system

Abstract

We provide the system. [Solution] A means of collecting data on tasks completed by individual users, A means for analyzing the aforementioned data and generating feedback for the work, A means for generating training content suitable for the user based on the aforementioned feedback, Means for providing the aforementioned feedback and training content to the user, 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] Conventionally, it has been difficult to analyze in detail the content of the work performed by individual users and provide immediate feedback. Also, it has been difficult to immediately generate training content optimized for each user and quickly improve skills. Furthermore, there has been a problem in that there is no unified system for effectively utilizing various data formats (for example, voice and text).

Means for Solving the Problems

[0005] To solve the aforementioned problems, the present invention provides a means for collecting data on tasks completed by users, analyzing this data, and generating feedback on those tasks. Furthermore, it includes a means for generating training content suitable for the user based on this feedback, and by constructing a system that provides these to users in an integrated manner, it is possible to respond to the individual needs of users and enable rapid skill improvement.

[0006] "User" refers to an individual or organization that uses this system to perform business operations.

[0007] "Tasks" refer to specific tasks or projects that users are required to complete.

[0008] "Data" refers to a collection of information generated by users and collected by the system, including in various forms such as audio, video, and text.

[0009] "Analysis" refers to the process of evaluating collected data in detail and extracting or transforming information.

[0010] "Feedback" refers to information that provides suggestions for improvement and evaluations regarding the user's work based on the analysis results.

[0011] "Training content" refers to educational or instructional materials and programs provided to improve the user's performance.

[0012] In this invention, "system" refers to a set of devices or software that include each processing means and have a series of functions for providing feedback and training content 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the language 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] This invention constructs a system that provides real-time feedback and optimized training content for the tasks performed by individual users. This system mainly consists of a server, terminals, and users.

[0035] The server plays a central role in processing data related to the tasks performed by the user. Specifically, the server receives audio, video, or text data sent from the terminal and analyzes it using natural language processing and machine learning technologies. Based on the results of this analysis, the server generates feedback for the user and selects the optimal training content considering the user's past data and performance.

[0036] The terminal functions as an interface for users performing their work. For example, it records the audio of a user giving a presentation and sends the data to a server. In this process, a secure protocol is typically used to ensure privacy. Additionally, the generated feedback and training content are sent to the terminal for the user to review.

[0037] Users can improve their work based on the feedback provided and implement recommended training. For example, immediately after a new employee finishes a presentation, the server analyzes it and generates feedback such as "making the introduction more concise would be more effective," and then training to improve introduction skills is provided via the terminal.

[0038] This system allows users to quickly and efficiently hone their skills and improve the quality of their work.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Once the user completes a task, the terminal collects data related to that task. The data collected here includes audio information, video footage, and text content, which are selected based on the type of task.

[0042] Step 2:

[0043] The device sends the collected data to the server. Data transmission is typically done via security protocols, ensuring data integrity and privacy.

[0044] Step 3:

[0045] The server analyzes the received data. Analysis methods include converting audio data into text using natural language processing and speech recognition technologies, and then extracting information and evaluating its structure. In particular, keywords and their frequency of use are counted to measure the level of understanding of the business content.

[0046] Step 4:

[0047] The server generates feedback based on the analysis results. This includes specific comments on the strengths and areas for improvement of the work, as well as quantitative evaluations (e.g., presentation time allocation).

[0048] Step 5:

[0049] The server selects the optimal training content based on the user's past work history and feedback records. It utilizes machine learning algorithms to identify the user's weaknesses and areas for improvement, creating an optimized training plan.

[0050] Step 6:

[0051] The server sends generated feedback and recommended training content to the device. The device receives this information and notifies the user. The user can access this information to view details.

[0052] Step 7:

[0053] Users review notifications and improve their work processes based on the feedback and training content provided. Training plans are implemented as needed to enhance skills.

[0054] (Example 1)

[0055] 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."

[0056] To effectively improve the diverse tasks performed by individual users, it is essential to provide feedback and training tailored to each task and user's characteristics. However, conventional systems have difficulty providing real-time feedback, and have been insufficient in suggesting optimal training content using past data. Thus, the challenge lies in providing rapid and accurate support for improving users' skills.

[0057] 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.

[0058] In this invention, the server includes means for recording actions related to tasks performed by the user and collecting data; means for analyzing the data and generating feedback based on the task content; and means for generating optimal training content based on the feedback and past performance data. This enables the provision of rapid and accurate feedback and training content for tasks performed by the user.

[0059] "User" refers to an individual or organization that uses this system to improve its operations.

[0060] "Business activities" refers to a series of activities that users perform on a daily basis, including actions taken to achieve a specific purpose.

[0061] "Feedback" refers to information that indicates evaluations and areas for improvement regarding the results and processes of tasks performed by users.

[0062] "Training content" refers to educational resources and programs recommended to improve users' skills and knowledge.

[0063] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and generate human language.

[0064] "Machine learning technology" refers to the ability of computers to learn from data and improve, and is used for prediction and pattern recognition.

[0065] "Data" refers to information such as audio, text, and video that is generated or collected in connection with the user's business operations.

[0066] This invention is a system for improving the tasks performed by users and for rapidly and effectively enhancing their skills. The system consists of three main components: a server, a terminal, and a user.

