user to submit the necessary documents online in the future.

[0625] Thus, this invention significantly reduces the time and effort required for handing over tasks, enabling the continuous operation of business. It is a system that contributes to increased efficiency and automation of operations, particularly in fields where labor shortages are a major problem.

[0626] The following describes the processing flow.

[0627] Step 1:

[0628] The user operates the terminal device, giving voice instructions for tasks and performing screen operations. The terminal records the voice and starts capturing the screen.

[0629] Step 2:

[0630] After the user completes the explanation of the work procedure, the terminal sends the recorded audio data and captured screen video data to the server.

[0631] Step 3:

[0632] The server processes the received audio data through a speech recognition system, converting the audio data into text data. Appropriate punctuation is then inserted into the converted text, and it is formatted into an easily understandable format.

[0633] Step 4:

[0634] The server processes video data frame by frame and uses image processing technology to analyze user actions. It identifies key actions and input points and extracts related image data.

[0635] Step 5:

[0636] The server uses a generation mechanism to integrate text converted from speech with image data obtained through analysis, creating a visually easy-to-understand operational manual. This manual details procedures, precautions, and necessary tasks.

[0637] Step 6:

[0638] The generated business manuals are saved to a storage device on the server and made available for future reference via cloud storage.

[0639] Step 7:

[0640] When a user requests automation of a task for future tasks, the server, based on the stored task manual, uses control mechanisms to deliver instructions to the terminal for the AI ​​to execute the task. The terminal then performs the task according to the automation script.

[0641] (Example 1)

[0642] 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".

[0643] In today's world, where efficiency and automation are paramount, effectively recording work instructions, sharing information, and utilizing it for future tasks is a challenge. In particular, methods that simultaneously record voice instructions and screen operations, and visually present work procedures, are crucial.

[0644] 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.

[0645] In this invention, the server includes means for receiving work instructions using an information processing device, recognition means for converting voice information into text information, recognition means for analyzing operation content from screen information, and creation means for creating work instruction manuals using the text information and screen information. This enables integrated recording of work instructions and automation of operations.

[0646] An "information processing device" is a mechanical or electronic device used to receive work instructions and has the function of acquiring voice information and screen information.

[0647] "Recognition means" refers to technical methods or devices for converting audio information into text information or for analyzing operation content from screen information.

[0648] "Creation means" refers to a function or device for creating a work instruction manual using the aforementioned text information and screen information.

[0649] "Recording means" refers to a technical method or device for saving the created work instruction manual on an information recording medium and referring to it when performing the work in the future.

[0650] "Transmission means" refers to a method or device for transmitting audio information and screen information together to a computer.

[0651] "Management means" refers to a control function or device for automatically executing tasks based on stored work instruction manuals.

[0652] "Editing means" refers to functions or devices that allow users to manually modify or change the created work instruction manuals.

[0653] The embodiments for carrying out the present invention are shown below.

[0654] This system achieves business automation by combining three elements: users, terminals, and servers.

[0655] Users input work instructions via voice through a terminal device, and also perform screen operations during this process. The terminal is equipped with input devices, recording devices, and display devices necessary to simultaneously record this voice data and screen operations.

[0656] Next, the terminal sends the recorded audio data to the server. The server converts the audio data into text data using speech recognition software. Widely used speech recognition software is employed for this process. In parallel, the screen capture data sent from the terminal is analyzed on the server side using an image processing library. This analysis specifically identifies the steps the user took on the screen.

[0657] Subsequently, the server uses a generative AI model to integrate these voice-text and image data to create a work instruction manual. This manual is presented to the user in a visually easy-to-understand format and saved to cloud storage for later reference. This generation process allows users to efficiently document work procedures. An example of a prompt message might be, "Starting the expense claim process."

[0658] In the future, when a user performs the same task, the server will use the stored task instructions to automate the entire process. This allows the user to complete the task efficiently without having to perform cumbersome operations again.

[0659] In this way, the present invention enables the achievement of increased efficiency and automation in business operations, and is particularly effective in fields where labor shortages are a problem.

