system

Abstract

We provide the system. [Solution] As a means of data collection, there is a means of recording each operation procedure and storing it in a database, As a preprocessing measure, the means include converting audio data to text and extracting information from image data, As a data analysis method, a means of comparing and analyzing standard operating procedures and operation logs to detect inconsistencies is provided. As a means of providing feedback, the system includes a means of notifying the user of detected inconsistencies and proposing corrective measures, As a means of generating reports, there is a means of recording the completion status of operations and creating a report that includes improvement suggestions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional work process, there have been problems that a lot of time and personnel are required, and human errors are likely to occur. Also, it is difficult to maintain a certain quality, and it is difficult to respond quickly especially when an urgent response is required. Therefore, a method for efficiently performing operations while maintaining the quality of work is demanded.

Means for Solving the Problems

[0005] This invention records each operation procedure by providing data collection means and stores it in a database. Furthermore, as a preprocessing means, it converts audio data into text and extracts information from image data to convert it into a data format that is easy to analyze. The data analysis means compares and analyzes standard work procedures and operation logs to automatically detect inconsistencies. Furthermore, the detected inconsistencies are notified to the user in real time by the feedback provision means, and corrective measures are presented. Using the report generation means, the completion status of operations is recorded and a report including improvement suggestions is created to improve the quality and efficiency of work.

[0006] "Data collection means" refers to the part of the system that records each operation procedure and stores it in a database.

[0007] The "preprocessing means" refers to the part of the system that converts audio data into text and extracts information from image data, converting it into a format that is easy to analyze.

[0008] The "data analysis means" refers to the system component that compares and analyzes standard operating procedures and operation logs to automatically detect inconsistencies.

[0009] The "feedback provision mechanism" refers to the part of the system that notifies the user of detected inconsistencies in real time and suggests corrective measures.

[0010] The "report generation means" refers to the part of the system that records the completion status of an operation and creates a report that includes suggestions for improvement.

[0011] An "anomaly detection means" is a part of the system that has the function of checking the range of input settings and identifying sequence errors.

[0012] A "real-time feedback mechanism" refers to a part of the system that has the functionality to display pop-up messages or alerts to the user. [Brief explanation of the drawing]

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

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] As an embodiment of the present invention, we introduce an automated system for work review utilizing generation AI. This system consists of elements that have the roles of server, terminal, and user, and performs data collection, preprocessing, analysis, inconsistency detection, feedback provision, and report generation. The specific operation details are described below.

[0035] The server first records each operation procedure and stores it in a database. The collected data includes audio and image data, which are stored in an appropriate format. Next, the server performs preprocessing, converting the audio data to text and extracting specific information from the image data. This converts the data into a format that is easy to analyze. Subsequently, based on the converted data, the server performs data analysis and compares it with standard operating procedures and operation logs to detect inconsistencies.

[0036] Any detected inconsistencies or errors are notified to the user in real time via the device. The device uses pop-up messages and alerts to indicate the detection and suggest necessary corrections. The user can then correct their work based on this information.

[0037] The user reviews the information presented by the system and performs the correct actions according to the instructions. The user's actions are then sent back to the server for analysis.

[0038] Finally, the server records the completion status of all operations and generates a report based on that data. This report includes suggestions for improving the work and is provided to the administrator. This ensures continuous improvement in future work efficiency and quality.

[0039] For example, when configuring network equipment, suppose the server analyzes the user's work log and detects an incorrect IP address setting. In this case, the terminal displays a warning to the user and presents the correct IP address to be corrected. As a result, the user can quickly correct the error and maintain the quality of their work.

[0040] Based on the above, the present invention is expected to achieve automation and efficiency in work and contribute to the reduction of human error.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server receives user instructions and input operations in real time and records them as logs in the database. Audio and image data are also collected during this process.

[0044] Step 2:

[0045] The server converts recorded audio data into text using speech recognition technology, and extracts necessary textual information from image data using image recognition technology. This organizes the data into a unified format.

[0046] Step 3:

[0047] The server analyzes the converted text data and checks for inconsistencies with the operation log, referring to pre-configured standard operating procedures (SOPs). Natural language processing techniques are used for this analysis.

[0048] Step 4:

[0049] When the server detects inconsistencies or errors, it logs them and generates feedback data. This data specifies which step the error occurred in and what kind of error it was.

[0050] Step 5:

[0051] The terminal displays feedback data received from the server to the user in real time. This is done using pop-up messages and alerts, which show details of detected errors and recommended corrective steps.

[0052] Step 6:

[0053] The user reviews the feedback displayed on the device and takes the necessary actions based on the instructions. The corrected actions are then sent back to the server for re-analysis.

[0054] Step 7:

[0055] Once all tasks are completed, the server compiles the results of each task and generates an overall work report and improvement suggestions. This report is provided to the administrator and used for future business improvements.

[0056] (Example 1)

[0057] 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."

[0058] In the field of information processing, there is a need to efficiently record and analyze operating procedures, thereby enabling the early detection of inconsistencies in manual work and human errors. However, existing systems face challenges in providing rapid responses due to the cost and time required for data preprocessing, analysis, and feedback.

[0059] 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.

[0060] In this invention, the server includes information processing means for monitoring operating procedures and storing data, information conversion means for converting audio information into written form and extracting specific information from visual information, and information analysis means for comparing standard procedures and work records using a generation AI model and detecting differences. This enables efficient monitoring and analysis of operating procedures and rapid detection of inconsistencies.

[0061] "Information processing means" refers to a method for monitoring operating procedures and efficiently collecting and storing data.

[0062] An "information conversion means" is a method for converting audio information into text format and for extracting necessary data from image information.

[0063] "Information analysis means" refers to a method for detecting inconsistencies by comparing standard work procedures with operation logs using a generative AI model.

[0064] A "feedback device" is a device that notifies the user of detected inconsistencies and suggests corrective actions.

[0065] A "report generation device" is a device that records the completion status of work and creates a report that includes suggestions for work improvement.

[0066] An "anomaly detection method" is a method for verifying the range of input information and identifying errors in its order.

[0067] "Information provision means" refers to methods for presenting notification messages and warnings to users in real time.

[0068] This invention consists of an automated system for task review utilizing generative AI. The system is operated by elements that have the roles of server, terminal, and user.

[0069] The server first monitors the operating procedures and collects and stores data, including audio and image data. Audio data is converted to text using speech recognition software, such as a general speech-to-text service. Image recognition technology is used to extract information from image data.

[0070] The server then uses a generative AI model to analyze the preprocessed data. Specifically, it compares standard operating procedures with user operation logs to detect inconsistencies between the data. The use of the generative AI model enables faster and more accurate analysis than before.

[0071] Any detected inconsistencies are notified to the user in real time via the device. The device provides feedback using pop-up messages and alerts, and suggests corrective actions to the user. This allows the user to quickly adjust their work.

[0072] The user performs the correct actions based on the feedback provided. The corrected actions are sent back to the server for further analysis. Finally, the server generates a report based on the completion status of the work and provides it to the administrator. This report also includes suggestions for improving the work, which is expected to improve the efficiency and quality of operations.

[0073] For example, if an incorrect IP address is set during network configuration, the terminal will display a warning to the user and provide the correct IP address for correction. As a result, the user can immediately correct the error and maintain the quality of their work.

[0074] An example of a prompt message might be, "Use a generative AI model to identify inconsistencies in the work log and generate suggestions."

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server monitors user actions and collects audio and image data. This data is stored in a database for subsequent processing. Specifically, it formats and stores raw data received from sensors and cameras. The input consists of audio and images based on user procedures, while the output is the database containing this data.

[0078] Step 2:

[0079] The server preprocesses the stored data. Audio data is converted to text using speech recognition software, and necessary information is extracted from image data using image processing software. This facilitates data analysis. The input is formatted audio and image data, and the output is analyzable text and extracted information.

[0080] Step 3:

[0081] The server performs analysis using a generative AI model. Based on the previously processed text and extracted information, it compares standard operating procedures and operation logs to detect inconsistencies between the data. Specifically, the analysis identifies patterns of inconsistencies and reveals anomalies. The input is the pre-processed text and extracted information, and the output is the detected inconsistencies.

[0082] Step 4:

[0083] The terminal notifies the user of any detected inconsistencies. It provides feedback and suggests corrective actions using pop-up messages and alerts. Specifically, it displays warnings on the screen to help the user understand the necessary corrections. The input is the inconsistent data from the server, and the output is the instructional information presented to the user.

[0084] Step 5:

[0085] The user modifies their work based on the feedback provided. They perform the correct operations and send the results back to the server. Specifically, they follow the on-screen guidance to implement the proposed modifications. The input is the instructions from the terminal, and the output is the result of the modified operation.

[0086] Step 6:

[0087] The server ultimately analyzes the corrected operation logs and generates a report. This report includes improvement suggestions and information on streamlining operations. Specifically, it evaluates the results based on comprehensive data and clearly identifies areas for improvement. The input is the corrected operation logs, and the output is the report.

[0088] (Application Example 1)

[0089] 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."

[0090] Modern manufacturing facilities and factories face challenges such as inconsistencies in work procedures and human error, which reduce production efficiency and increase the likelihood of quality defects. Furthermore, because it is difficult for workers to make quick, real-time corrections, there is a need for efficient solutions to address these issues.

[0091] 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.

[0092] In this invention, the server includes means for collecting data, means for preprocessing data, and means for performing data analysis. This makes it possible to immediately detect inconsistencies in work procedures and provide corrective suggestions to the worker in real time.

[0093] "Data collection means" refers to devices or methods for recording work procedures and storing them in a database.

[0094] The "preprocessing means" is a function that converts collected audio information into text and extracts necessary information from image information.

[0095] "Data analysis means" refers to the process and techniques of comparing standard operating procedures with actual operation records to detect inconsistencies.

[0096] A "feedback provision mechanism" is a mechanism that notifies the operator of detected inconsistencies and proposes corrective measures.

[0097] The "report generation means" is a function that records the completion status of an operation and creates a report that includes suggestions for improvement.

[0098] "A means of displaying feedback in real time on an information terminal carried by the operator" refers to a technology that instantly displays feedback information on a portable electronic device.

[0099] The system implementing this invention has a configuration that facilitates data collection, preprocessing, data analysis, real-time feedback, and report generation. It consists of three components: a server, a terminal, and a user.

[0100] The server first collects data. Using speech recognition software (e.g., Google® Speech-to-Text) and an image analysis platform (e.g., OpenCV), it records audio and image data related to factory work procedures and stores them in a database. Next, as a preprocessing step, the audio data is converted to text format, and work-related information is extracted from the image data. This data is analyzed using a custom generative AI model with PyTorch to detect inconsistencies when compared to standard work procedures.

[0101] The terminal plays a role in notifying the user of detected inconsistencies in real time. By displaying feedback as pop-up messages or warning alerts on portable information terminals such as smartphones and tablets, it provides an environment where users can take immediate action.

[0102] Users modify their work based on the feedback they receive and feed their results back to the server, continuously optimizing the process. Finally, the server records the work completion status and generates a report including improvement suggestions. This report is then used by administrators to improve future work efficiency and quality.

[0103] As a concrete example, consider a factory where a robot verifies in real time whether it has correctly installed parts in their designated positions. The server analyzes the robot's operation logs and immediately notifies the user via a terminal if it detects incorrect operation or misalignment. Based on this information, the user can quickly take corrective action.

[0104] An example of a prompt for the generated AI model is, "Detect inconsistencies in the factory line operation logs and generate real-time alerts." Using this prompt, the server can automatically perform analysis and improve the accuracy of inconsistency detection.

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

[0106] Step 1:

[0107] The server receives audio and image data in real time from sensors and cameras within the factory. The server stores this data directly in a database. The input is audio and image data, and the output is the raw data stored in the database.

[0108] Step 2:

[0109] The server uses speech recognition software to convert audio data into text data and image analysis software to extract features relevant to the task from the image data. The input is the audio and image data obtained in step 1, and the output is the transcribed audio data and the extracted image feature data.

[0110] Step 3:

[0111] The server uses the transformed and extracted data to compare it with standard operating procedure data using a generative AI model. If inconsistencies are detected, the details are recorded. The input consists of text data and image feature data, and the output is whether or not inconsistencies exist and their details.

[0112] Step 4:

[0113] The server sends detected inconsistencies to the terminal, which then notifies the user in real time as a pop-up message. The input is the detailed information about the inconsistency, and the output is the notification message displayed on the terminal.

[0114] Step 5:

[0115] The user receives a notification from the device and modifies the work according to the suggested corrections. The input is the suggested corrections from the device, and the output is the modified work procedure.

[0116] Step 6:

[0117] The server recollects the data after the work has been corrected, reanalyzes it, and records the completion status of the work. Finally, it generates a report including improvement suggestions and provides it to the administrator. The input is the corrected work data, and the output is a report including the completion status record and improvement suggestions.