[0067] First, the terminal functions as an interface for users to perform their work. For example, when a user is giving a presentation, the terminal records the audio and sends it to the server in real time. The data is sent using a secure protocol, ensuring privacy.

[0068] The server receives and analyzes audio, video, and text data sent from terminals. It utilizes natural language processing and machine learning technologies to analyze the data and generate feedback on the user's work. The analyzed information indicates areas where the user should improve their work, and the server selects the optimal training content based on past performance data.

[0069] On the other hand, users can review the feedback provided by the server and improve their work. For example, after a presentation, they might receive feedback such as "shorten the introduction," and specific training to address this will be provided through their device.

[0070] As a concrete example, one prompt that utilizes a generative AI model might be, "Please give me some specific tips on how to improve my presentation." Users can input such prompts and immediately receive feedback and training information.

[0071] This system enables users to improve the quality of their work and continuously enhance their individual skills.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The terminal records the user's activities while they are performing their work. It collects data such as audio and video in real time as input. Specifically, it records audio during a presentation and sends that data to the server using a secure protocol. The output is the recorded data file.

[0075] Step 2:

[0076] The server receives audio data from the terminal as input. The server uses natural language processing techniques to convert the audio data into text data. At this stage, a speech recognition algorithm is used to output the result converted into text information. Specifically, the recognized text data is passed on to the next analysis step.

[0077] Step 3:

[0078] The server analyzes the converted text data as input and generates feedback related to the task. It utilizes a generative AI model to evaluate user performance from the text and identify areas for improvement. As output, it generates specific feedback for that task. A concrete example of this is feedback such as, "The introduction is too long; it would be more effective to halve the time."

[0079] Step 4:

[0080] The server selects the optimal training content based on feedback and the user's past performance data. Feedback and historical data are used as input. A machine learning model outputs a training program to improve the user's skills. Specifically, a particular training module for improving introduction skills is selected.

[0081] Step 5:

[0082] The device receives feedback and training content sent from the server. These are used as input and displayed in a user-friendly format. As output, the user reviews this information and decides on their next action. Specifically, the UI / UX ensures that feedback is displayed on the screen, providing an environment where the user can access it immediately.

[0083] Step 6:

[0084] The user takes action to improve their work based on the feedback and training content provided. As input, they receive all the provided information. As output, the user improves the quality of their work and moves on to the next task with enhanced skills. Specifically, they might implement improvements to the introduction, as pointed out, for their next presentation.

[0085] (Application Example 1)

[0086] 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."

[0087] In modern manufacturing environments, there is a constant need to improve work efficiency and product quality. However, evaluating the performance of each worker and piece of machinery in real time and providing immediate, appropriate feedback based on that evaluation is a challenging task. As a result, optimization of work processes and quality improvements can be delayed. In factories in particular, rapid improvement of work processes directly impacts product production, making the resolution of these challenges essential.

[0088] 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.

[0089] In this invention, the server includes means for acquiring information about the tasks performed by individual users, means for analyzing the information and creating a response to the tasks, and means for creating training content tailored to the user based on the responses. This enables workers and machinery to immediately adjust their operations through real-time work evaluation and feedback, thereby improving product quality and work efficiency.

[0090] A "user" is an entity that uses the system to obtain information and receive training.

[0091] "Information" refers to data related to the tasks completed by the user, and is acquired in various forms, including audio and video.

[0092] "Means of acquisition" refers to methods and devices for collecting information, such as cameras and sensors.

[0093] "Means of analysis" refers to the process of analyzing acquired information and generating a response based on that analysis.

[0094] A "response" is feedback or instructions generated based on the analysis results.

[0095] "Training content" refers to educational content provided with the aim of improving users' skills and work processes.

[0096] A "recording device" refers to hardware or software used to store information.

[0097] "Image recognition technology" is a technique for extracting features from image data and analyzing them.

[0098] "Mechanical equipment" refers to devices and robots used to perform specific tasks in factories and other similar facilities.

[0099] A "means of adjustment" is a mechanism that changes or corrects the operation of a device based on a response.

[0100] This invention provides a system for acquiring and analyzing information in real time to improve work efficiency and quality in workplaces such as factories. This system mainly consists of a server, a recording device, and mechanical devices.

[0101] The server plays a central role, primarily analyzing information and generating responses. It receives audio and video information transmitted from recording devices and analyzes it using a generative AI model. This model then generates responses and training content.

[0102] Recording devices primarily function as interfaces for acquiring information about the operation of machinery and the surrounding environment. For example, cameras and microphones attached to machinery operating in a factory capture the work in real time and transmit that information to a server. Secure protocols are used to ensure the safety of the data.

[0103] The machine adjusts and corrects its operation based on the response received from the server. For example, an assembly robot can improve work accuracy by adjusting the speed and angle at which it places parts. A specific example is optimizing the operation with the goal of increasing the rate at which parts are placed in the correct position to over 90%.

[0104] A concrete example of a response that might be generated is feedback such as, "It would be more effective to perform the introductory actions faster." This feedback allows the machine to speed up its operations, thereby improving production efficiency.