[0660] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0661] Step 1:

[0662] The user inputs work instructions by voice and simultaneously performs related screen operations on the terminal. The input voice data is captured by the terminal's microphone, and the screen operations are recorded as screen captures. The input for this step consists of voice instructions and on-screen actions, and the output consists of audio and video files. Each file is temporarily stored in the terminal's storage.

[0663] Step 2:

[0664] The device sends the acquired audio file to the server, which processes the audio data into text data. On the server side, this audio file is analyzed using speech recognition technology and the corresponding text data is output. When using Google Cloud Speech-to-Text, the audio is appropriately converted to text by the speech language model, and this converted text is recorded on the server.

[0665] Step 3:

[0666] Similarly, captured video files sent from the terminal are analyzed on the server using image processing technology to determine the operation procedures. The video file is taken as input, and the on-screen operations are identified frame by frame. For example, buttons clicked by the user and the contents of forms entered are identified, and this information is output in text format. An image recognition library is used for this process.

[0667] Step 4:

[0668] The server uses a generative AI model to create work instruction manuals by combining text converted from speech with operation details analyzed through image processing. Inputs are speech text and operation text, and the output is an instruction manual containing visualized work procedures. This generative AI model utilizes Text-to-Image and Text-to-Text technologies to organize information in a way that users can intuitively understand.

[0669] Step 5:

[0670] The generated work instructions are saved to cloud storage by the server and prepared for future reference and automation. The server uploads the generated files to the cloud and manages the link to the storage location. The output of this step is an online access link to the work instructions, making them accessible to users at any time.

[0671] Step 6:

[0672] The next time a user issues the same work instruction, the server will send an automated task execution control command to the terminal based on the saved work instruction document. Based on this information, the server will generate an automated script and instruct the terminal to execute it. The output is the automated execution of the process requested by the user, resulting in efficient work processing that does not require manual intervention.

[0673] (Application Example 1)

[0674] 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".

[0675] In manufacturing environments, there is a need to streamline the handover and setup of work procedures and the operation of maintenance tasks. However, manual handover and task execution are time-consuming and labor-intensive. Furthermore, since these processes depend on the skills of the workers, there is a concern that variations may occur depending on the task. To solve this problem, there is a need for a system that enables effective recording and automation of work procedures.

[0676] 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.

[0677] In this invention, the server includes an information processing device for receiving work instructions, a speech recognition means for converting received voice information into text information, and an image analysis means for analyzing the procedure content from received operation information. This enables efficient recording of work procedures and automation in the manufacturing process.

[0678] An "information processing device" is a device that receives voice information and operation information and transmits work instructions.

[0679] "Speech recognition means" refers to a technology that has the function of analyzing received speech information and converting it into text information.

[0680] "Image analysis means" refers to a method for analyzing the procedure content from operation information and extracting necessary information.

[0681] "Generation means" refers to a process that automatically creates work procedure manuals using audio information and procedural information.

[0682] A "recording mechanism" is a system for saving generated work procedure manuals and managing them so that they can be easily referenced later.

[0683] "Control means" refers to methods or devices for automatically executing tasks based on recorded work procedures.

[0684] "Manufacturing process control means" refers to technology that applies operational procedures to setting up and maintaining a manufacturing line and has the function of automating these tasks.

[0685] "Communication means" refers to technology for packaging voice information and procedural information and transmitting it to an information processing device.

[0686] "Revision means" refers to methods or functions that enable users to edit and modify the generated work procedure manuals.

[0687] The system that realizes this application example aims to automate work procedures in manufacturing sites. The system includes an information processing device that receives various types of information, a speech recognition means that converts voice information into text information, and an image analysis means that analyzes the procedure content from operation information.

[0688] First, the terminal receives work instructions from the factory. The user uses an information processing device such as a smartphone or tablet to operate the screen while explaining the manufacturing line setup and maintenance procedures by voice. This voice data and operation information are recorded in the terminal and converted into text data by a voice recognition system.