[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] As an embodiment of the present invention, we introduce an automated work review system incorporating an emotion engine that recognizes user emotions. This system has functions for emotion recognition in addition to data collection, preprocessing, data analysis, inconsistency detection, feedback provision, and report generation. The specific operation of each component is described below.

[0120] The server receives user input, including voice and facial expression data, in real time and stores it in a database. The emotion engine uses the voice and image data to analyze the user's emotional state and identify states such as "stress," "confusion," and "excitement." This information is also stored in the database.

[0121] Next, the server performs preprocessing, converting the audio data into text and extracting necessary information from the image data. It also attaches the analysis results from the emotion engine as supplementary information to the data.

[0122] Based on the converted data, the server performs data analysis and detects inconsistencies by referring to standard operating procedures (SOPs) and operation logs. Considering the sentiment analysis results, it determines the optimal feedback content according to the work situation.

[0123] Feedback on detected inconsistencies and errors is communicated to the user via the device. The feedback is delivered in a tone and wording that takes into account the user's emotional state, and is provided in an appropriate format, ranging from gentle suggestions to detailed technical instructions.

[0124] The user reviews the feedback displayed on the device and performs the necessary actions according to the instructions. The data and user sentiment after the action are sent back to the server for further analysis if necessary.

[0125] Once all operations are complete, the server compiles the results and generates a detailed work report, including sentiment analysis. This report includes improvement suggestions and is provided to administrators for use in future business improvements.

[0126] For example, if the system detects a user's unstable emotional state, the terminal will gently suggest how to correct the error. This approach allows users to proceed with the correction process without feeling stressed. This is expected to further improve work efficiency and quality.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] When a user begins an operation, the server receives input data in real time and records each operation step in the database. At the same time, audio and image data are also collected.

[0130] Step 2:

[0131] The server converts the recorded audio data into text using a speech recognition engine and extracts necessary textual information from the image data. Simultaneously, it uses an emotion engine to analyze the user's emotional state from their voice tone and facial expressions. The results of this analysis are then added to the data.

[0132] Step 3:

[0133] The server compares the converted operation data with the standard operating procedure (SOP), performs data analysis, and detects inconsistencies. It also takes sentiment analysis results into consideration to determine the content of the feedback appropriate to the user's emotional state.

[0134] Step 4:

[0135] The server generates feedback based on the inconsistencies and sentiment analysis results. This feedback is crafted in a tone that matches the user's emotional state and sent to the device.

[0136] Step 5:

[0137] The device displays feedback received from the server to the user. Messages that take the user's emotional state into consideration are output as pop-ups or alerts, and errors and suggested solutions are presented.

[0138] Step 6:

[0139] The user reviews the information displayed on the device and performs the necessary actions according to the instructions. After any modifications or additions are made, the data is sent back to the server.

[0140] Step 7:

[0141] Once all tasks are complete, the server compiles the results and generates a detailed report, including sentiment analysis findings. This report, which includes suggestions for improvement, is created for administrators.

[0142] (Example 2)

[0143] 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".

[0144] In many modern work environments, there is a demand for increased user efficiency and accuracy. However, manual data recording and analysis, as well as providing human-based feedback, are time-consuming and labor-intensive. Furthermore, there is a lack of means to provide timely and appropriate feedback while reducing the stress and confusion users experience during their work. In this context, there is a growing need for systems that support users in performing tasks efficiently and accurately.

[0145] 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.

[0146] In this invention, the server includes means for recording user input and storing data including audio and video data; means for pre-processing audio information into text information and extracting relevant information from video data; means for analyzing data consistency by comparing work instructions and operation history and detecting inconsistencies; means for providing feedback to the user in appropriate words and tone regarding data inconsistencies based on sentiment analysis results; and means for recording the progress of operations, generating a detailed report including sentiment analysis, and including suggestions. This enables the provision of immediate feedback that takes into account the user's emotional state, thereby simultaneously improving work efficiency and maintaining accuracy.

[0147] "User input" refers to information such as audio and video data that users provide to the system.

[0148] "Converting audio information to text information" is the process of converting data given in audio format into text, and is a means of facilitating data analysis by machines.

[0149] "Extracting relevant information from video data" refers to the process of identifying important features and patterns within video data and extracting the data necessary for analysis.

[0150] A "work instruction sheet" is a document that describes the standard procedures and instructions for performing a specific task.

[0151] "Operation history" refers to a record of past actions and inputs a user has made while interacting with the system.

[0152] "Analyzing data integrity and detecting inconsistencies" is the process of evaluating the consistency between data and identifying behaviors or information that deviate from standard procedures.

[0153] "Emotional analysis results" refer to the results of an analysis of the user's emotional state, with corresponding data points provided as numerical values ​​or categories.

[0154] Providing feedback in the right words and tone is the process of conveying information in a way that is optimized for the user's emotional state. This helps reduce stress and aids understanding.

[0155] "Generating a detailed report" means creating a document that summarizes the overall progress and results of the work, and includes feedback and suggestions that will be useful for future reference.

[0156] This system is designed to support user operational efficiency and accuracy, and its embodiments are shown below.

[0157] The server records user input in real time during each operation step. This requires a terminal equipped with a microphone and camera to process voice and video input. The server uses speech recognition software to convert voice data into text. For example, a speech recognition API or machine learning model could be applied as a common speech recognition technique. Image analysis software is used to extract relevant information from video data and estimate the user's emotional state. For example, a common image analysis API could be used as the image recognition technique.

[0158] The terminal is responsible for providing feedback from the server to the user. The server analyzes the user's emotional state, and based on the results, the terminal displays feedback in an appropriate tone and language. This ensures that the information the user receives is tailored to their individual situation, improving work efficiency and accuracy. Feedback is displayed as pop-up messages or alerts as needed.

[0159] The user performs the instructed operations based on feedback received through the terminal. These operations are carried out according to standard operating procedures, ensuring consistency and efficient progress. The progress of the operations is sent back to the server for re-analysis as needed.

[0160] Furthermore, once the operation is complete, the server generates a detailed work report. This report includes the overall status of the work, any inconsistencies detected, the results of the sentiment analysis, and suggestions for improvement based on those results. This makes it possible to provide specific feedback that can be used to improve future operations.

[0161] As a concrete example, the system issues prompts to the generating AI model such as, "Analyze the user's emotions and provide appropriate feedback. If the user is feeling anxious, use specific and gentle language." This design allows the system to function interactively in a way that best suits the user's emotional state.

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

[0163] Step 1:

[0164] The server receives audio and video data from the user in real time through the terminal and stores it in a database. It receives audio data acquired using a microphone and video data acquired using a camera as input. This data is stored in the database for use in a later analysis step. The resulting output is the audio and video data registered in the database.

[0165] Step 2:

[0166] The server converts audio data into text and extracts emotional features from video data. It uses audio data stored in a database as audio input and transcribes it using a speech recognition algorithm. In parallel, it analyzes facial expressions from video data using image analysis technology and extracts the user's emotional state numerically. This results in output consisting of transcribed audio data and parameters indicating the emotional state.

[0167] Step 3:

[0168] The server detects inconsistencies by comparing standard operating procedures (SOPs) with operation history based on transcribed voice data and emotion parameters. It uses recent user operation history and transcribed voice data as input. This data is compared against the SOPs to determine if there are any discrepancies or deviations. The output provides a list of detected inconsistencies and supplementary information explaining their causes.

[0169] Step 4:

[0170] The terminal uses inconsistency information and sentiment analysis results obtained from the server to display feedback to the user in an appropriate tone. It receives inconsistency information and sentiment parameters as input and generates a feedback message. The generated message uses calm and polite language, especially when the user is showing signs of stress or confusion. The output is the feedback message displayed on the terminal.

[0171] Step 5:

[0172] The user performs actions based on the displayed feedback and sends new action data and emotional state results to the server. Input includes receiving specific instructions, including feedback messages displayed on the device, and performing actions according to those instructions. Output includes returning the results of the performed actions and the current emotional state as data to the server.

[0173] Step 6:

[0174] The server generates a detailed report based on the final operation results and accumulated sentiment analysis data. It receives accumulated data from each step, analysis results, and inconsistency information as input, and integrates them to create a report that includes an operation summary and improvement suggestions. The final output is a report that can be submitted to administrators or used for internal optimization.

[0175] (Application Example 2)

[0176] 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".

[0177] In factory and other work environments, providing feedback based on standard operating procedures and inconsistency detection without considering the emotional state of workers can lead to decreased efficiency and safety issues. Therefore, a system is needed that appropriately recognizes workers' emotions and provides feedback based on those emotions.

[0178] 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.

[0179] In this invention, the server includes, as a data collection means, means for recording each operation procedure and storing it in a database; as a preprocessing means, means for converting audio data into text and extracting information from image data; and as an emotion analysis means, means for identifying the user's emotional state using facial expressions and audio data and incorporating the results into the feedback. This enables the provision of appropriate feedback that takes the user's emotional state into consideration, thereby improving work efficiency and safety.

[0180] A "data collection means" is a component that provides the function of recording each operation procedure and storing it in a database.

[0181] A "preprocessing means" is a component that has the function of converting audio data into text and extracting information from image data.

[0182] A "data analysis tool" is a component that has the function of comparing and analyzing standard operating procedures and operation logs to detect inconsistencies.

[0183] A "feedback provision mechanism" is a component that has the function of notifying the user of detected inconsistencies and suggesting appropriate corrections.

[0184] An "emotion analysis tool" is a component that uses facial expression and voice data to identify the user's emotional state and incorporates the results into the feedback.

[0185] A "report generation means" is a component that has the function of recording the completion status of an operation and creating a report that includes suggestions for improvement.

[0186] The work support system based on this invention aims to improve work efficiency and safety in a factory environment. This system provides feedback that takes into account the emotional state of the worker through emotion analysis.

[0187] The server first uses data collection means to collect voice and facial expression data from workers and stores it in a database. Voice is acquired via a microphone, and facial expression data is captured by a camera. These data are then processed by preprocessing means, which transcribe the voice data into text and extract information from the image data, converting them into a format suitable for analysis.

[0188] For emotion analysis, open-source libraries such as "Librosa" and "OpenCV" are used. These software programs are used to extract emotional features from audio and analyze facial expressions from images in real time. This allows for the identification of the worker's emotional state, which is then stored in a database.

[0189] The data analysis system detects inconsistencies between standard operating procedures and operation logs, taking into account the results of sentiment analysis. The feedback system generates optimal instructions and corrections based on these inconsistencies and the worker's emotions, and presents them to the worker via a terminal. The presented information is customized to include softer tones or more technically detailed explanations as needed.

[0190] The user modifies their work based on the displayed feedback and sends the results from their terminal to the server. The server comprehensively evaluates the completion status of the operation and the user's emotional state, and generates a report that includes suggestions for improvement. This report is provided to the administrator and used to improve future operations.

[0191] For example, if an error is detected on the production line, the system senses the worker's stress and displays feedback on the terminal such as, "Don't worry, it's okay. Please double-check step B." This feedback helps the worker regain their composure and make the appropriate correction.

[0192] As an example of a prompt for the generative AI model, providing a format such as, "Analyze the emotional state of this data and generate relaxing feedback if 'stress' or 'confusion' is detected," allows for a more human-like response.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The server collects voice and facial expression data from the user via microphone and camera and stores it in a database. The input consists of real-time captured voice and image data, which are also stored in the database. Initial data is organized with a time stamp and session ID.

[0196] Step 2:

[0197] The server uses preprocessing to convert the collected audio data into text using "Librosa" and extract features from the image data using "OpenCV". The input is the audio and image data stored in step 1, and the output is the generated text data and digitized facial expression feature data.

[0198] Step 3:

[0199] The server identifies the user's emotional state based on data transcribed and digitized by emotion analysis tools. The input is the data generated in step 2, and the output is the user's emotional state (e.g., stress, joy). Here, a generative AI model is applied to analyze emotions based on specific prompt sentences.

[0200] Step 4:

[0201] The server uses data analysis tools to compare standard operating procedures (SOPs) and operation logs, taking emotional states into consideration, to detect inconsistencies. The input consists of the emotional analysis results from step 3 and the operation procedure data, and the output is a list of inconsistencies.

[0202] Step 5:

[0203] The device displays appropriate feedback to the user based on a list of inconsistencies and emotional state through a feedback provision mechanism. The input is the result of step 4, and the output is a feedback message displayed to the user. The message is customized using prompt sentences by a generating AI model.

[0204] Step 6:

[0205] The user reviews the feedback displayed on the device, performs the correction work according to the instructions, and sends the results of the work from the device to the server. The input is the feedback message and the work correction information, and the output is the updated operation data sent to the server.