[0105] An example of a prompt would be, "Analyze a video of a robot assembling parts. The main objective is to obtain feedback to improve the accuracy and speed of the assembly." Using this prompt, the generative AI model can perform appropriate analysis and ultimately provide high-quality feedback.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The terminal captures the operation of machinery and equipment running in the factory in real time using cameras and microphones. This acquires audio and video data. This acquired data is then transmitted to the server using a secure protocol.

[0109] Step 2:

[0110] The server receives audio and video data from the terminal and analyzes it using a generated AI model. This allows for advanced data analysis using the acquired data in conjunction with prompt messages, extracting features related to work efficiency and quality.

[0111] Step 3:

[0112] The server generates a response to the user based on the analysis results. This response includes specific feedback, such as "It would be desirable to improve the assembly speed of the parts by 10%." The generated response is transmitted to the machine via the terminal.

[0113] Step 4:

[0114] The terminal receives a response sent from the server and applies it to the machine. Based on this response, the machine adjusts its own operation, optimizing its speed and accuracy. This allows the production line in the factory to improve its operation in real time.

[0115] Step 5:

[0116] Users can check the operational improvements of the machinery and equipment through their terminals. Based on this feedback, further training will be conducted to improve work efficiency and quality. Specifically, training content such as operation manuals and video materials will be provided.

[0117] 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.

[0118] This invention is a system that recognizes the emotions of users performing tasks and provides feedback and training content that takes those emotions into account. The system consists of a server, terminals, users, and an emotion recognition engine.

[0119] The server plays a central role in analyzing data transmitted from terminals. It works in conjunction with the emotion recognition engine to identify the user's emotional state using audio and video data. This emotion analysis is performed based on the user's facial expressions and tone of voice when giving presentations or entering information. The emotion recognition engine utilizes natural language processing and computer vision technologies to evaluate the user's emotions (e.g., joy, tension, excitement) in real time.

[0120] The device functions as an interface to the user and collects composite data, including emotional data. For example, when a user is giving a presentation in a meeting, the device records both audio and video and sends them to a server. Based on this data, the server performs emotion recognition.

[0121] Users view server-generated feedback on their devices. This feedback, based on analyzed emotional states, can include not only performance-based feedback but also emotional care and support. For example, if a user shows signs of nervousness during a presentation, the server might offer advice such as, "Try these techniques to ease your tension." Conversely, if the presentation is successful, feedback is provided to amplify the user's satisfaction.

[0122] In this way, the system takes the user's emotional state into account, providing more effective and personalized feedback and training content. This can improve the user's work efficiency and satisfaction.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] As the user begins work, the device collects the user's voice and video data in real time. This includes speech and facial movements during presentations.

[0126] Step 2:

[0127] The device sends the collected audio and video data to the server. This transmission utilizes a secure communication protocol to ensure data privacy.

[0128] Step 3:

[0129] The server analyzes the received data and uses an emotion recognition engine to determine the user's emotional state from their facial expressions and tone of voice. For example, machine learning algorithms can be used to identify joy or tension from changes in voice pitch and facial expressions.

[0130] Step 4:

[0131] The server generates feedback based on the analysis results. This feedback takes into account the user's emotional state and includes areas for improvement and points of praise. The feedback is designed to be sensitive to the user's feelings.

[0132] Step 5:

[0133] The server selects the optimal training content based on the user's emotional state and work history. It utilizes machine learning models based on historical data to recommend personalized training methods for each user.

[0134] Step 6:

[0135] The server generates feedback and training content, which is then sent to the terminal and presented to the user. The user reviews this information and uses the feedback to improve their work or conduct further training.

[0136] Step 7:

[0137] Users improve their skills by putting the feedback they receive into practice and completing recommended training. Through this process, they can receive more effective and emotionally sensitive support.

[0138] (Example 2)

[0139] 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".

[0140] The emotional state of users performing tasks significantly impacts work efficiency and results. However, conventional systems have struggled to provide specific and personalized feedback that takes into account the emotional state of users. In particular, integrating and analyzing data in multiple formats, such as audio and video, to accurately identify a user's emotional state is technically difficult and has not yielded sufficient results.

[0141] 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.

[0142] In this invention, the server includes means for collecting information about tasks completed by individual users, means for analyzing the information and generating a response to the tasks, and means for identifying the user's emotional state using the generated audio and video information. This makes it possible to provide personalized feedback that corresponds to the user's emotional state.

[0143] "User" refers to an individual or organization that uses the system to perform business operations.

[0144] "Information" is a general term that includes data related to the user, such as audio data, video data, and text data.

[0145] "Response" refers to feedback or advice given to the user based on the analyzed information.

[0146] "Emotional state" is a concept that refers to the result of identifying and identifying the emotional responses and conditions exhibited by a user.

[0147] "Means" refers to a method, device, or mechanism used to achieve a specific function or purpose.

[0148] "Personalized feedback" refers to responses that are customized based on the emotional state and work content of each individual user.

[0149] This invention is a system that recognizes the emotional state of users performing tasks in real time and provides appropriate feedback and training content. The system consists of a server, terminals, users, and an emotion recognition engine.