[0689] Following this, the server analyzes the voice data and operation information in the cloud. It processes the data using a speech recognition API (e.g., Google Speech-to-Text) and an image processing library (e.g., OpenCV). From the analyzed information, the generation system automatically creates a work procedure manual.

[0690] This work procedure manual is recorded and referenced for future work execution. The recording means uses a cloud storage service to store the work procedure manual. The control means allows for the automatic control of the manufacturing process based on the recorded work procedure manual.

[0691] As a concrete example, when workers on a factory painting line set parameters such as paint type, application speed, and drying time, this system allows for efficient recording of the work procedure, enabling automatic settings for subsequent uses.

[0692] As an example of a prompt message to the generating AI model, you could give the instruction, "Analyze this audio data and video of screen operations, and generate a procedural manual for future automated setup of the painting line."

[0693] In this way, we provide a system that improves work efficiency in manufacturing sites, compensates for labor shortages, and enables tasks to be performed with high precision.

[0694] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0695] Step 1:

[0696] The user uses an information processing device to perform screen operations while explaining work instructions verbally. Specifically, the user speaks into the microphone of their smartphone and simultaneously operates the touchscreen. The input consists of voice data and operation information, which are recorded on the device.

[0697] Step 2:

[0698] The device sends the recorded audio data to a speech recognition API, which converts it into text data. Specifically, the device calls a speech recognition service in the cloud (e.g., Google Speech-to-Text) and uploads the audio file. The output is text data. This processing makes the audio information into a format that can be analyzed.

[0699] Step 3:

[0700] The server analyzes the recorded operation information using image analysis tools. Specifically, the server uses an image processing library (e.g., OpenCV) to analyze what operations were performed from the captured screen operations. The input is operation information, and the output is data detailing the procedure. This digitizes the flow of operations.

[0701] Step 4:

[0702] The server integrates the analyzed text data and procedure data, and automatically generates a business procedure manual using a generation method. Specifically, the server utilizes a generation AI model to combine both sets of data into a format instructed by prompts. The generated business procedure manual is then output. This creates a visualized procedure manual.

[0703] Step 5:

[0704] The server saves the generated work procedure manuals to cloud storage using a recording mechanism. Specifically, the server uploads and saves the data to the storage service via an API. The input is the work procedure manual, and the output is a file stored in the cloud. The manual is retained for future reference.

[0705] Step 6:

[0706] In subsequent instances, when a user performs a similar task, the server will refer to the recorded work procedure manual and automatically execute the task using control mechanisms. For example, the equipment settings on a manufacturing line will be automatically configured. The input is the saved work procedure manual, and the output is the configured manufacturing equipment. This enables efficient and consistent task execution.

[0707] 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.

[0708] The system of the present invention not only records the process of a user providing work instructions to an AI via a terminal device using voice and screen operations, but also has the function of simultaneously recognizing the user's emotional state. When a user explains a task on the terminal, the terminal captures the voice and screen, and in addition, uses emotion recognition means to analyze the user's facial expressions and tone of voice to acquire emotional data.

[0709] The captured audio data is sent to the server and converted into text data by the server's speech recognition system. Simultaneously, screen data is analyzed by the server's image processing system, and image data related to user actions is extracted. Furthermore, the emotional data analyzed by the emotion engine is considered when generating the operational manual.

[0710] The server uses a generation mechanism to combine text converted from speech, analyzed image data, and emotion data to generate a work manual in a format appropriate to the user's current emotional state. For example, if the user is confused, the explanation can be made more detailed, and additional support information can be included in the manual. The generated work manual and emotion data are stored in the cloud and used for future work execution and as internal user feedback.

[0711] The next time a user requests a similar task, the server will send optimized tasks to the terminal based on stored manuals and emotional data, and the AI ​​will automatically perform the tasks. For example, if a user feels anxious about a particular procedure, the system can automatically generate a more reassuring script based on past emotional data and use it when performing the task.

[0712] Thus, by considering user emotions, this invention not only improves operational efficiency but also provides a user-friendly environment. It appropriately utilizes emotional information to improve the continuity and accuracy of operations.