[0206] Step 7:

[0207] The server uses a report generation mechanism to generate a detailed work report, including improvement suggestions, after all operations are completed. The input is the operation history and sentiment analysis results from step 6, and the output is a report provided to the administrator.

[0208] In this manner, the work support system operates efficiently in real time, providing optimal support that takes user emotions into consideration.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] As an embodiment of the present invention, we introduce an automated system for work review utilizing generation AI. This system consists of elements that have the roles of server, terminal, and user, and performs data collection, preprocessing, analysis, inconsistency detection, feedback provision, and report generation. The specific operation details are described below.

[0226] The server first records each operation procedure and stores it in a database. The collected data includes audio and image data, which are stored in an appropriate format. Next, the server performs preprocessing, converting the audio data to text and extracting specific information from the image data. This converts the data into a format that is easy to analyze. Subsequently, based on the converted data, the server performs data analysis and compares it with standard operating procedures and operation logs to detect inconsistencies.

[0227] Any detected inconsistencies or errors are notified to the user in real time via the device. The device uses pop-up messages and alerts to indicate the detection and suggest necessary corrections. The user can then correct their work based on this information.

[0228] The user reviews the information presented by the system and performs the correct actions according to the instructions. The user's actions are then sent back to the server for analysis.

[0229] Finally, the server records the completion status of all operations and generates a report based on that data. This report includes suggestions for improving the work and is provided to the administrator. This ensures continuous improvement in future work efficiency and quality.

[0230] For example, when configuring network equipment, suppose the server analyzes the user's work log and detects an incorrect IP address setting. In this case, the terminal displays a warning to the user and presents the correct IP address to be corrected. As a result, the user can quickly correct the error and maintain the quality of their work.

[0231] Based on the above, the present invention is expected to achieve automation and efficiency in work and contribute to the reduction of human error.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The server receives user instructions and input operations in real time and records them as logs in the database. Audio and image data are also collected during this process.

[0235] Step 2:

[0236] The server converts recorded audio data into text using speech recognition technology, and extracts necessary textual information from image data using image recognition technology. This organizes the data into a unified format.

[0237] Step 3:

[0238] The server analyzes the converted text data and checks for inconsistencies with the operation log, referring to pre-configured standard operating procedures (SOPs). Natural language processing techniques are used for this analysis.

[0239] Step 4:

[0240] When the server detects inconsistencies or errors, it logs them and generates feedback data. This data specifies which step the error occurred in and what kind of error it was.

[0241] Step 5:

[0242] The terminal displays feedback data received from the server to the user in real time. This is done using pop-up messages and alerts, which show details of detected errors and recommended corrective steps.

[0243] Step 6:

[0244] The user reviews the feedback displayed on the device and takes the necessary actions based on the instructions. The corrected actions are then sent back to the server for re-analysis.

[0245] Step 7:

[0246] Once all tasks are completed, the server compiles the results of each task and generates an overall work report and improvement suggestions. This report is provided to the administrator and used for future business improvements.

[0247] (Example 1)

[0248] 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."

[0249] In the field of information processing, there is a need to efficiently record and analyze operating procedures, thereby enabling the early detection of inconsistencies in manual work and human errors. However, existing systems face challenges in providing rapid responses due to the cost and time required for data preprocessing, analysis, and feedback.

[0250] 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.

[0251] In this invention, the server includes information processing means for monitoring operating procedures and storing data, information conversion means for converting audio information into written form and extracting specific information from visual information, and information analysis means for comparing standard procedures and work records using a generation AI model and detecting differences. This enables efficient monitoring and analysis of operating procedures and rapid detection of inconsistencies.

[0252] "Information processing means" refers to a method for monitoring operating procedures and efficiently collecting and storing data.

[0253] An "information conversion means" is a method for converting audio information into text format and for extracting necessary data from image information.

[0254] "Information analysis means" refers to a method for detecting inconsistencies by comparing standard work procedures with operation logs using a generative AI model.

[0255] A "feedback device" is a device that notifies the user of detected inconsistencies and suggests corrective actions.

[0256] A "report generation device" is a device that records the completion status of work and creates a report that includes suggestions for work improvement.

[0257] An "anomaly detection method" is a method for verifying the range of input information and identifying errors in its order.

[0258] "Information provision means" refers to methods for presenting notification messages and warnings to users in real time.

[0259] This invention consists of an automated system for task review utilizing generative AI. The system is operated by elements that have the roles of server, terminal, and user.

[0260] The server first monitors the operating procedures and collects and stores data, including audio and image data. Audio data is converted to text using speech recognition software, such as a general speech-to-text service. Image recognition technology is used to extract information from image data.

[0261] The server then uses a generative AI model to analyze the preprocessed data. Specifically, it compares standard operating procedures with user operation logs to detect inconsistencies between the data. The use of the generative AI model enables faster and more accurate analysis than before.

[0262] Any detected inconsistencies are notified to the user in real time via the device. The device provides feedback using pop-up messages and alerts, and suggests corrective actions to the user. This allows the user to quickly adjust their work.

[0263] The user performs the correct actions based on the feedback provided. The corrected actions are sent back to the server for further analysis. Finally, the server generates a report based on the completion status of the work and provides it to the administrator. This report also includes suggestions for improving the work, which is expected to improve the efficiency and quality of operations.

[0264] For example, if an incorrect IP address is set during network configuration, the terminal will display a warning to the user and provide the correct IP address for correction. As a result, the user can immediately correct the error and maintain the quality of their work.

[0265] An example of a prompt message might be, "Use a generative AI model to identify inconsistencies in the work log and generate suggestions."

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The server monitors user actions and collects audio and image data. This data is stored in a database for subsequent processing. Specifically, it formats and stores raw data received from sensors and cameras. The input consists of audio and images based on user procedures, while the output is the database containing this data.

[0269] Step 2:

[0270] The server preprocesses the stored data. Audio data is converted to text using speech recognition software, and necessary information is extracted from image data using image processing software. This facilitates data analysis. The input is formatted audio and image data, and the output is analyzable text and extracted information.

[0271] Step 3:

[0272] The server performs analysis using a generative AI model. Based on the previously processed text and extracted information, it compares standard operating procedures and operation logs to detect inconsistencies between the data. Specifically, the analysis identifies patterns of inconsistencies and reveals anomalies. The input is the pre-processed text and extracted information, and the output is the detected inconsistencies.

[0273] Step 4:

[0274] The terminal notifies the user of any detected inconsistencies. It provides feedback and suggests corrective actions using pop-up messages and alerts. Specifically, it displays warnings on the screen to help the user understand the necessary corrections. The input is the inconsistent data from the server, and the output is the instructional information presented to the user.

[0275] Step 5:

[0276] The user modifies their work based on the feedback provided. They perform the correct operations and send the results back to the server. Specifically, they follow the on-screen guidance to implement the proposed modifications. The input is the instructions from the terminal, and the output is the result of the modified operation.

[0277] Step 6:

[0278] The server ultimately analyzes the corrected operation logs and generates a report. This report includes improvement suggestions and information on streamlining operations. Specifically, it evaluates the results based on comprehensive data and clearly identifies areas for improvement. The input is the corrected operation logs, and the output is the report.

[0279] (Application Example 1)

[0280] 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."

[0281] In modern manufacturing facilities and factories, there are issues such as inconsistencies in work procedures and human errors, which can lead to a decrease in production efficiency and an increased likelihood of defective products. Additionally, since it is difficult for workers to make quick corrections in real time, there is a need for means to efficiently solve this problem.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0283] In this invention, the server includes means for collecting data, means for performing preprocessing, and means for performing data analysis. This makes it possible to immediately detect inconsistencies in work procedures and provide amendment suggestions to workers in real time.

[0284] The "data collection means" is a device or method for recording work procedures and storing them in a database.

[0285] The "preprocessing means" is a function for converting the collected voice information into text and extracting necessary information from the image information.

[0286] The "data analysis means" is a process and technology for comparing the standard work procedure manual with the actual operation records to detect inconsistencies.

[0287] The "feedback providing means" is a mechanism for notifying the detected inconsistencies to the operating entity and presenting the amendment suggestions.

[0288] The "report generating means" is a function for recording the completion status of operations and creating a report including improvement suggestions.

[0289] The "means for displaying feedback on the information terminal carried by the operating entity in real time" is a technology for immediately displaying feedback information on a portable electronic device.

[0290] The system implementing this invention has a configuration that facilitates data collection, preprocessing, data analysis, real-time feedback, and report generation. It consists of three components: a server, a terminal, and a user.

[0291] The server first collects data. Using speech recognition software (e.g., Google Speech-to-Text) and an image analysis platform (e.g., OpenCV), it records audio and image data related to factory work procedures and stores them in a database. Next, as a preprocessing step, the audio data is converted to text format, and work-related information is extracted from the image data. This data is analyzed using a custom generative AI model with PyTorch to detect inconsistencies when compared to standard work procedures.

[0292] The terminal plays a role in notifying the user of detected inconsistencies in real time. By displaying feedback as pop-up messages or warning alerts on portable information terminals such as smartphones and tablets, it provides an environment where users can take immediate action.

[0293] Users modify their work based on the feedback they receive and feed their results back to the server, continuously optimizing the process. Finally, the server records the work completion status and generates a report including improvement suggestions. This report is then used by administrators to improve future work efficiency and quality.

[0294] As a concrete example, consider a factory where a robot verifies in real time whether it has correctly installed parts in their designated positions. The server analyzes the robot's operation logs and immediately notifies the user via a terminal if it detects incorrect operation or misalignment. Based on this information, the user can quickly take corrective action.

[0295] An example of a prompt for the generated AI model is, "Detect inconsistencies in the factory line operation logs and generate real-time alerts." Using this prompt, the server can automatically perform analysis and improve the accuracy of inconsistency detection.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The server receives audio and image data in real time from sensors and cameras within the factory. The server stores this data directly in a database. The input is audio and image data, and the output is the raw data stored in the database.

[0299] Step 2:

[0300] The server uses speech recognition software to convert audio data into text data and image analysis software to extract features relevant to the task from the image data. The input is the audio and image data obtained in step 1, and the output is the transcribed audio data and the extracted image feature data.

[0301] Step 3:

[0302] The server uses the transformed and extracted data to compare it with standard operating procedure data using a generative AI model. If inconsistencies are detected, the details are recorded. The input consists of text data and image feature data, and the output is whether or not inconsistencies exist and their details.

[0303] Step 4:

[0304] The server sends detected inconsistencies to the terminal, which then notifies the user in real time as a pop-up message. The input is the detailed information about the inconsistency, and the output is the notification message displayed on the terminal.

[0305] Step 5:

[0306] The user receives a notification from the terminal and modifies the work according to the presented amendment. The input is the amendment presented from the terminal, and the output is the modified work procedure.

[0307] Step 6:

[0308] The server collects the data again after the work is modified, performs re-analysis, and records the completion status of the work. Finally, a report including improvement suggestions is generated and provided to the administrator. The input is the modified work data, and the output is the report including the record of the completion status and improvement suggestions.

[0309] 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 recognition model 59 and perform specific processing using the user's emotion.

[0310] As a form for implementing the present invention, an operation inspection automation system incorporating an emotion engine for recognizing the user's emotion is introduced. This system has a function of performing emotion recognition in addition to data collection, preprocessing, data analysis, inconsistency detection, feedback provision, and report generation. The specific operation contents of each component are described below.

[0311] The server receives in real time the operation procedures including voice and expression data input from the user and stores them in the database. The emotion engine uses the voice and image data to analyze the user's emotional state and identify states such as "stress", "confusion", "excitement", etc. This information is stored in the database.

[0312] Next, the server executes preprocessing, converts the voice data into text, and extracts necessary information from the image data. Also, the analysis result by the emotion engine is attached to the data as additional information.

[0313] Based on the converted data, the server performs data analysis and detects inconsistencies by referring to standard operating procedures (SOPs) and operation logs. Considering the sentiment analysis results, it determines the optimal feedback content according to the work situation.

[0314] Feedback on detected inconsistencies and errors is communicated to the user via the device. The feedback is delivered in a tone and wording that takes into account the user's emotional state, and is provided in an appropriate format, ranging from gentle suggestions to detailed technical instructions.

[0315] The user reviews the feedback displayed on the device and performs the necessary actions according to the instructions. The data and user sentiment after the action are sent back to the server for further analysis if necessary.

[0316] Once all operations are complete, the server compiles the results and generates a detailed work report, including sentiment analysis. This report includes improvement suggestions and is provided to administrators for use in future business improvements.

[0317] For example, if the system detects a user's unstable emotional state, the terminal will gently suggest how to correct the error. This approach allows users to proceed with the correction process without feeling stressed. This is expected to further improve work efficiency and quality.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] When a user begins an operation, the server receives input data in real time and records each operation step in the database. At the same time, audio and image data are also collected.