[0150] The server plays a central role in the system, analyzing audio and video data transmitted from terminals. This data is analyzed through an emotion recognition engine that utilizes natural language processing and computer vision technologies. Specific hardware components include a camera, microphone, and a high-performance processor. Software components include natural language processing libraries and image analysis libraries.

[0151] The device functions as an interface to the user and collects composite data, including emotional data. For example, when a user gives a presentation in a meeting, the device records the audio and video and sends that data to a server.

[0152] Users view feedback generated by the server through their device. This feedback is based on analyzed emotional states and can provide specific advice and emotional support to help improve work efficiency. For example, if a user shows signs of nervousness during a presentation, the server might offer specific advice such as, "Try taking some deep breaths to ease your tension."

[0153] An example of a prompt might be: "Provide appropriate feedback based on the user's emotional state during the presentation. Include specific advice, especially if tension or excitement is observed." This instruction could be input into the AI ​​model.

[0154] In this way, the system aims to improve work performance by providing more personalized support while taking into account the user's emotional state.

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

[0156] Step 1:

[0157] The device collects audio and video data emitted by the user during work. Specifically, it uses the device's microphone and camera to record the user's speech and facial expressions in real time. This input data is important for evaluating the user's emotional state. The collected audio and video data is prepared for analysis after noise is removed using noise cancellation technology.

[0158] Step 2:

[0159] The device transmits the collected audio and video data to the server. Encryption technology is used during the transmission process to ensure data integrity and security. This transmitted data forms the basis for subsequent sentiment analysis and processing.

[0160] Step 3:

[0161] The server begins analyzing the received audio and video data using an emotion recognition engine. Specifically, it converts the audio data into text using natural language processing and identifies emotions based on the content. Furthermore, it analyzes the video data using computer vision technology, identifying emotional states by evaluating the user's facial expressions and body gestures. The output of this analysis is a detailed assessment of the user's emotions, such as joy, tension, and excitement.

[0162] Step 4:

[0163] Based on the analysis results, the server uses a generative AI model to create feedback to provide to the user. At this stage, it generates appropriate advice and training content based on the recognized emotional state. For example, if the analysis results indicate tension, it generates specific feedback such as, "To relieve tension, try the following breathing exercise." This feedback is output in text format.

[0164] Step 5:

[0165] The user receives feedback from the server via their device. The device displays the received feedback for the user to review. In this step, the user is expected to actually use the feedback in their work. Based on the advice provided, the user adjusts their behavior during work, aiming to improve their emotional state and work efficiency.

[0166] (Application Example 2)

[0167] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0168] In many commercial facilities and service providers, the emotions of staff members significantly impact customer satisfaction. Traditional systems fail to provide real-time feedback based on staff members' emotional states, leading to decreased operational efficiency and customer service capabilities. Therefore, to improve the quality of the customer experience, there is a need for a system that appropriately analyzes staff members' emotional states and provides effective feedback in real time.

[0169] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0170] In this invention, the server includes means for collecting information on operations completed by the person in charge of operations; means for analyzing the information and generating an evaluation result for the operations; means for generating training content suitable for the person in charge based on the evaluation result; means for analyzing the emotional state of the person in charge of operations; and means for providing real-time feedback during operations based on the emotional state. This enables real-time feedback that takes into account the emotional state of the person in charge of operations, thereby improving customer service capabilities.

[0171] A "business representative" refers to a person whose job is to interact with customers at a commercial facility or service provider.

[0172] "Information related to operations" refers to specific data about the tasks and activities that employees perform on a daily basis.

[0173] "Evaluation results" refer to judgments regarding the performance and efficiency of tasks, based on analysis of information related to operations.

[0174] "Educational content" refers to training programs and instruction provided to improve the skills and operational efficiency of employees.

[0175] "Emotional state" refers to the current mental and emotional state of the person performing the task, and includes emotions such as tension, joy, and anxiety.

[0176] "Real-time feedback" refers to evaluations and advice provided immediately to employees while they are performing their tasks, with the aim of improving operations and enhancing the quality of customer service.

[0177] This system aims to improve the performance of business personnel by efficiently managing their emotional state in customer interactions and providing real-time feedback.

[0178] The server collects operational information from smart glasses and devices carried by the service provider. The smart glasses have a built-in camera and microphone to record the service provider's voice and video through interactions with customers. This data is transmitted to the device via Bluetooth and then uploaded to the server. The server uses generative AI models such as Google Cloud's Natural Language API and Amazon Rekognition to analyze the voice and video in real time to identify the service provider's emotional state.

[0179] After analyzing the employee's emotions, the server generates appropriate training content for the employee and displays it on the smart glasses or device screen. For example, if the system detects that the employee is feeling nervous, advice such as "Take a deep breath and try to relax" might appear on the glasses. This encourages the employee to interact with customers more naturally.

[0180] For example, if the person in charge of the task is a newly hired barista at a cafe, real-time feedback can provide specific advice on how to respond with a smile when a customer asks a particular question. An example of a prompt to the generative AI model would be, "Please provide advice on how to respond with a smile when a customer asks a specific question."

[0181] In this way, the system can analyze emotional states in real time and provide feedback to staff, thereby improving customer service capabilities.