[0713] The following describes the processing flow.

[0714] Step 1:

[0715] The user uses a terminal device to explain work instructions through voice and screen operations. During this process, the terminal records the user's voice and captures the screen. Furthermore, the terminal uses its built-in camera and microphone to analyze the user's facial expressions and voice tone, and emotional data is acquired using emotion recognition technology.

[0716] Step 2:

[0717] The device packages the recorded audio data, screen capture data, and analyzed emotion data, and sends them to the server.

[0718] Step 3:

[0719] The server converts the received audio data into text data using speech recognition technology. Appropriate punctuation is inserted into the text data, and it is organized into work instructions.

[0720] Step 4:

[0721] The server analyzes the received screen capture data frame by frame, uses image processing to analyze the user's actions, and extracts image data of important operation steps.

[0722] Step 5:

[0723] Based on the results of emotion recognition, the server adjusts the content of the work manual according to the user's psychological state indicated by the analyzed emotion data. For example, if the user is feeling anxious, the manual will be adjusted to include more detailed procedural explanations and additional supplementary information.

[0724] Step 6:

[0725] The server uses generation tools to combine analyzed text data, image data, and sentiment data to generate a business manual. This manual will reflect the user's psychological state.

[0726] Step 7:

[0727] The generated work manuals and emotional data are recorded on a server storage system and managed on the cloud. This information will be available for reference when performing tasks in the future.

[0728] Step 8:

[0729] When a user instructs the next task to be performed, the server delivers an optimized work process to the terminal based on stored work manuals and sentiment data. The terminal then executes an AI-powered automated script to perform the task. During this process, the user experience is optimized based on past sentiment data.

[0730] (Example 2)

[0731] 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".

[0732] Conventional work instruction systems generated work guidelines without considering the user's emotional state, thus failing to adequately improve user understanding and confidence. Furthermore, because tasks were automated based solely on past operation data, optimization that considered the user's emotional aspects was not achieved. This resulted in shortcomings in user experience and operational efficiency.

[0733] 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.

[0734] In this invention, the server includes speech recognition means for converting speech information into text information, image processing means for analyzing operation content from image information, and emotion recognition means for acquiring emotion information from facial expressions and tone of voice. This enables the generation of work guidelines that take into account the user's emotional state and optimization based on past emotion information.

[0735] An "information processing device" is a device used by users to input work instructions and is equipped with the function of processing and transmitting data such as voice and images.

[0736] "Speech recognition means" refers to a technical means that has the function of converting received speech information into text information.

[0737] "Image processing means" refers to a technical means that analyzes the operation content from received image information and acquires it as data.

[0738] An "emotion recognition tool" is a tool that uses a user's facial expressions and tone of voice to identify their emotions and acquire that information.

[0739] "Generation means" refers to means equipped with the function of generating appropriate business guidelines using text information, image information, and emotional information.

[0740] "Storage methods" refer to technical means that enable the creation of business guidelines to be stored in a database or cloud, and referenced in the future.

[0741] A "control means" is a technical means that has the function of automatically performing tasks based on stored work guidelines.

[0742] "Transmission means" refers to means equipped with the function of packaging audio and image information and transmitting it to a network-connected device.

[0743] An "editing tool" is a means that allows users to modify or change the generated business guidelines.

[0744] The system of this invention is designed to support and streamline the process by which users input work instructions. This system generates personalized work guidelines tailored to the user through the acquisition and analysis of voice data, image data, and emotion data.

[0745] First, the user inputs work instructions by voice using a terminal. The terminal captures this voice and converts it into text information using speech recognition technology. In this process, for example, a commonly used speech recognition API is used as the speech recognition software.

[0746] Next, the user's screen operations are also recorded by the device. Screen capture software is used to acquire image data. This ensures a detailed record of the user's specific steps.

[0747] Furthermore, the device uses emotion recognition to analyze the user's facial expressions and tone of voice to acquire emotional data. This process utilizes an emotion analysis engine, which can determine the user's emotional state from changes in facial expressions and tone of voice.