[0321] Step 2:

[0322] The server converts the recorded audio data into text using a speech recognition engine and extracts necessary textual information from the image data. Simultaneously, it uses an emotion engine to analyze the user's emotional state from their voice tone and facial expressions. The results of this analysis are then added to the data.

[0323] Step 3:

[0324] The server compares the converted operation data with the standard operating procedure (SOP), performs data analysis, and detects inconsistencies. It also takes sentiment analysis results into consideration to determine the content of the feedback appropriate to the user's emotional state.

[0325] Step 4:

[0326] The server generates feedback based on the inconsistencies and sentiment analysis results. This feedback is crafted in a tone that matches the user's emotional state and sent to the device.

[0327] Step 5:

[0328] The device displays feedback received from the server to the user. Messages that take the user's emotional state into consideration are output as pop-ups or alerts, and errors and suggested solutions are presented.

[0329] Step 6:

[0330] The user reviews the information displayed on the device and performs the necessary actions according to the instructions. After any modifications or additions are made, the data is sent back to the server.

[0331] Step 7:

[0332] Once all tasks are complete, the server compiles the results and generates a detailed report, including sentiment analysis findings. This report, which includes suggestions for improvement, is created for administrators.

[0333] (Example 2)

[0334] 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".

[0335] In many modern work environments, there is a demand for increased user efficiency and accuracy. However, manual data recording and analysis, as well as providing human-based feedback, are time-consuming and labor-intensive. Furthermore, there is a lack of means to provide timely and appropriate feedback while reducing the stress and confusion users experience during their work. In this context, there is a growing need for systems that support users in performing tasks efficiently and accurately.

[0336] 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.

[0337] In this invention, the server includes means for recording user input and storing data including audio and video data; means for pre-processing audio information into text information and extracting relevant information from video data; means for analyzing data consistency by comparing work instructions and operation history and detecting inconsistencies; means for providing feedback to the user in appropriate words and tone regarding data inconsistencies based on sentiment analysis results; and means for recording the progress of operations, generating a detailed report including sentiment analysis, and including suggestions. This enables the provision of immediate feedback that takes into account the user's emotional state, thereby simultaneously improving work efficiency and maintaining accuracy.

[0338] "User input" refers to information such as audio and video data that users provide to the system.

[0339] "Converting audio information to text information" is the process of converting data given in audio format into text, and is a means of facilitating data analysis by machines.

[0340] "Extracting relevant information from video data" refers to the process of identifying important features and patterns within video data and extracting the data necessary for analysis.

[0341] A "work instruction sheet" is a document that describes the standard procedures and instructions for performing a specific task.

[0342] "Operation history" refers to a record of past actions and inputs a user has made while interacting with the system.

[0343] "Analyzing data integrity and detecting inconsistencies" is the process of evaluating the consistency between data and identifying behaviors or information that deviate from standard procedures.

[0344] "Emotional analysis results" refer to the results of an analysis of the user's emotional state, with corresponding data points provided as numerical values ​​or categories.

[0345] Providing feedback in the right words and tone is the process of conveying information in a way that is optimized for the user's emotional state. This helps reduce stress and aids understanding.

[0346] "Generating a detailed report" means creating a document that summarizes the overall progress and results of the work, and includes feedback and suggestions that will be useful for future reference.

[0347] This system is designed to support user operational efficiency and accuracy, and its embodiments are shown below.

[0348] The server records user input in real time during each operation step. This requires a terminal equipped with a microphone and camera to process voice and video input. The server uses speech recognition software to convert voice data into text. For example, a speech recognition API or machine learning model could be applied as a common speech recognition technique. Image analysis software is used to extract relevant information from video data and estimate the user's emotional state. For example, a common image analysis API could be used as the image recognition technique.

[0349] The terminal is responsible for providing feedback from the server to the user. The server analyzes the user's emotional state, and based on the results, the terminal displays feedback in an appropriate tone and language. This ensures that the information the user receives is tailored to their individual situation, improving work efficiency and accuracy. Feedback is displayed as pop-up messages or alerts as needed.

[0350] The user performs the instructed operations based on feedback received through the terminal. These operations are carried out according to standard operating procedures, ensuring consistency and efficient progress. The progress of the operations is sent back to the server for re-analysis as needed.

[0351] Furthermore, once the operation is complete, the server generates a detailed work report. This report includes the overall status of the work, any inconsistencies detected, the results of the sentiment analysis, and suggestions for improvement based on those results. This makes it possible to provide specific feedback that can be used to improve future operations.

[0352] As a concrete example, the system issues prompts to the generating AI model such as, "Analyze the user's emotions and provide appropriate feedback. If the user is feeling anxious, use specific and gentle language." This design allows the system to function interactively in a way that best suits the user's emotional state.

[0353] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0354] Step 1:

[0355] The server receives audio and video data from the user in real time through the terminal and stores it in a database. It receives audio data acquired using a microphone and video data acquired using a camera as input. This data is stored in the database for use in a later analysis step. The resulting output is the audio and video data registered in the database.

[0356] Step 2:

[0357] The server converts audio data into text and extracts emotional features from video data. It uses audio data stored in a database as audio input and transcribes it using a speech recognition algorithm. In parallel, it analyzes facial expressions from video data using image analysis technology and extracts the user's emotional state numerically. This results in output consisting of transcribed audio data and parameters indicating the emotional state.

[0358] Step 3:

[0359] The server detects inconsistencies by comparing standard operating procedures (SOPs) with operation history based on transcribed voice data and emotion parameters. It uses recent user operation history and transcribed voice data as input. This data is compared against the SOPs to determine if there are any discrepancies or deviations. The output provides a list of detected inconsistencies and supplementary information explaining their causes.

[0360] Step 4:

[0361] The terminal uses inconsistency information and sentiment analysis results obtained from the server to display feedback to the user in an appropriate tone. It receives inconsistency information and sentiment parameters as input and generates a feedback message. The generated message uses calm and polite language, especially when the user is showing signs of stress or confusion. The output is the feedback message displayed on the terminal.

[0362] Step 5:

[0363] The user performs actions based on the displayed feedback and sends new action data and emotional state results to the server. Input includes receiving specific instructions, including feedback messages displayed on the device, and performing actions according to those instructions. Output includes returning the results of the performed actions and the current emotional state as data to the server.

[0364] Step 6:

[0365] The server generates a detailed report based on the final operation results and accumulated sentiment analysis data. It receives accumulated data from each step, analysis results, and inconsistency information as input, and integrates them to create a report that includes an operation summary and improvement suggestions. The final output is a report that can be submitted to administrators or used for internal optimization.

[0366] (Application Example 2)

[0367] 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."

[0368] In factory and other work environments, providing feedback based on standard operating procedures and inconsistency detection without considering the emotional state of workers can lead to decreased efficiency and safety issues. Therefore, a system is needed that appropriately recognizes workers' emotions and provides feedback based on those emotions.

[0369] 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.

[0370] In this invention, the server includes, as a data collection means, means for recording each operation procedure and storing it in a database; as a preprocessing means, means for converting audio data into text and extracting information from image data; and as an emotion analysis means, means for identifying the user's emotional state using facial expressions and audio data and incorporating the results into the feedback. This enables the provision of appropriate feedback that takes the user's emotional state into consideration, thereby improving work efficiency and safety.

[0371] A "data collection means" is a component that provides the function of recording each operation procedure and storing it in a database.

[0372] A "preprocessing means" is a component that has the function of converting audio data into text and extracting information from image data.

[0373] A "data analysis tool" is a component that has the function of comparing and analyzing standard operating procedures and operation logs to detect inconsistencies.

[0374] A "feedback provision mechanism" is a component that has the function of notifying the user of detected inconsistencies and suggesting appropriate corrections.

[0375] An "emotion analysis tool" is a component that uses facial expression and voice data to identify the user's emotional state and incorporates the results into the feedback.

[0376] A "report generation means" is a component that has the function of recording the completion status of an operation and creating a report that includes suggestions for improvement.

[0377] The work support system based on this invention aims to improve work efficiency and safety in a factory environment. This system provides feedback that takes into account the emotional state of the worker through emotion analysis.

[0378] The server first uses data collection means to collect voice and facial expression data from workers and stores it in a database. Voice is acquired via a microphone, and facial expression data is captured by a camera. These data are then processed by preprocessing means, which transcribe the voice data into text and extract information from the image data, converting them into a format suitable for analysis.

[0379] For emotion analysis, open-source libraries such as "Librosa" and "OpenCV" are used. These software programs are used to extract emotional features from audio and analyze facial expressions from images in real time. This allows for the identification of the worker's emotional state, which is then stored in a database.

[0380] The data analysis system detects inconsistencies between standard operating procedures and operation logs, taking into account the results of sentiment analysis. The feedback system generates optimal instructions and corrections based on these inconsistencies and the worker's emotions, and presents them to the worker via a terminal. The presented information is customized to include softer tones or more technically detailed explanations as needed.

[0381] The user modifies their work based on the displayed feedback and sends the results from their terminal to the server. The server comprehensively evaluates the completion status of the operation and the user's emotional state, and generates a report that includes suggestions for improvement. This report is provided to the administrator and used to improve future operations.

[0382] For example, if an error is detected on the production line, the system senses the worker's stress and displays feedback on the terminal such as, "Don't worry, it's okay. Please double-check step B." This feedback helps the worker regain their composure and make the appropriate correction.

[0383] As an example of a prompt for the generative AI model, providing a format such as, "Analyze the emotional state of this data and generate relaxing feedback if 'stress' or 'confusion' is detected," allows for a more human-like response.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The server collects voice and facial expression data from the user via microphone and camera and stores it in a database. The input consists of real-time captured voice and image data, which are also stored in the database. Initial data is organized with a time stamp and session ID.

[0387] Step 2:

[0388] The server uses preprocessing to convert the collected audio data into text using "Librosa" and extract features from the image data using "OpenCV". The input is the audio and image data stored in step 1, and the output is the generated text data and digitized facial expression feature data.

[0389] Step 3:

[0390] The server identifies the user's emotional state based on data transcribed and digitized by emotion analysis tools. The input is the data generated in step 2, and the output is the user's emotional state (e.g., stress, joy). Here, a generative AI model is applied to analyze emotions based on specific prompt sentences.

[0391] Step 4:

[0392] The server uses data analysis tools to compare standard operating procedures (SOPs) and operation logs, taking emotional states into consideration, to detect inconsistencies. The input consists of the emotional analysis results from step 3 and the operation procedure data, and the output is a list of inconsistencies.

[0393] Step 5:

[0394] The device displays appropriate feedback to the user based on a list of inconsistencies and emotional state through a feedback provision mechanism. The input is the result of step 4, and the output is a feedback message displayed to the user. The message is customized using prompt sentences by a generating AI model.

[0395] Step 6:

[0396] The user reviews the feedback displayed on the device, performs the correction work according to the instructions, and sends the results of the work from the device to the server. The input is the feedback message and the work correction information, and the output is the updated operation data sent to the server.

[0397] Step 7:

[0398] The server uses a report generation mechanism to generate a detailed work report, including improvement suggestions, after all operations are completed. The input is the operation history and sentiment analysis results from step 6, and the output is a report provided to the administrator.

[0399] In this manner, the work support system operates efficiently in real time, providing optimal support that takes user emotions into consideration.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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".

[0416] As an embodiment of the present invention, we introduce an automated system for work review utilizing generation AI. This system consists of elements that have the roles of server, terminal, and user, and performs data collection, preprocessing, analysis, inconsistency detection, feedback provision, and report generation. The specific operation details are described below.

[0417] The server first records each operation procedure and stores it in a database. The collected data includes audio and image data, which are stored in an appropriate format. Next, the server performs preprocessing, converting the audio data to text and extracting specific information from the image data. This converts the data into a format that is easy to analyze. Subsequently, based on the converted data, the server performs data analysis and compares it with standard operating procedures and operation logs to detect inconsistencies.

[0418] Any detected inconsistencies or errors are notified to the user in real time via the device. The device uses pop-up messages and alerts to indicate the detection and suggest necessary corrections. The user can then correct their work based on this information.

[0419] The user reviews the information presented by the system and performs the correct actions according to the instructions. The user's actions are then sent back to the server for analysis.

[0420] Finally, the server records the completion status of all operations and generates a report based on that data. This report includes suggestions for improving the work and is provided to the administrator. This ensures continuous improvement in future work efficiency and quality.

[0421] For example, when configuring network equipment, suppose the server analyzes the user's work log and detects an incorrect IP address setting. In this case, the terminal displays a warning to the user and presents the correct IP address to be corrected. As a result, the user can quickly correct the error and maintain the quality of their work.