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

[0183] Step 1:

[0184] The terminal acquires audio and video data from smart glasses worn by the employee. This data is used to capture the employee's actions and facial expressions during work. The input is data from the smart glasses, and the output is audio and video data stored on the terminal.

[0185] Step 2:

[0186] The terminal transmits the acquired audio and video data to the server using Bluetooth. This process is the transfer of data to prepare it for analysis. The input is the audio and video data stored on the terminal, and the output is the data transferred to the server.

[0187] Step 3:

[0188] The server analyzes the received audio and video data. This analysis uses generative AI models such as Google Cloud's Natural Language API and Amazon Rekognition. The input is the data transferred to the server, and the output is an evaluation of emotional state, including the generated analysis data. Specifically, it performs emotion extraction from audio and facial expression recognition from video.

[0189] Step 4:

[0190] The server generates appropriate feedback and training content for the employee based on the analyzed emotional state. The input is the analyzed data, and the output is the generated feedback content. Specifically, it generates text information and constructs prompt messages to provide to the employee.

[0191] Step 5:

[0192] The server sends the generated feedback to the terminal and displays it on the smart glasses' screen. The input is the generated feedback content, and the output is the feedback presented to the person performing the task. Specific actions include displaying text on the screen and executing simple animations.

[0193] Step 6:

[0194] Users (business personnel) adjust their customer interactions based on the real-time feedback provided. This feedback serves as a guideline for business personnel to confidently interact with customers during their work. Implementing feedback improves the quality of work.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] [Second Embodiment]

[0199] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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".

[0211] This invention constructs a system that provides real-time feedback and optimized training content for the tasks performed by individual users. This system mainly consists of a server, terminals, and users.

[0212] The server plays a central role in processing data related to the tasks performed by the user. Specifically, the server receives audio, video, or text data sent from the terminal and analyzes it using natural language processing and machine learning technologies. Based on the results of this analysis, the server generates feedback for the user and selects the optimal training content considering the user's past data and performance.

[0213] The terminal functions as an interface for users performing their work. For example, it records the audio of a user giving a presentation and sends the data to a server. In this process, a secure protocol is typically used to ensure privacy. Additionally, the generated feedback and training content are sent to the terminal for the user to review.

[0214] Users can improve their work based on the feedback provided and implement recommended training. For example, immediately after a new employee finishes a presentation, the server analyzes it and generates feedback such as "making the introduction more concise would be more effective," and then training to improve introduction skills is provided via the terminal.

[0215] This system allows users to quickly and efficiently hone their skills and improve the quality of their work.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] Once the user completes a task, the terminal collects data related to that task. The data collected here includes audio information, video footage, and text content, which are selected based on the type of task.

[0219] Step 2:

[0220] The device sends the collected data to the server. Data transmission is typically done via security protocols, ensuring data integrity and privacy.

[0221] Step 3:

[0222] The server analyzes the received data. Analysis methods include converting audio data into text using natural language processing and speech recognition technologies, and then extracting information and evaluating its structure. In particular, keywords and their frequency of use are counted to measure the level of understanding of the business content.

[0223] Step 4:

[0224] The server generates feedback based on the analysis results. This includes specific comments on the strengths and areas for improvement of the work, as well as quantitative evaluations (e.g., presentation time allocation).

[0225] Step 5:

[0226] The server selects the optimal training content based on the user's past work history and feedback records. It utilizes machine learning algorithms to identify the user's weaknesses and areas for improvement, creating an optimized training plan.

[0227] Step 6:

[0228] The server sends generated feedback and recommended training content to the device. The device receives this information and notifies the user. The user can access this information to view details.

[0229] Step 7:

[0230] Users review notifications and improve their work processes based on the feedback and training content provided. Training plans are implemented as needed to enhance skills.

[0231] (Example 1)

[0232] 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."

[0233] To effectively improve the diverse tasks performed by individual users, it is essential to provide feedback and training tailored to each task and user's characteristics. However, conventional systems have difficulty providing real-time feedback, and have been insufficient in suggesting optimal training content using past data. Thus, the challenge lies in providing rapid and accurate support for improving users' skills.

[0234] 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.

[0235] In this invention, the server includes means for recording actions related to tasks performed by the user and collecting data; means for analyzing the data and generating feedback based on the task content; and means for generating optimal training content based on the feedback and past performance data. This enables the provision of rapid and accurate feedback and training content for tasks performed by the user.

[0236] "User" refers to an individual or organization that uses this system to improve its operations.

[0237] "Business activities" refers to a series of activities that users perform on a daily basis, including actions taken to achieve a specific purpose.

[0238] "Feedback" refers to information that indicates evaluations and areas for improvement regarding the results and processes of tasks performed by users.

[0239] "Training content" refers to educational resources and programs recommended to improve users' skills and knowledge.

[0240] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and generate human language.

[0241] "Machine learning technology" refers to the ability of computers to learn from data and improve, and is used for prediction and pattern recognition.

[0242] "Data" refers to information such as audio, text, and video that is generated or collected in connection with the user's business operations.

[0243] This invention is a system for improving the tasks performed by users and for rapidly and effectively enhancing their skills. The system consists of three main components: a server, a terminal, and a user.