[0748] All of this data is sent to a server, which uses a generation mechanism to generate business guidelines that encompass this data. Using a generation AI model, voice, image, and emotion data are integrated to create customized business guidelines that respond to the user's emotional state.

[0749] For example, if a user feels anxious while learning the procedures for migrating to a new system, the system can take that emotional data into account and generate operational guidelines that include detailed explanations and supplementary information. It can also provide less stressful operating procedures that reduce user anxiety by comparing them with past records.

[0750] An example of a prompt message might be, "Please create instructions to alleviate any anxiety you may feel about the installation process for new software." This allows for flexible adaptation of work guidelines to the specific situation.

[0751] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0752] Step 1:

[0753] The user inputs work instructions by voice using a terminal. The terminal captures the input voice and saves it as audio data. The input is the user's voice, which is converted into text information using speech recognition software. Specifically, the terminal's microphone is used to collect voice in real time and send it to a recognition API. The output is text data.

[0754] Step 2:

[0755] The terminal records the screen operations performed by the user while they are receiving work instructions. The terminal uses a screen capture function to acquire the entire sequence of operations as image data. The input is the user's screen operation actions, which are captured as snapshots and analyzed by an image processing algorithm. The output is image data that provides a detailed record of the user's operation steps.

[0756] Step 3:

[0757] The device uses emotion recognition to analyze the user's facial expressions and voice tone. This is done using data collected through the camera and microphone. The input consists of the user's facial expression data and voice tone, which are identified as emotional states by the emotion analysis engine. Specifically, it analyzes changes in facial expressions and voice tone to generate emotion data. The output is data indicating the user's emotional state.

[0758] Step 4:

[0759] The device sends collected audio, image, and emotion data to the server. This transmission process, which includes data packaging, is securely transferred to the server over the network. The input is all data collected by the device, and the output is the packaged, integrated data sent to the server.

[0760] Step 5:

[0761] The server uses the received data to generate business guidelines through a generative AI model. The input consists of text data, image data, and sentiment data collected on the server, which are integrated to create customized guidelines. The output is business guidelines that reflect the user's current emotional state. Based on the prompt text, the generative AI model suggests appropriate guidance content.

[0762] Step 6:

[0763] The server saves the generated business guidelines to the cloud. The input is the generated business guidelines, which are recorded in the database for future reference using a storage method. Specifically, this involves writing to the data storage system. The output is the business guidelines stored in the cloud.

[0764] Step 7:

[0765] The next time a work instruction is given, the server will send the most suitable work task to the terminal based on the stored work guidelines. The input consists of past work guidelines and sentiment data stored in the cloud, and new work guidelines are generated and sent based on this. The output is instructions for performing the work adapted to the user. The script is adjusted according to the user's emotional state.

[0766] (Application Example 2)

[0767] 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".

[0768] Conventional business support systems automate basic instructions without considering user emotions, resulting in instructions that don't match the user's emotional state and fail to be fully effective. Furthermore, it was difficult for users to properly edit instructional materials. For home robots to provide users with comfortable and personalized services, an approach that considers user emotions is necessary. Therefore, there is a need for a system that automatically provides appropriate instruction tailored to the user's emotions, while also allowing users to easily edit instructional materials themselves.

[0769] 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.

[0770] In this invention, the server includes an information input device for receiving work instructions, a language recognition means for converting received voice information into text information, a video processing means for analyzing operation content from received screen information, an emotion recognition means for analyzing the user's emotional state and adjusting the content of work instruction materials accordingly, a recording means for saving the generated work instruction materials and referring to them when performing work in the future, a management and control means for automatically performing work based on the saved work instruction materials, and a modification means for allowing the user to adjust the generated work instruction materials. This enables work instruction that reflects the user's emotions, allows the user to easily edit the work instruction materials, and makes the services provided by the home robot even more user-friendly and effective.

[0771] An "information input device" is a device used to receive work instructions and emotional states from users.

[0772] "Language recognition means" refers to technology that has the function of analyzing received audio information and converting it into textual information.