[0422] Based on the above, the present invention is expected to achieve automation and efficiency in work and contribute to the reduction of human error.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] The server receives user instructions and input operations in real time and records them as logs in the database. Audio and image data are also collected during this process.

[0426] Step 2:

[0427] The server converts recorded audio data into text using speech recognition technology, and extracts necessary textual information from image data using image recognition technology. This organizes the data into a unified format.

[0428] Step 3:

[0429] The server analyzes the converted text data and checks for inconsistencies with the operation log, referring to pre-configured standard operating procedures (SOPs). Natural language processing techniques are used for this analysis.

[0430] Step 4:

[0431] When the server detects inconsistencies or errors, it logs them and generates feedback data. This data specifies which step the error occurred in and what kind of error it was.

[0432] Step 5:

[0433] The terminal displays feedback data received from the server to the user in real time. This is done using pop-up messages and alerts, which show details of detected errors and recommended corrective steps.

[0434] Step 6:

[0435] The user reviews the feedback displayed on the device and takes the necessary actions based on the instructions. The corrected actions are then sent back to the server for re-analysis.

[0436] Step 7:

[0437] Once all tasks are completed, the server compiles the results of each task and generates an overall work report and improvement suggestions. This report is provided to the administrator and used for future business improvements.

[0438] (Example 1)

[0439] 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."

[0440] In the field of information processing, there is a need to efficiently record and analyze operating procedures, thereby enabling the early detection of inconsistencies in manual work and human errors. However, existing systems face challenges in providing rapid responses due to the cost and time required for data preprocessing, analysis, and feedback.

[0441] 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.

[0442] In this invention, the server includes information processing means for monitoring operating procedures and storing data, information conversion means for converting audio information into written form and extracting specific information from visual information, and information analysis means for comparing standard procedures and work records using a generation AI model and detecting differences. This enables efficient monitoring and analysis of operating procedures and rapid detection of inconsistencies.

[0443] "Information processing means" refers to a method for monitoring operating procedures and efficiently collecting and storing data.

[0444] An "information conversion means" is a method for converting audio information into text format and for extracting necessary data from image information.

[0445] "Information analysis means" refers to a method for detecting inconsistencies by comparing standard work procedures with operation logs using a generative AI model.

[0446] A "feedback device" is a device that notifies the user of detected inconsistencies and suggests corrective actions.

[0447] A "report generation device" is a device that records the completion status of work and creates a report that includes suggestions for work improvement.

[0448] An "anomaly detection method" is a method for verifying the range of input information and identifying errors in its order.

[0449] "Information provision means" refers to methods for presenting notification messages and warnings to users in real time.

[0450] This invention consists of an automated system for task review utilizing generative AI. The system is operated by elements that have the roles of server, terminal, and user.

[0451] The server first monitors the operating procedures and collects and stores data, including audio and image data. Audio data is converted to text using speech recognition software, such as a general speech-to-text service. Image recognition technology is used to extract information from image data.

[0452] The server then uses a generative AI model to analyze the preprocessed data. Specifically, it compares standard operating procedures with user operation logs to detect inconsistencies between the data. The use of the generative AI model enables faster and more accurate analysis than before.

[0453] Any detected inconsistencies are notified to the user in real time via the device. The device provides feedback using pop-up messages and alerts, and suggests corrective actions to the user. This allows the user to quickly adjust their work.

[0454] The user performs the correct actions based on the feedback provided. The corrected actions are sent back to the server for further analysis. Finally, the server generates a report based on the completion status of the work and provides it to the administrator. This report also includes suggestions for improving the work, which is expected to improve the efficiency and quality of operations.

[0455] For example, if an incorrect IP address is set during network configuration, the terminal will display a warning to the user and provide the correct IP address for correction. As a result, the user can immediately correct the error and maintain the quality of their work.

[0456] An example of a prompt message might be, "Use a generative AI model to identify inconsistencies in the work log and generate suggestions."

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The server monitors user actions and collects audio and image data. This data is stored in a database for subsequent processing. Specifically, it formats and stores raw data received from sensors and cameras. The input consists of audio and images based on user procedures, while the output is the database containing this data.

[0460] Step 2:

[0461] The server preprocesses the stored data. Audio data is converted to text using speech recognition software, and necessary information is extracted from image data using image processing software. This facilitates data analysis. The input is formatted audio and image data, and the output is analyzable text and extracted information.

[0462] Step 3:

[0463] The server performs analysis using a generative AI model. Based on the previously processed text and extracted information, it compares standard operating procedures and operation logs to detect inconsistencies between the data. Specifically, the analysis identifies patterns of inconsistencies and reveals anomalies. The input is the pre-processed text and extracted information, and the output is the detected inconsistencies.

[0464] Step 4:

[0465] The terminal notifies the user of any detected inconsistencies. It provides feedback and suggests corrective actions using pop-up messages and alerts. Specifically, it displays warnings on the screen to help the user understand the necessary corrections. The input is the inconsistent data from the server, and the output is the instructional information presented to the user.

[0466] Step 5:

[0467] The user modifies their work based on the feedback provided. They perform the correct operations and send the results back to the server. Specifically, they follow the on-screen guidance to implement the proposed modifications. The input is the instructions from the terminal, and the output is the result of the modified operation.

[0468] Step 6:

[0469] The server ultimately analyzes the corrected operation logs and generates a report. This report includes improvement suggestions and information on streamlining operations. Specifically, it evaluates the results based on comprehensive data and clearly identifies areas for improvement. The input is the corrected operation logs, and the output is the report.

[0470] (Application Example 1)

[0471] 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."

[0472] Modern manufacturing facilities and factories face challenges such as inconsistencies in work procedures and human error, which reduce production efficiency and increase the likelihood of quality defects. Furthermore, because it is difficult for workers to make quick, real-time corrections, there is a need for efficient solutions to address these issues.

[0473] 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.

[0474] In this invention, the server includes means for collecting data, means for preprocessing data, and means for performing data analysis. This makes it possible to immediately detect inconsistencies in work procedures and provide corrective suggestions to the worker in real time.

[0475] "Data collection means" refers to devices or methods for recording work procedures and storing them in a database.

[0476] The "preprocessing means" is a function that converts collected audio information into text and extracts necessary information from image information.

[0477] "Data analysis means" refers to the process and techniques of comparing standard operating procedures with actual operation records to detect inconsistencies.

[0478] A "feedback provision mechanism" is a mechanism that notifies the operator of detected inconsistencies and proposes corrective measures.

[0479] The "report generation means" is a function that records the completion status of an operation and creates a report that includes suggestions for improvement.

[0480] "A means of displaying feedback in real time on an information terminal carried by the operator" refers to a technology that instantly displays feedback information on a portable electronic device.

[0481] The system implementing this invention has a configuration that facilitates data collection, preprocessing, data analysis, real-time feedback, and report generation. It consists of three components: a server, a terminal, and a user.

[0482] The server first collects data. Using speech recognition software (e.g., Google Speech-to-Text) and an image analysis platform (e.g., OpenCV), it records audio and image data related to factory work procedures and stores them in a database. Next, as a preprocessing step, the audio data is converted to text format, and work-related information is extracted from the image data. This data is analyzed using a custom generative AI model with PyTorch to detect inconsistencies when compared to standard work procedures.

[0483] The terminal plays a role in notifying the user of detected inconsistencies in real time. By displaying feedback as pop-up messages or warning alerts on portable information terminals such as smartphones and tablets, it provides an environment where users can take immediate action.

[0484] Users modify their work based on the feedback they receive and feed their results back to the server, continuously optimizing the process. Finally, the server records the work completion status and generates a report including improvement suggestions. This report is then used by administrators to improve future work efficiency and quality.

[0485] As a concrete example, consider a factory where a robot verifies in real time whether it has correctly installed parts in their designated positions. The server analyzes the robot's operation logs and immediately notifies the user via a terminal if it detects incorrect operation or misalignment. Based on this information, the user can quickly take corrective action.

[0486] An example of a prompt for the generated AI model is, "Detect inconsistencies in the factory line operation logs and generate real-time alerts." Using this prompt, the server can automatically perform analysis and improve the accuracy of inconsistency detection.

[0487] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0488] Step 1:

[0489] The server receives audio and image data in real time from sensors and cameras within the factory. The server stores this data directly in a database. The input is audio and image data, and the output is the raw data stored in the database.

[0490] Step 2:

[0491] The server uses speech recognition software to convert audio data into text data and image analysis software to extract features relevant to the task from the image data. The input is the audio and image data obtained in step 1, and the output is the transcribed audio data and the extracted image feature data.

[0492] Step 3:

[0493] The server uses the transformed and extracted data to compare it with standard operating procedure data using a generative AI model. If inconsistencies are detected, the details are recorded. The input consists of text data and image feature data, and the output is whether or not inconsistencies exist and their details.

[0494] Step 4:

[0495] The server sends detected inconsistencies to the terminal, which then notifies the user in real time as a pop-up message. The input is the detailed information about the inconsistency, and the output is the notification message displayed on the terminal.

[0496] Step 5:

[0497] The user receives a notification from the device and modifies the work according to the suggested corrections. The input is the suggested corrections from the device, and the output is the modified work procedure.

[0498] Step 6:

[0499] The server recollects the data after the work has been corrected, reanalyzes it, and records the completion status of the work. Finally, it generates a report including improvement suggestions and provides it to the administrator. The input is the corrected work data, and the output is a report including the completion status record and improvement suggestions.

[0500] 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.

[0501] As an embodiment of the present invention, we introduce an automated work review system incorporating an emotion engine that recognizes user emotions. This system has functions for emotion recognition in addition to data collection, preprocessing, data analysis, inconsistency detection, feedback provision, and report generation. The specific operation of each component is described below.

[0502] The server receives user input, including voice and facial expression data, in real time and stores it in a database. The emotion engine uses the voice and image data to analyze the user's emotional state and identify states such as "stress," "confusion," and "excitement." This information is also stored in the database.

[0503] Next, the server performs preprocessing, converting the audio data into text and extracting necessary information from the image data. It also attaches the analysis results from the emotion engine as supplementary information to the data.

[0504] Based on the converted data, the server performs data analysis and detects inconsistencies by referring to standard operating procedures (SOPs) and operation logs. Considering the sentiment analysis results, it determines the optimal feedback content according to the work situation.

[0505] Feedback on detected inconsistencies and errors is communicated to the user via the device. The feedback is delivered in a tone and wording that takes into account the user's emotional state, and is provided in an appropriate format, ranging from gentle suggestions to detailed technical instructions.

[0506] The user reviews the feedback displayed on the device and performs the necessary actions according to the instructions. The data and user sentiment after the action are sent back to the server for further analysis if necessary.

[0507] Once all operations are complete, the server compiles the results and generates a detailed work report, including sentiment analysis. This report includes improvement suggestions and is provided to administrators for use in future business improvements.

[0508] For example, if the system detects a user's unstable emotional state, the terminal will gently suggest how to correct the error. This approach allows users to proceed with the correction process without feeling stressed. This is expected to further improve work efficiency and quality.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] When a user begins an operation, the server receives input data in real time and records each operation step in the database. At the same time, audio and image data are also collected.

[0512] Step 2:

[0513] The server converts the recorded audio data into text using a speech recognition engine and extracts necessary textual information from the image data. Simultaneously, it uses an emotion engine to analyze the user's emotional state from their voice tone and facial expressions. The results of this analysis are then added to the data.

[0514] Step 3:

[0515] The server compares the converted operation data with the standard operating procedure (SOP), performs data analysis, and detects inconsistencies. It also takes sentiment analysis results into consideration to determine the content of the feedback appropriate to the user's emotional state.

[0516] Step 4:

[0517] The server generates feedback based on the inconsistencies and sentiment analysis results. This feedback is crafted in a tone that matches the user's emotional state and sent to the device.

[0518] Step 5:

[0519] The device displays feedback received from the server to the user. Messages that take the user's emotional state into consideration are output as pop-ups or alerts, and errors and suggested solutions are presented.

[0520] Step 6:

[0521] The user reviews the information displayed on the device and performs the necessary actions according to the instructions. After any modifications or additions are made, the data is sent back to the server.

[0522] Step 7:

[0523] Once all tasks are complete, the server compiles the results and generates a detailed report, including sentiment analysis findings. This report, which includes suggestions for improvement, is created for administrators.

[0524] (Example 2)

[0525] 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."

[0526] In many modern work environments, there is a demand for increased user efficiency and accuracy. However, manual data recording and analysis, as well as providing human-based feedback, are time-consuming and labor-intensive. Furthermore, there is a lack of means to provide timely and appropriate feedback while reducing the stress and confusion users experience during their work. In this context, there is a growing need for systems that support users in performing tasks efficiently and accurately.

[0527] 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.