[0244] First, the terminal functions as an interface for users to perform their work. For example, when a user is giving a presentation, the terminal records the audio and sends it to the server in real time. The data is sent using a secure protocol, ensuring privacy.

[0245] The server receives and analyzes audio, video, and text data sent from terminals. It utilizes natural language processing and machine learning technologies to analyze the data and generate feedback on the user's work. The analyzed information indicates areas where the user should improve their work, and the server selects the optimal training content based on past performance data.

[0246] On the other hand, users can review the feedback provided by the server and improve their work. For example, after a presentation, they might receive feedback such as "shorten the introduction," and specific training to address this will be provided through their device.

[0247] As a concrete example, one prompt that utilizes a generative AI model might be, "Please give me some specific tips on how to improve my presentation." Users can input such prompts and immediately receive feedback and training information.

[0248] This system enables users to improve the quality of their work and continuously enhance their individual skills.

[0249] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0250] Step 1:

[0251] The terminal records the user's activities while they are performing their work. It collects data such as audio and video in real time as input. Specifically, it records audio during a presentation and sends that data to the server using a secure protocol. The output is the recorded data file.

[0252] Step 2:

[0253] The server receives audio data from the terminal as input. The server uses natural language processing techniques to convert the audio data into text data. At this stage, a speech recognition algorithm is used to output the result converted into text information. Specifically, the recognized text data is passed on to the next analysis step.

[0254] Step 3:

[0255] The server analyzes the converted text data as input and generates feedback related to the task. It utilizes a generative AI model to evaluate user performance from the text and identify areas for improvement. As output, it generates specific feedback for that task. A concrete example of this is feedback such as, "The introduction is too long; it would be more effective to halve the time."

[0256] Step 4:

[0257] The server selects the optimal training content based on feedback and the user's past performance data. Feedback and historical data are used as input. A machine learning model outputs a training program to improve the user's skills. Specifically, a particular training module for improving introduction skills is selected.

[0258] Step 5:

[0259] The device receives feedback and training content sent from the server. These are used as input and displayed in a user-friendly format. As output, the user reviews this information and decides on their next action. Specifically, the UI / UX ensures that feedback is displayed on the screen, providing an environment where the user can access it immediately.

[0260] Step 6:

[0261] The user takes action to improve their work based on the feedback and training content provided. As input, they receive all the provided information. As output, the user improves the quality of their work and moves on to the next task with enhanced skills. Specifically, they might implement improvements to the introduction, as pointed out, for their next presentation.

[0262] (Application Example 1)

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

[0264] In modern manufacturing environments, there is a constant need to improve work efficiency and product quality. However, evaluating the performance of each worker and piece of machinery in real time and providing immediate, appropriate feedback based on that evaluation is a challenging task. As a result, optimization of work processes and quality improvements can be delayed. In factories in particular, rapid improvement of work processes directly impacts product production, making the resolution of these challenges essential.

[0265] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0266] In this invention, the server includes means for acquiring information about the tasks performed by individual users, means for analyzing the information and creating a response to the tasks, and means for creating training content tailored to the user based on the responses. This enables workers and machinery to immediately adjust their operations through real-time work evaluation and feedback, thereby improving product quality and work efficiency.

[0267] A "user" is an entity that uses the system to obtain information and receive training.

[0268] "Information" refers to data related to the tasks completed by the user, and is acquired in various forms, including audio and video.

[0269] "Means of acquisition" refers to methods and devices for collecting information, such as cameras and sensors.

[0270] "Means of analysis" refers to the process of analyzing acquired information and generating a response based on that analysis.

[0271] A "response" is feedback or instructions generated based on the analysis results.

[0272] "Training content" refers to educational content provided with the aim of improving users' skills and work processes.

[0273] A "recording device" refers to hardware or software used to store information.

[0274] "Image recognition technology" is a technique for extracting features from image data and analyzing them.

[0275] "Mechanical equipment" refers to devices and robots used to perform specific tasks in factories and other similar facilities.

[0276] A "means of adjustment" is a mechanism that changes or corrects the operation of a device based on a response.

[0277] This invention provides a system for acquiring and analyzing information in real time to improve work efficiency and quality in workplaces such as factories. This system mainly consists of a server, a recording device, and mechanical devices.

[0278] The server plays a central role, primarily analyzing information and generating responses. It receives audio and video information transmitted from recording devices and analyzes it using a generative AI model. This model then generates responses and training content.

[0279] Recording devices primarily function as interfaces for acquiring information about the operation of machinery and the surrounding environment. For example, cameras and microphones attached to machinery operating in a factory capture the work in real time and transmit that information to a server. Secure protocols are used to ensure the safety of the data.

[0280] The machine adjusts and corrects its operation based on the response received from the server. For example, an assembly robot can improve work accuracy by adjusting the speed and angle at which it places parts. A specific example is optimizing the operation with the goal of increasing the rate at which parts are placed in the correct position to over 90%.

[0281] As a specific example of the generated response, feedback such as "Performing the operation of the introduction part faster is effective" can be considered. With this feedback, the mechanical device can speed up its operation and as a result, improve production efficiency.