[0773] "Image processing means" refers to technology used to analyze the operation content based on the received screen information.

[0774] "Emotion recognition means" refers to technology that analyzes a user's facial expressions and tone of voice to recognize their emotional state.

[0775] "Generation means" refers to technology for creating work instruction materials using text information and screen information.

[0776] "Recording means" refers to a function for saving generated work instruction materials so that they can be referenced later.

[0777] "Management and control means" refers to a function that automatically performs tasks based on stored work instruction materials.

[0778] "Modification means" refers to a function that allows users to adjust and edit the generated work instruction materials.

[0779] Embodiments of this invention relate to a user assistance system using a home robot. The system integrates an information input device, a language recognition function, an image processing function, an emotion recognition function, a generation function, a recording function, a management and control function, and a modification function. The user inputs work instructions by voice through the home robot, and the information input device captures the voice and screen information.

[0780] This information is converted into text using language recognition means, and then image processing means analyze the screen information and extract the operation content. Subsequently, emotion recognition means analyze the user's facial expressions and tone of voice to identify their emotional state. This series of data is then transmitted to the cloud by a server.

[0781] The server uses a generation function to create work instruction materials that combine text information, operation details, and emotional states. These materials are saved using a recording function and automatically used for subsequent work executions by a management control function. Users can edit the generated materials as needed using a modification function.

[0782] As a concrete example, when a home robot cleans, it can provide a message tailored to the user's emotions while they rest, such as, "Thank you for your hard work. I'll finish cleaning while you rest, so please relax." In this way, the system can provide optimal support that responds to the user's emotions.

[0783] An example of a prompt message is as follows:

[0784] "Design the emotional responses for a robot that helps with household chores. Think of suggestions for when the user is relaxed and support options for when they are busy."

[0785] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0786] Step 1:

[0787] The terminal receives voice commands from the user. The input is the user's voice data. The terminal acquires this voice data through the microphone and temporarily stores it in its memory.

[0788] Step 2:

[0789] The terminal converts received audio data into text information using language recognition means. The input is audio data, and the output is corresponding text data. Speech recognition software is used to analyze the audio signal and convert it into text format.

[0790] Step 3:

[0791] The terminal captures user screen operations and analyzes them using video processing equipment. The input is screen data, and the output is data indicating the operations performed. An image analysis algorithm is used to identify changes in the screen and extract the operations performed.

[0792] Step 4:

[0793] The device uses a camera to record the user's facial expressions and analyzes them using emotion recognition technology. The input is video data of the user, and the output is data indicating their emotional state. It analyzes facial features and infers emotions from facial expressions.

[0794] Step 5:

[0795] The server receives text data, operation data, and emotion data sent from the terminal. This data is then integrated and stored in the cloud.

[0796] Step 6:

[0797] The server uses a generation function to combine received data and generate work instruction materials. Inputs include text data, operation data, and sentiment data, while output is work instruction materials. The data is integrated and formatted into a set of instructions.

[0798] Step 7:

[0799] The server records the generated work instruction materials and uses its management and control functions to automatically utilize them during the next work execution. These materials will be referenced when giving the next instructions.

[0800] Step 8:

[0801] Users can edit and adjust work instruction materials as needed through the editing function. Users can also review and modify the materials using the interface.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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."

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] The following is further disclosed regarding the embodiments described above.

[0824] (Claim 1)

[0825] A terminal device for receiving work instructions,

[0826] A speech recognition means that converts received audio data into text data,

[0827] An image processing means that analyzes the operation content from the received screen data,

[0828] A generation means for generating a business manual using the text data and the image data,

[0829] A means of saving the generated work manual and referring to it when performing the work again,

[0830] A system including control means for automatically executing tasks based on saved work manuals.

[0831] (Claim 2)

[0832] The system according to claim 1, characterized by comprising a transmission means for packaging audio data and image data and sending them to a server.

[0833] (Claim 3)

[0834] The system according to claim 1, characterized by comprising editing means that enable users to edit the generated business manual.