[0528] In this invention, the server includes means for recording user input and storing data including audio and video data; means for pre-processing audio information into text information and extracting relevant information from video data; means for analyzing data consistency by comparing work instructions and operation history and detecting inconsistencies; means for providing feedback to the user in appropriate words and tone regarding data inconsistencies based on sentiment analysis results; and means for recording the progress of operations, generating a detailed report including sentiment analysis, and including suggestions. This enables the provision of immediate feedback that takes into account the user's emotional state, thereby simultaneously improving work efficiency and maintaining accuracy.

[0529] "User input" refers to information such as audio and video data that users provide to the system.

[0530] "Converting audio information to text information" is the process of converting data given in audio format into text, and is a means of facilitating data analysis by machines.

[0531] "Extracting relevant information from video data" refers to the process of identifying important features and patterns within video data and extracting the data necessary for analysis.

[0532] A "work instruction sheet" is a document that describes the standard procedures and instructions for performing a specific task.

[0533] "Operation history" refers to a record of past actions and inputs a user has made while interacting with the system.

[0534] "Analyzing data integrity and detecting inconsistencies" is the process of evaluating the consistency between data and identifying behaviors or information that deviate from standard procedures.

[0535] "Emotional analysis results" refer to the results of an analysis of the user's emotional state, with corresponding data points provided as numerical values ​​or categories.

[0536] Providing feedback in the right words and tone is the process of conveying information in a way that is optimized for the user's emotional state. This helps reduce stress and aids understanding.

[0537] "Generating a detailed report" means creating a document that summarizes the overall progress and results of the work, and includes feedback and suggestions that will be useful for future reference.

[0538] This system is designed to support user operational efficiency and accuracy, and its embodiments are shown below.

[0539] The server records user input in real time during each operation step. This requires a terminal equipped with a microphone and camera to process voice and video input. The server uses speech recognition software to convert voice data into text. For example, a speech recognition API or machine learning model could be applied as a common speech recognition technique. Image analysis software is used to extract relevant information from video data and estimate the user's emotional state. For example, a common image analysis API could be used as the image recognition technique.

[0540] The terminal is responsible for providing feedback from the server to the user. The server analyzes the user's emotional state, and based on the results, the terminal displays feedback in an appropriate tone and language. This ensures that the information the user receives is tailored to their individual situation, improving work efficiency and accuracy. Feedback is displayed as pop-up messages or alerts as needed.

[0541] The user performs the instructed operations based on feedback received through the terminal. These operations are carried out according to standard operating procedures, ensuring consistency and efficient progress. The progress of the operations is sent back to the server for re-analysis as needed.

[0542] Furthermore, once the operation is complete, the server generates a detailed work report. This report includes the overall status of the work, any inconsistencies detected, the results of the sentiment analysis, and suggestions for improvement based on those results. This makes it possible to provide specific feedback that can be used to improve future operations.

[0543] As a concrete example, the system issues prompts to the generating AI model such as, "Analyze the user's emotions and provide appropriate feedback. If the user is feeling anxious, use specific and gentle language." This design allows the system to function interactively in a way that best suits the user's emotional state.

[0544] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0545] Step 1:

[0546] The server receives audio and video data from the user in real time through the terminal and stores it in a database. It receives audio data acquired using a microphone and video data acquired using a camera as input. This data is stored in the database for use in a later analysis step. The resulting output is the audio and video data registered in the database.

[0547] Step 2:

[0548] The server converts audio data into text and extracts emotional features from video data. It uses audio data stored in a database as audio input and transcribes it using a speech recognition algorithm. In parallel, it analyzes facial expressions from video data using image analysis technology and extracts the user's emotional state numerically. This results in output consisting of transcribed audio data and parameters indicating the emotional state.

[0549] Step 3:

[0550] The server detects inconsistencies by comparing standard operating procedures (SOPs) with operation history based on transcribed voice data and emotion parameters. It uses recent user operation history and transcribed voice data as input. This data is compared against the SOPs to determine if there are any discrepancies or deviations. The output provides a list of detected inconsistencies and supplementary information explaining their causes.

[0551] Step 4:

[0552] The terminal uses inconsistency information and sentiment analysis results obtained from the server to display feedback to the user in an appropriate tone. It receives inconsistency information and sentiment parameters as input and generates a feedback message. The generated message uses calm and polite language, especially when the user is showing signs of stress or confusion. The output is the feedback message displayed on the terminal.

[0553] Step 5:

[0554] The user performs actions based on the displayed feedback and sends new action data and emotional state results to the server. Input includes receiving specific instructions, including feedback messages displayed on the device, and performing actions according to those instructions. Output includes returning the results of the performed actions and the current emotional state as data to the server.

[0555] Step 6:

[0556] The server generates a detailed report based on the final operation results and accumulated sentiment analysis data. It receives accumulated data from each step, analysis results, and inconsistency information as input, and integrates them to create a report that includes an operation summary and improvement suggestions. The final output is a report that can be submitted to administrators or used for internal optimization.

[0557] (Application Example 2)

[0558] 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."

[0559] In factory and other work environments, providing feedback based on standard operating procedures and inconsistency detection without considering the emotional state of workers can lead to decreased efficiency and safety issues. Therefore, a system is needed that appropriately recognizes workers' emotions and provides feedback based on those emotions.

[0560] 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.

[0561] In this invention, the server includes, as a data collection means, means for recording each operation procedure and storing it in a database; as a preprocessing means, means for converting audio data into text and extracting information from image data; and as an emotion analysis means, means for identifying the user's emotional state using facial expressions and audio data and incorporating the results into the feedback. This enables the provision of appropriate feedback that takes the user's emotional state into consideration, thereby improving work efficiency and safety.

[0562] A "data collection means" is a component that provides the function of recording each operation procedure and storing it in a database.

[0563] A "preprocessing means" is a component that has the function of converting audio data into text and extracting information from image data.

[0564] A "data analysis tool" is a component that has the function of comparing and analyzing standard operating procedures and operation logs to detect inconsistencies.

[0565] A "feedback provision mechanism" is a component that has the function of notifying the user of detected inconsistencies and suggesting appropriate corrections.

[0566] An "emotion analysis tool" is a component that uses facial expression and voice data to identify the user's emotional state and incorporates the results into the feedback.

[0567] A "report generation means" is a component that has the function of recording the completion status of an operation and creating a report that includes suggestions for improvement.

[0568] The work support system based on this invention aims to improve work efficiency and safety in a factory environment. This system provides feedback that takes into account the emotional state of the worker through emotion analysis.

[0569] The server first uses data collection means to collect voice and facial expression data from workers and stores it in a database. Voice is acquired via a microphone, and facial expression data is captured by a camera. These data are then processed by preprocessing means, which transcribe the voice data into text and extract information from the image data, converting them into a format suitable for analysis.

[0570] For emotion analysis, open-source libraries such as "Librosa" and "OpenCV" are used. These software programs are used to extract emotional features from audio and analyze facial expressions from images in real time. This allows for the identification of the worker's emotional state, which is then stored in a database.

[0571] The data analysis system detects inconsistencies between standard operating procedures and operation logs, taking into account the results of sentiment analysis. The feedback system generates optimal instructions and corrections based on these inconsistencies and the worker's emotions, and presents them to the worker via a terminal. The presented information is customized to include softer tones or more technically detailed explanations as needed.

[0572] The user modifies their work based on the displayed feedback and sends the results from their terminal to the server. The server comprehensively evaluates the completion status of the operation and the user's emotional state, and generates a report that includes suggestions for improvement. This report is provided to the administrator and used to improve future operations.

[0573] For example, if an error is detected on the production line, the system senses the worker's stress and displays feedback on the terminal such as, "Don't worry, it's okay. Please double-check step B." This feedback helps the worker regain their composure and make the appropriate correction.

[0574] As an example of a prompt for the generative AI model, providing a format such as, "Analyze the emotional state of this data and generate relaxing feedback if 'stress' or 'confusion' is detected," allows for a more human-like response.

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] The server collects voice and facial expression data from the user via microphone and camera and stores it in a database. The input consists of real-time captured voice and image data, which are also stored in the database. Initial data is organized with a time stamp and session ID.

[0578] Step 2:

[0579] The server uses preprocessing to convert the collected audio data into text using "Librosa" and extract features from the image data using "OpenCV". The input is the audio and image data stored in step 1, and the output is the generated text data and digitized facial expression feature data.

[0580] Step 3:

[0581] The server identifies the user's emotional state based on data transcribed and digitized by emotion analysis tools. The input is the data generated in step 2, and the output is the user's emotional state (e.g., stress, joy). Here, a generative AI model is applied to analyze emotions based on specific prompt sentences.

[0582] Step 4:

[0583] The server uses data analysis tools to compare standard operating procedures (SOPs) and operation logs, taking emotional states into consideration, to detect inconsistencies. The input consists of the emotional analysis results from step 3 and the operation procedure data, and the output is a list of inconsistencies.

[0584] Step 5:

[0585] The device displays appropriate feedback to the user based on a list of inconsistencies and emotional state through a feedback provision mechanism. The input is the result of step 4, and the output is a feedback message displayed to the user. The message is customized using prompt sentences by a generating AI model.

[0586] Step 6:

[0587] The user reviews the feedback displayed on the device, performs the correction work according to the instructions, and sends the results of the work from the device to the server. The input is the feedback message and the work correction information, and the output is the updated operation data sent to the server.

[0588] Step 7:

[0589] The server uses a report generation mechanism to generate a detailed work report, including improvement suggestions, after all operations are completed. The input is the operation history and sentiment analysis results from step 6, and the output is a report provided to the administrator.

[0590] In this manner, the work support system operates efficiently in real time, providing optimal support that takes user emotions into consideration.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] [Fourth Embodiment]

[0595] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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".

[0608] As an embodiment of the present invention, we introduce an automated system for work review utilizing generation AI. This system consists of elements that have the roles of server, terminal, and user, and performs data collection, preprocessing, analysis, inconsistency detection, feedback provision, and report generation. The specific operation details are described below.

[0609] The server first records each operation procedure and stores it in a database. The collected data includes audio and image data, which are stored in an appropriate format. Next, the server performs preprocessing, converting the audio data to text and extracting specific information from the image data. This converts the data into a format that is easy to analyze. Subsequently, based on the converted data, the server performs data analysis and compares it with standard operating procedures and operation logs to detect inconsistencies.

[0610] Any detected inconsistencies or errors are notified to the user in real time via the device. The device uses pop-up messages and alerts to indicate the detection and suggest necessary corrections. The user can then correct their work based on this information.

[0611] The user reviews the information presented by the system and performs the correct actions according to the instructions. The user's actions are then sent back to the server for analysis.

[0612] Finally, the server records the completion status of all operations and generates a report based on that data. This report includes suggestions for improving the work and is provided to the administrator. This ensures continuous improvement in future work efficiency and quality.

[0613] For example, when configuring network equipment, suppose the server analyzes the user's work log and detects an incorrect IP address setting. In this case, the terminal displays a warning to the user and presents the correct IP address to be corrected. As a result, the user can quickly correct the error and maintain the quality of their work.

[0614] Based on the above, the present invention is expected to achieve automation and efficiency in work and contribute to the reduction of human error.

[0615] The following describes the processing flow.

[0616] Step 1:

[0617] The server receives user instructions and input operations in real time and records them as logs in the database. Audio and image data are also collected during this process.

[0618] Step 2:

[0619] The server converts recorded audio data into text using speech recognition technology, and extracts necessary textual information from image data using image recognition technology. This organizes the data into a unified format.

[0620] Step 3:

[0621] The server analyzes the converted text data and checks for inconsistencies with the operation log, referring to pre-configured standard operating procedures (SOPs). Natural language processing techniques are used for this analysis.

[0622] Step 4:

[0623] When the server detects inconsistencies or errors, it logs them and generates feedback data. This data specifies which step the error occurred in and what kind of error it was.

[0624] Step 5:

[0625] The terminal displays feedback data received from the server to the user in real time. This is done using pop-up messages and alerts, which show details of detected errors and recommended corrective steps.

[0626] Step 6:

[0627] The user reviews the feedback displayed on the device and takes the necessary actions based on the instructions. The corrected actions are then sent back to the server for re-analysis.

[0628] Step 7:

[0629] Once all tasks are completed, the server compiles the results of each task and generates an overall work report and improvement suggestions. This report is provided to the administrator and used for future business improvements.

[0630] (Example 1)

[0631] 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".

[0632] In the field of information processing, there is a need to efficiently record and analyze operating procedures, thereby enabling the early detection of inconsistencies in manual work and human errors. However, existing systems face challenges in providing rapid responses due to the cost and time required for data preprocessing, analysis, and feedback.

[0633] 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.

[0634] In this invention, the server includes information processing means for monitoring operating procedures and storing data, information conversion means for converting audio information into written form and extracting specific information from visual information, and information analysis means for comparing standard procedures and work records using a generation AI model and detecting differences. This enables efficient monitoring and analysis of operating procedures and rapid detection of inconsistencies.