[0282] As an example of a prompt sentence, there is one such as "Please analyze the video of the robot's part assembly work. The main purpose is to obtain feedback for improving the accuracy and speed of assembly." By using this prompt, the generative AI model can perform appropriate analysis and ultimately provide high-quality feedback.

[0283] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0284] Step 1:

[0285] The terminal captures in real time the operation of the mechanical device operating in the factory using a camera or microphone. As a result, audio data and video data are acquired. This acquired data is transmitted to the server using a secure protocol.

[0286] Step 2:

[0287] The server receives the audio data and video data received from the terminal and performs analysis using the generative AI model. As a result, advanced data analysis is performed using the acquired data in combination with the prompt sentence, and features related to work efficiency and quality are extracted.

[0288] Step 3:

[0289] The server generates a response to the user based on the analysis result. This response includes specific feedback such as, for example, "It is desirable to improve the assembly speed of the parts by 10%." The generated response is transmitted to the mechanical device via the terminal.

[0290] Step 4:

[0291] The terminal receives a response sent from the server and applies it to the machine. Based on this response, the machine adjusts its own operation, optimizing its speed and accuracy. This allows the production line in the factory to improve its operation in real time.

[0292] Step 5:

[0293] Users can check the operational improvements of the machinery and equipment through their terminals. Based on this feedback, further training will be conducted to improve work efficiency and quality. Specifically, training content such as operation manuals and video materials will be provided.

[0294] 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.

[0295] This invention is a system that recognizes the emotions of users performing tasks and provides feedback and training content that takes those emotions into account. The system consists of a server, terminals, users, and an emotion recognition engine.

[0296] The server plays a central role in analyzing data transmitted from terminals. It works in conjunction with the emotion recognition engine to identify the user's emotional state using audio and video data. This emotion analysis is performed based on the user's facial expressions and tone of voice when giving presentations or entering information. The emotion recognition engine utilizes natural language processing and computer vision technologies to evaluate the user's emotions (e.g., joy, tension, excitement) in real time.

[0297] The device functions as an interface to the user and collects composite data, including emotional data. For example, when a user is giving a presentation in a meeting, the device records both audio and video and sends them to a server. Based on this data, the server performs emotion recognition.

[0298] The user checks the feedback generated by the server on the terminal. This feedback can include not only the performance of the business but also emotional care and support based on the analyzed emotional state. As a specific example, when the user shows signs of tension during a presentation, the server provides advice such as "Please try the following techniques to relieve tension." Also, when the presentation is successful, feedback is provided to amplify the user's joy.

[0299] In this way, by considering the user's emotional state, the system provides more effective and personalized feedback and training content, thereby improving the user's work efficiency and satisfaction.

[0300] The following describes the processing flow.

[0301] Step 1:

[0302] The user starts the business, and the terminal collects the user's voice and video data in real time. This includes speech during a presentation, facial movements, etc.

[0303] Step 2:

[0304] The terminal sends the collected voice and video data to the server. For this transmission, a secure communication protocol is used to ensure the privacy of the data.

[0305] [[ID=​​​​​​​​​​​ The server generates feedback based on the analysis results. This feedback takes into account the user's emotional state and includes areas for improvement and points of praise. The feedback is designed to be sensitive to the user's feelings.

[0309] Step 5:

[0310] The server selects the optimal training content based on the user's emotional state and work history. It utilizes machine learning models based on historical data to recommend personalized training methods for each user.

[0311] Step 6:

[0312] The server generates feedback and training content, which is then sent to the terminal and presented to the user. The user reviews this information and uses the feedback to improve their work or conduct further training.

[0313] Step 7:

[0314] Users improve their skills by putting the feedback they receive into practice and completing recommended training. Through this process, they can receive more effective and emotionally sensitive support.

[0315] (Example 2)

[0316] 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".

[0317] The emotional state of users performing tasks significantly impacts work efficiency and results. However, conventional systems have struggled to provide specific and personalized feedback that takes into account the emotional state of users. In particular, integrating and analyzing data in multiple formats, such as audio and video, to accurately identify a user's emotional state is technically difficult and has not yielded sufficient results.

[0318] 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.

[0319] In this invention, the server includes means for collecting information about tasks completed by individual users, means for analyzing the information and generating a response to the tasks, and means for identifying the user's emotional state using the generated audio and video information. This makes it possible to provide personalized feedback that corresponds to the user's emotional state.

[0320] "User" refers to an individual or organization that uses the system to perform business operations.

[0321] "Information" is a general term that includes data related to the user, such as audio data, video data, and text data.

[0322] "Response" refers to feedback or advice given to the user based on the analyzed information.

[0323] "Emotional state" is a concept that refers to the result of identifying and identifying the emotional responses and conditions exhibited by a user.

[0324] "Means" refers to a method, device, or mechanism used to achieve a specific function or purpose.

[0325] "Personalized feedback" refers to responses that are customized based on the emotional state and work content of each individual user.

[0326] This invention is a system that recognizes the emotional state of users performing tasks in real time and provides appropriate feedback and training content. The system consists of a server, terminals, users, and an emotion recognition engine.

[0327] The server plays a central role in the system, analyzing audio and video data transmitted from terminals. This data is analyzed through an emotion recognition engine that utilizes natural language processing and computer vision technologies. Specific hardware components include a camera, microphone, and a high-performance processor. Software components include natural language processing libraries and image analysis libraries.