[0835] "Example 1"

[0836] (Claim 1)

[0837] An information processing device for receiving work instructions,

[0838] A recognition means that converts received audio information into text information,

[0839] A recognition means that analyzes the operation content from the received screen information,

[0840] A means for creating a work instruction manual using the aforementioned text information and the aforementioned screen information,

[0841] A means of recording the created work instruction manual and referring to it when performing the work in the future,

[0842] A system that includes management means for automatically executing tasks based on saved work instruction manuals.

[0843] (Claim 2)

[0844] The system according to claim 1, characterized by comprising a transmission means for transmitting audio information and screen information together to a computer.

[0845] (Claim 3)

[0846] The system according to claim 1, characterized in that it includes an editing means that allows the user to edit the created work instruction manual.

[0847] "Application Example 1"

[0848] (Claim 1)

[0849] An information processing device for receiving work instructions,

[0850] A speech recognition means that converts received audio information into text information,

[0851] An image analysis means that analyzes the procedure content from the received operation information,

[0852] A generation means for generating a work procedure manual using the text information and the procedure information,

[0853] A means of recording the generated work procedure manual and referring to it when performing the work in the future,

[0854] A control means for automatically executing tasks based on recorded work procedures,

[0855] A system that includes manufacturing process control means for applying operational procedures to setting up and maintaining production lines.

[0856] (Claim 2)

[0857] The system according to claim 1, characterized by comprising communication means for packaging voice information and procedural information and transmitting them to an information processing device.

[0858] (Claim 3)

[0859] The system according to claim 1, characterized by comprising a means for enabling users to revise the generated work procedure manual.

[0860] "Example 2 of combining an emotion engine"

[0861] (Claim 1)

[0862] An information processing device for receiving work instructions,

[0863] A speech recognition means that converts received audio information into text information,

[0864] Image processing means for analyzing the operation content from received image information,

[0865] An emotion recognition means that acquires emotional information using facial expressions and tone of voice,

[0866] A generation means for generating business guidelines using the aforementioned text information, image information, and emotion information,

[0867] A means of saving the generated work guidelines and referring to them when performing future tasks,

[0868] A system including control means for automatically performing tasks based on saved work guidelines.

[0869] (Claim 2)

[0870] The system according to claim 1, characterized by comprising a transmission means for packaging audio information and image information and transmitting them to a network connection device.

[0871] (Claim 3)

[0872] The system according to claim 1, characterized by comprising editing means that enable users to edit the generated business guidelines.

[0873] "Application example 2 when combining with an emotional engine"

[0874] (Claim 1)

[0875] An information input device for receiving work instructions,

[0876] A language recognition means that converts received audio information into text information,

[0877] A video processing means that analyzes the operation content from the received screen information,

[0878] A generation means for generating business instruction materials using the aforementioned text information and the aforementioned screen information,

[0879] A means of recording the generated work instruction materials and referring to them when performing the work in the future,

[0880] A management and control means for automatically executing tasks based on saved work instruction materials,

[0881] An emotion recognition means that analyzes the user's emotional state and adjusts the content of the work instruction materials accordingly,

[0882] An information processing system that includes this.

[0883] (Claim 2)

[0884] The information processing system according to claim 1, characterized by comprising a transmission means for integrating voice information, screen information, and emotional state data and transmitting them to a communication device.

[0885] (Claim 3)

[0886] The information processing system according to claim 1, characterized by comprising a means for modifying the generated work instruction materials so that the user can adjust them. [Explanation of symbols]

[0887] 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 terminal device for receiving work instructions, A speech recognition means that converts received audio data into text data, An image processing means that analyzes the operation content from the received screen data, A generation means for generating a business manual using the text data and the image data, A means of saving the generated work manual and referring to it when performing the work again, A system including control means for automatically executing tasks based on saved work manuals.

2. The system according to claim 1, characterized by comprising a transmission means for packaging audio data and image data and sending them to a server.

3. The system according to claim 1, characterized in that it includes editing means that allow the user to edit the generated business manual.

Images