[0635] "Information processing means" refers to a method for monitoring operating procedures and efficiently collecting and storing data.

[0636] An "information conversion means" is a method for converting audio information into text format and for extracting necessary data from image information.

[0637] "Information analysis means" refers to a method for detecting inconsistencies by comparing standard work procedures with operation logs using a generative AI model.

[0638] A "feedback device" is a device that notifies the user of detected inconsistencies and suggests corrective actions.

[0639] A "report generation device" is a device that records the completion status of work and creates a report that includes suggestions for work improvement.

[0640] An "anomaly detection method" is a method for verifying the range of input information and identifying errors in its order.

[0641] "Information provision means" refers to methods for presenting notification messages and warnings to users in real time.

[0642] This invention consists of an automated system for task review utilizing generative AI. The system is operated by elements that have the roles of server, terminal, and user.

[0643] The server first monitors the operating procedures and collects and stores data, including audio and image data. Audio data is converted to text using speech recognition software, such as a general speech-to-text service. Image recognition technology is used to extract information from image data.

[0644] The server then uses a generative AI model to analyze the preprocessed data. Specifically, it compares standard operating procedures with user operation logs to detect inconsistencies between the data. The use of the generative AI model enables faster and more accurate analysis than before.

[0645] Any detected inconsistencies are notified to the user in real time via the device. The device provides feedback using pop-up messages and alerts, and suggests corrective actions to the user. This allows the user to quickly adjust their work.

[0646] The user performs the correct actions based on the feedback provided. The corrected actions are sent back to the server for further analysis. Finally, the server generates a report based on the completion status of the work and provides it to the administrator. This report also includes suggestions for improving the work, which is expected to improve the efficiency and quality of operations.

[0647] For example, if an incorrect IP address is set during network configuration, the terminal will display a warning to the user and provide the correct IP address for correction. As a result, the user can immediately correct the error and maintain the quality of their work.

[0648] An example of a prompt message might be, "Use a generative AI model to identify inconsistencies in the work log and generate suggestions."

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] The server monitors user actions and collects audio and image data. This data is stored in a database for subsequent processing. Specifically, it formats and stores raw data received from sensors and cameras. The input consists of audio and images based on user procedures, while the output is the database containing this data.

[0652] Step 2:

[0653] The server preprocesses the stored data. Audio data is converted to text using speech recognition software, and necessary information is extracted from image data using image processing software. This facilitates data analysis. The input is formatted audio and image data, and the output is analyzable text and extracted information.

[0654] Step 3:

[0655] The server performs analysis using a generative AI model. Based on the previously processed text and extracted information, it compares standard operating procedures and operation logs to detect inconsistencies between the data. Specifically, the analysis identifies patterns of inconsistencies and reveals anomalies. The input is the pre-processed text and extracted information, and the output is the detected inconsistencies.

[0656] Step 4:

[0657] The terminal notifies the user of any detected inconsistencies. It provides feedback and suggests corrective actions using pop-up messages and alerts. Specifically, it displays warnings on the screen to help the user understand the necessary corrections. The input is the inconsistent data from the server, and the output is the instructional information presented to the user.

[0658] Step 5:

[0659] The user modifies their work based on the feedback provided. They perform the correct operations and send the results back to the server. Specifically, they follow the on-screen guidance to implement the proposed modifications. The input is the instructions from the terminal, and the output is the result of the modified operation.

[0660] Step 6:

[0661] The server ultimately analyzes the corrected operation logs and generates a report. This report includes improvement suggestions and information on streamlining operations. Specifically, it evaluates the results based on comprehensive data and clearly identifies areas for improvement. The input is the corrected operation logs, and the output is the report.

[0662] (Application Example 1)

[0663] 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".

[0664] Modern manufacturing facilities and factories face challenges such as inconsistencies in work procedures and human error, which reduce production efficiency and increase the likelihood of quality defects. Furthermore, because it is difficult for workers to make quick, real-time corrections, there is a need for efficient solutions to address these issues.

[0665] 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.

[0666] In this invention, the server includes means for collecting data, means for preprocessing data, and means for performing data analysis. This makes it possible to immediately detect inconsistencies in work procedures and provide corrective suggestions to the worker in real time.

[0667] "Data collection means" refers to devices or methods for recording work procedures and storing them in a database.

[0668] The "preprocessing means" is a function that converts collected audio information into text and extracts necessary information from image information.

[0669] "Data analysis means" refers to the process and techniques of comparing standard operating procedures with actual operation records to detect inconsistencies.

[0670] A "feedback provision mechanism" is a mechanism that notifies the operator of detected inconsistencies and proposes corrective measures.

[0671] The "report generation means" is a function that records the completion status of an operation and creates a report that includes suggestions for improvement.

[0672] "A means of displaying feedback in real time on an information terminal carried by the operator" refers to a technology that instantly displays feedback information on a portable electronic device.

[0673] The system implementing this invention has a configuration that facilitates data collection, preprocessing, data analysis, real-time feedback, and report generation. It consists of three components: a server, a terminal, and a user.

[0674] The server first collects data. Using speech recognition software (e.g., Google Speech-to-Text) and an image analysis platform (e.g., OpenCV), it records audio and image data related to factory work procedures and stores them in a database. Next, as a preprocessing step, the audio data is converted to text format, and work-related information is extracted from the image data. This data is analyzed using a custom generative AI model with PyTorch to detect inconsistencies when compared to standard work procedures.

[0675] The terminal plays a role in notifying the user of detected inconsistencies in real time. By displaying feedback as pop-up messages or warning alerts on portable information terminals such as smartphones and tablets, it provides an environment where users can take immediate action.

[0676] Users modify their work based on the feedback they receive and feed their results back to the server, continuously optimizing the process. Finally, the server records the work completion status and generates a report including improvement suggestions. This report is then used by administrators to improve future work efficiency and quality.

[0677] As a concrete example, consider a factory where a robot verifies in real time whether it has correctly installed parts in their designated positions. The server analyzes the robot's operation logs and immediately notifies the user via a terminal if it detects incorrect operation or misalignment. Based on this information, the user can quickly take corrective action.

[0678] An example of a prompt for the generated AI model is, "Detect inconsistencies in the factory line operation logs and generate real-time alerts." Using this prompt, the server can automatically perform analysis and improve the accuracy of inconsistency detection.

[0679] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0680] Step 1:

[0681] The server receives audio and image data in real time from sensors and cameras within the factory. The server stores this data directly in a database. The input is audio and image data, and the output is the raw data stored in the database.

[0682] Step 2:

[0683] The server uses speech recognition software to convert audio data into text data and image analysis software to extract features relevant to the task from the image data. The input is the audio and image data obtained in step 1, and the output is the transcribed audio data and the extracted image feature data.

[0684] Step 3:

[0685] The server uses the transformed and extracted data to compare it with standard operating procedure data using a generative AI model. If inconsistencies are detected, the details are recorded. The input consists of text data and image feature data, and the output is whether or not inconsistencies exist and their details.

[0686] Step 4:

[0687] The server sends detected inconsistencies to the terminal, which then notifies the user in real time as a pop-up message. The input is the detailed information about the inconsistency, and the output is the notification message displayed on the terminal.

[0688] Step 5:

[0689] The user receives a notification from the device and modifies the work according to the suggested corrections. The input is the suggested corrections from the device, and the output is the modified work procedure.

[0690] Step 6:

[0691] The server recollects the data after the work has been corrected, reanalyzes it, and records the completion status of the work. Finally, it generates a report including improvement suggestions and provides it to the administrator. The input is the corrected work data, and the output is a report including the completion status record and improvement suggestions.

[0692] 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.

[0693] As an embodiment of the present invention, we introduce an automated work review system incorporating an emotion engine that recognizes user emotions. This system has functions for emotion recognition in addition to data collection, preprocessing, data analysis, inconsistency detection, feedback provision, and report generation. The specific operation of each component is described below.

[0694] The server receives user input, including voice and facial expression data, in real time and stores it in a database. The emotion engine uses the voice and image data to analyze the user's emotional state and identify states such as "stress," "confusion," and "excitement." This information is also stored in the database.

[0695] Next, the server performs preprocessing, converting the audio data into text and extracting necessary information from the image data. It also attaches the analysis results from the emotion engine as supplementary information to the data.

[0696] Based on the converted data, the server performs data analysis and detects inconsistencies by referring to standard operating procedures (SOPs) and operation logs. Considering the sentiment analysis results, it determines the optimal feedback content according to the work situation.

[0697] Feedback on detected inconsistencies and errors is communicated to the user via the device. The feedback is delivered in a tone and wording that takes into account the user's emotional state, and is provided in an appropriate format, ranging from gentle suggestions to detailed technical instructions.

[0698] The user reviews the feedback displayed on the device and performs the necessary actions according to the instructions. The data and user sentiment after the action are sent back to the server for further analysis if necessary.

[0699] Once all operations are complete, the server compiles the results and generates a detailed work report, including sentiment analysis. This report includes improvement suggestions and is provided to administrators for use in future business improvements.

[0700] For example, if the system detects a user's unstable emotional state, the terminal will gently suggest how to correct the error. This approach allows users to proceed with the correction process without feeling stressed. This is expected to further improve work efficiency and quality.

[0701] The following describes the processing flow.

[0702] Step 1:

[0703] When a user begins an operation, the server receives input data in real time and records each operation step in the database. At the same time, audio and image data are also collected.

[0704] Step 2:

[0705] The server converts the recorded audio data into text using a speech recognition engine and extracts necessary textual information from the image data. Simultaneously, it uses an emotion engine to analyze the user's emotional state from their voice tone and facial expressions. The results of this analysis are then added to the data.

[0706] Step 3:

[0707] The server compares the converted operation data with the standard operating procedure (SOP), performs data analysis, and detects inconsistencies. It also takes sentiment analysis results into consideration to determine the content of the feedback appropriate to the user's emotional state.

[0708] Step 4:

[0709] The server generates feedback based on the inconsistencies and sentiment analysis results. This feedback is crafted in a tone that matches the user's emotional state and sent to the device.

[0710] Step 5:

[0711] The device displays feedback received from the server to the user. Messages that take the user's emotional state into consideration are output as pop-ups or alerts, and errors and suggested solutions are presented.

[0712] Step 6:

[0713] The user reviews the information displayed on the device and performs the necessary actions according to the instructions. After any modifications or additions are made, the data is sent back to the server.

[0714] Step 7:

[0715] Once all tasks are complete, the server compiles the results and generates a detailed report, including sentiment analysis findings. This report, which includes suggestions for improvement, is created for administrators.

[0716] (Example 2)

[0717] 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".

[0718] In many modern work environments, there is a demand for increased user efficiency and accuracy. However, manual data recording and analysis, as well as providing human-based feedback, are time-consuming and labor-intensive. Furthermore, there is a lack of means to provide timely and appropriate feedback while reducing the stress and confusion users experience during their work. In this context, there is a growing need for systems that support users in performing tasks efficiently and accurately.

[0719] 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.

[0720] In this invention, the server includes means for recording user input and storing data including audio and video data; means for pre-processing audio information into text information and extracting relevant information from video data; means for analyzing data consistency by comparing work instructions and operation history and detecting inconsistencies; means for providing feedback to the user in appropriate words and tone regarding data inconsistencies based on sentiment analysis results; and means for recording the progress of operations, generating a detailed report including sentiment analysis, and including suggestions. This enables the provision of immediate feedback that takes into account the user's emotional state, thereby simultaneously improving work efficiency and maintaining accuracy.

[0721] "User input" refers to information such as audio and video data that users provide to the system.

[0722] "Converting audio information to text information" is the process of converting data given in audio format into text, and is a means of facilitating data analysis by machines.

[0723] "Extracting relevant information from video data" refers to the process of identifying important features and patterns within video data and extracting the data necessary for analysis.

[0724] A "work instruction sheet" is a document that describes the standard procedures and instructions for performing a specific task.

[0725] "Operation history" refers to a record of past actions and inputs a user has made while interacting with the system.

[0726] "Analyzing data integrity and detecting inconsistencies" is the process of evaluating the consistency between data and identifying behaviors or information that deviate from standard procedures.

[0727] "Emotional analysis results" refer to the results of an analysis of the user's emotional state, with corresponding data points provided as numerical values ​​or categories.

[0728] Providing feedback in the right words and tone is the process of conveying information in a way that is optimized for the user's emotional state. This helps reduce stress and aids understanding.

[0729] "Generating a detailed report" means creating a document that summarizes the overall progress and results of the work, and includes feedback and suggestions that will be useful for future reference.

[0730] This system is designed to support user operational efficiency and accuracy, and its embodiments are shown below.

[0731] The server records user input in real time during each operation step. This requires a terminal equipped with a microphone and camera to process voice and video input. The server uses speech recognition software to convert voice data into text. For example, a speech recognition API or machine learning model could be applied as a common speech recognition technique. Image analysis software is used to extract relevant information from video data and estimate the user's emotional state. For example, a common image analysis API could be used as the image recognition technique.

[0732] The terminal is responsible for providing feedback from the server to the user. The server analyzes the user's emotional state, and based on the results, the terminal displays feedback in an appropriate tone and language. This ensures that the information the user receives is tailored to their individual situation, improving work efficiency and accuracy. Feedback is displayed as pop-up messages or alerts as needed.

[0733] The user performs the instructed operations based on feedback received through the terminal. These operations are carried out according to standard operating procedures, ensuring consistency and efficient progress. The progress of the operations is sent back to the server for re-analysis as needed.

[0734] Furthermore, once the operation is complete, the server generates a detailed work report. This report includes the overall status of the work, any inconsistencies detected, the results of the sentiment analysis, and suggestions for improvement based on those results. This makes it possible to provide specific feedback that can be used to improve future operations.

[0735] As a concrete example, the system issues prompts to the generating AI model such as, "Analyze the user's emotions and provide appropriate feedback. If the user is feeling anxious, use specific and gentle language." This design allows the system to function interactively in a way that best suits the user's emotional state.

[0736] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0737] Step 1:

[0738] The server receives audio and video data from the user in real time through the terminal and stores it in a database. It receives audio data acquired using a microphone and video data acquired using a camera as input. This data is stored in the database for use in a later analysis step. The resulting output is the audio and video data registered in the database.

[0739] Step 2:

[0740] The server converts audio data into text and extracts emotional features from video data. It uses audio data stored in a database as audio input and transcribes it using a speech recognition algorithm. In parallel, it analyzes facial expressions from video data using image analysis technology and extracts the user's emotional state numerically. This results in output consisting of transcribed audio data and parameters indicating the emotional state.

[0741] Step 3:

[0742] The server detects inconsistencies by comparing standard operating procedures (SOPs) with operation history based on transcribed voice data and emotion parameters. It uses recent user operation history and transcribed voice data as input. This data is compared against the SOPs to determine if there are any discrepancies or deviations. The output provides a list of detected inconsistencies and supplementary information explaining their causes.

[0743] Step 4:

[0744] The terminal uses inconsistency information and sentiment analysis results obtained from the server to display feedback to the user in an appropriate tone. It receives inconsistency information and sentiment parameters as input and generates a feedback message. The generated message uses calm and polite language, especially when the user is showing signs of stress or confusion. The output is the feedback message displayed on the terminal.

[0745] Step 5:

[0746] The user performs actions based on the displayed feedback and sends new action data and emotional state results to the server. Input includes receiving specific instructions, including feedback messages displayed on the device, and performing actions according to those instructions. Output includes returning the results of the performed actions and the current emotional state as data to the server.

[0747] Step 6:

[0748] The server generates a detailed report based on the final operation results and accumulated sentiment analysis data. It receives accumulated data from each step, analysis results, and inconsistency information as input, and integrates them to create a report that includes an operation summary and improvement suggestions. The final output is a report that can be submitted to administrators or used for internal optimization.

[0749] (Application Example 2)

[0750] 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".

[0751] In factory and other work environments, providing feedback based on standard operating procedures and inconsistency detection without considering the emotional state of workers can lead to decreased efficiency and safety issues. Therefore, a system is needed that appropriately recognizes workers' emotions and provides feedback based on those emotions.

[0752] 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.

[0753] In this invention, the server includes, as a data collection means, means for recording each operation procedure and storing it in a database; as a preprocessing means, means for converting audio data into text and extracting information from image data; and as an emotion analysis means, means for identifying the user's emotional state using facial expressions and audio data and incorporating the results into the feedback. This enables the provision of appropriate feedback that takes the user's emotional state into consideration, thereby improving work efficiency and safety.

[0754] A "data collection means" is a component that provides the function of recording each operation procedure and storing it in a database.

[0755] A "preprocessing means" is a component that has the function of converting audio data into text and extracting information from image data.

[0756] A "data analysis tool" is a component that has the function of comparing and analyzing standard operating procedures and operation logs to detect inconsistencies.

[0757] A "feedback provision mechanism" is a component that has the function of notifying the user of detected inconsistencies and suggesting appropriate corrections.

[0758] An "emotion analysis tool" is a component that uses facial expression and voice data to identify the user's emotional state and incorporates the results into the feedback.

[0759] A "report generation means" is a component that has the function of recording the completion status of an operation and creating a report that includes suggestions for improvement.

[0760] The work support system based on this invention aims to improve work efficiency and safety in a factory environment. This system provides feedback that takes into account the emotional state of the worker through emotion analysis.

[0761] The server first uses data collection means to collect voice and facial expression data from workers and stores it in a database. Voice is acquired via a microphone, and facial expression data is captured by a camera. These data are then processed by preprocessing means, which transcribe the voice data into text and extract information from the image data, converting them into a format suitable for analysis.

[0762] For emotion analysis, open-source libraries such as "Librosa" and "OpenCV" are used. These software programs are used to extract emotional features from audio and analyze facial expressions from images in real time. This allows for the identification of the worker's emotional state, which is then stored in a database.

[0763] The data analysis system detects inconsistencies between standard operating procedures and operation logs, taking into account the results of sentiment analysis. The feedback system generates optimal instructions and corrections based on these inconsistencies and the worker's emotions, and presents them to the worker via a terminal. The presented information is customized to include softer tones or more technically detailed explanations as needed.

[0764] The user modifies their work based on the displayed feedback and sends the results from their terminal to the server. The server comprehensively evaluates the completion status of the operation and the user's emotional state, and generates a report that includes suggestions for improvement. This report is provided to the administrator and used to improve future operations.

[0765] For example, if an error is detected on the production line, the system senses the worker's stress and displays feedback on the terminal such as, "Don't worry, it's okay. Please double-check step B." This feedback helps the worker regain their composure and make the appropriate correction.

[0766] As an example of a prompt for the generative AI model, providing a format such as, "Analyze the emotional state of this data and generate relaxing feedback if 'stress' or 'confusion' is detected," allows for a more human-like response.

[0767] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0768] Step 1:

[0769] The server collects voice and facial expression data from the user via microphone and camera and stores it in a database. The input consists of real-time captured voice and image data, which are also stored in the database. Initial data is organized with a time stamp and session ID.

[0770] Step 2:

[0771] The server uses preprocessing to convert the collected audio data into text using "Librosa" and extract features from the image data using "OpenCV". The input is the audio and image data stored in step 1, and the output is the generated text data and digitized facial expression feature data.

[0772] Step 3:

[0773] The server identifies the user's emotional state based on data transcribed and digitized by emotion analysis tools. The input is the data generated in step 2, and the output is the user's emotional state (e.g., stress, joy). Here, a generative AI model is applied to analyze emotions based on specific prompt sentences.

[0774] Step 4:

[0775] The server uses data analysis tools to compare standard operating procedures (SOPs) and operation logs, taking emotional states into consideration, to detect inconsistencies. The input consists of the emotional analysis results from step 3 and the operation procedure data, and the output is a list of inconsistencies.

[0776] Step 5:

[0777] The device displays appropriate feedback to the user based on a list of inconsistencies and emotional state through a feedback provision mechanism. The input is the result of step 4, and the output is a feedback message displayed to the user. The message is customized using prompt sentences by a generating AI model.

[0778] Step 6:

[0779] The user reviews the feedback displayed on the device, performs the correction work according to the instructions, and sends the results of the work from the device to the server. The input is the feedback message and the work correction information, and the output is the updated operation data sent to the server.

[0780] Step 7:

[0781] The server uses a report generation mechanism to generate a detailed work report, including improvement suggestions, after all operations are completed. The input is the operation history and sentiment analysis results from step 6, and the output is a report provided to the administrator.

[0782] In this manner, the work support system operates efficiently in real time, providing optimal support that takes user emotions into consideration.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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."

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] The following is further disclosed regarding the embodiments described above.

[0805] (Claim 1)

[0806] As a means of data collection, there is a means of recording each operation procedure and storing it in a database,

[0807] As a preprocessing measure, the means include converting audio data to text and extracting information from image data,

[0808] As a data analysis method, a means of comparing and analyzing standard operating procedures and operation logs to detect inconsistencies is provided.

[0809] As a means of providing feedback, the system includes a means of notifying the user of detected inconsistencies and proposing corrective measures,

[0810] As a means of generating reports, there is a means of recording the completion status of operations and creating a report that includes improvement suggestions,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the means for detecting abnormalities include checking the range of input settings and identifying sequence errors.

[0814] (Claim 3)

[0815] The system according to claim 1, which displays pop-up messages or alerts to the user as a means of real-time feedback.

[0816] "Example 1"

[0817] (Claim 1)

[0818] Information processing means for monitoring operating procedures and storing data,

[0819] An information conversion means that converts audio information into written format and extracts specific information from visual information,

[0820] An information analysis means that uses a generative AI model to compare standard procedures and work records and detect differences,

[0821] A feedback device that notifies the user of the detected differences and suggests methods for improvement,

[0822] A report generation device that records work completion data and creates a report including improvement suggestions,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, which has an anomaly detection means for verifying the range of input information and identifying errors in the order.

[0826] (Claim 3)

[0827] The system according to claim 1, further comprising information provision means for presenting notification messages or warnings as dynamic feedback.

[0828] "Application Example 1"

[0829] (Claim 1)

[0830] As a means of data collection, there is a means of recording each operation procedure and storing it in a database,

[0831] As a preprocessing means, means for converting audio information into text and extracting predetermined information from image information,

[0832] As a data analysis method, a means of comparing and analyzing standard operating procedures and operation records to detect inconsistencies is provided.

[0833] As a means of providing feedback, there is a means of notifying the operator of detected inconsistencies and proposing corrective measures,

[0834] As a means of generating reports, there is a means of recording the completion status of operations and creating a report that includes improvement suggestions,

[0835] A means of displaying feedback in real time on an information terminal carried by the operator,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, wherein the means for detecting abnormalities include checking the range of input settings and identifying sequence errors.

[0839] (Claim 3)

[0840] The system according to claim 1, which utilizes pop-up notifications to display details of inconsistencies and correction procedures on an information terminal.

[0841] "Example 2 of combining an emotion engine"

[0842] (Claim 1)

[0843] A means for recording user input and accumulating data including audio and video data,

[0844] A means of pre-processing audio information to convert it into text information and extract related information from video data,

[0845] A means to analyze data consistency by comparing work instructions and operation history, and to detect discrepancies,

[0846] A means of providing users with appropriate words and tone of feedback regarding data inconsistencies based on sentiment analysis results,

[0847] Means for recording the progress of operations, generating detailed reports including sentiment analysis, and including suggestions,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, which analyzes the user's emotional state and dynamically adjusts the tone and content of the operation feedback.

[0851] (Claim 3)

[0852] The system according to claim 1, which displays emotion-responsive feedback in real time to user actions.

[0853] "Application example 2 when combining with an emotional engine"

[0854] (Claim 1)

[0855] As a means of data collection, there is a means of recording each operation procedure and storing it in a database,

[0856] As a preprocessing measure, the means include converting audio data to text and extracting information from image data,

[0857] As a data analysis method, a means of comparing and analyzing standard operating procedures and operation logs to detect inconsistencies is provided.

[0858] As a means of providing feedback, the system includes a means of notifying the user of detected inconsistencies and proposing corrective measures,

[0859] As a means of emotion analysis, a means of identifying the user's emotional state using facial expression and voice data and incorporating the results into feedback,

[0860] As a means of generating reports, there is a means of recording the completion status of operations and creating a report that includes improvement suggestions,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, wherein the means for detecting abnormalities include checking the range of input settings and identifying sequence errors.

[0864] (Claim 3)

[0865] The system according to claim 1, which displays pop-up messages or alerts to the user as a means of real-time feedback and adjusts the information according to the user's emotional state. [Explanation of symbols]

[0866] 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. As a means of data collection, there is a means of recording each operation procedure and storing it in a database, As a preprocessing measure, the means include converting audio data to text and extracting information from image data, As a data analysis method, a means of comparing and analyzing standard operating procedures and operation logs to detect inconsistencies is provided. As a means of providing feedback, the system includes a means of notifying the user of detected inconsistencies and proposing corrective measures, As a means of generating reports, there is a means of recording the completion status of operations and creating a report that includes improvement suggestions, A system that includes this.

2. The system according to claim 1, wherein the means for detecting abnormalities include checking the range of input settings and identifying sequence errors.

3. The system according to claim 1, which displays pop-up messages or alerts to the user as a means of real-time feedback.

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