[0328] The device functions as an interface to the user and collects composite data, including emotional data. For example, when a user gives a presentation in a meeting, the device records the audio and video and sends that data to a server.

[0329] Users view feedback generated by the server through their device. This feedback is based on analyzed emotional states and can provide specific advice and emotional support to help improve work efficiency. For example, if a user shows signs of nervousness during a presentation, the server might offer specific advice such as, "Try taking some deep breaths to ease your tension."

[0330] An example of a prompt might be: "Provide appropriate feedback based on the user's emotional state during the presentation. Include specific advice, especially if tension or excitement is observed." This instruction could be input into the AI ​​model.

[0331] In this way, the system aims to improve work performance by providing more personalized support while taking into account the user's emotional state.

[0332] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0333] Step 1:

[0334] The device collects audio and video data emitted by the user during work. Specifically, it uses the device's microphone and camera to record the user's speech and facial expressions in real time. This input data is important for evaluating the user's emotional state. The collected audio and video data is prepared for analysis after noise is removed using noise cancellation technology.

[0335] Step 2:

[0336] The device transmits the collected audio and video data to the server. Encryption technology is used during the transmission process to ensure data integrity and security. This transmitted data forms the basis for subsequent sentiment analysis and processing.

[0337] Step 3:

[0338] The server begins analyzing the received audio and video data using an emotion recognition engine. Specifically, it converts the audio data into text using natural language processing and identifies emotions based on the content. Furthermore, it analyzes the video data using computer vision technology, identifying emotional states by evaluating the user's facial expressions and body gestures. The output of this analysis is a detailed assessment of the user's emotions, such as joy, tension, and excitement.

[0339] Step 4:

[0340] Based on the analysis results, the server uses a generative AI model to create feedback to provide to the user. At this stage, it generates appropriate advice and training content based on the recognized emotional state. For example, if the analysis results indicate tension, it generates specific feedback such as, "To relieve tension, try the following breathing exercise." This feedback is output in text format.

[0341] Step 5:

[0342] The user receives feedback from the server via their device. The device displays the received feedback for the user to review. In this step, the user is expected to actually use the feedback in their work. Based on the advice provided, the user adjusts their behavior during work, aiming to improve their emotional state and work efficiency.

[0343] (Application Example 2)

[0344] 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."

[0345] In many commercial facilities and service providers, the emotions of staff members significantly impact customer satisfaction. Traditional systems fail to provide real-time feedback based on staff members' emotional states, leading to decreased operational efficiency and customer service capabilities. Therefore, to improve the quality of the customer experience, there is a need for a system that appropriately analyzes staff members' emotional states and provides effective feedback in real time.

[0346] 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.

[0347] In this invention, the server includes means for collecting information on operations completed by the person in charge of operations; means for analyzing the information and generating an evaluation result for the operations; means for generating training content suitable for the person in charge based on the evaluation result; means for analyzing the emotional state of the person in charge of operations; and means for providing real-time feedback during operations based on the emotional state. This enables real-time feedback that takes into account the emotional state of the person in charge of operations, thereby improving customer service capabilities.

[0348] A "business representative" refers to a person whose job is to interact with customers at a commercial facility or service provider.

[0349] "Information related to operations" refers to specific data about the tasks and activities that employees perform on a daily basis.

[0350] "Evaluation results" refer to judgments regarding the performance and efficiency of tasks, based on analysis of information related to operations.

[0351] "Educational content" refers to training programs and instruction provided to improve the skills and operational efficiency of employees.

[0352] "Emotional state" refers to the current mental and emotional state of the person performing the task, and includes emotions such as tension, joy, and anxiety.

[0353] "Real-time feedback" refers to evaluations and advice provided immediately to employees while they are performing their tasks, with the aim of improving operations and enhancing the quality of customer service.

[0354] This system aims to improve the performance of business personnel by efficiently managing their emotional state in customer interactions and providing real-time feedback.

[0355] The server collects operational information from smart glasses or devices carried by the service provider. The smart glasses have a built-in camera and microphone to record the service provider's voice and video through interactions with customers. This data is transmitted to the device via Bluetooth and then uploaded to the server. The server uses generative AI models such as Google Cloud's Natural Language API and Amazon Rekognition to analyze the voice and video in real time to identify the service provider's emotional state.

[0356] After analyzing the employee's emotions, the server generates appropriate training content for the employee and displays it on the smart glasses or device screen. For example, if the system detects that the employee is feeling nervous, advice such as "Take a deep breath and try to relax" might appear on the glasses. This encourages the employee to interact with customers more naturally.

[0357] For example, if the person in charge of the task is a newly hired barista at a cafe, real-time feedback can provide specific advice on how to respond with a smile when a customer asks a particular question. An example of a prompt to the generative AI model would be, "Please provide advice on how to respond with a smile when a customer asks a specific question."

[0358] In this way, the system can analyze emotional states in real time and provide feedback to staff, thereby improving customer service capabilities.

[0359] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0360] Step 1:

[0361] The terminal acquires audio and video data from smart glasses worn by the employee. This data is used to capture the employee's actions and facial expressions during work. The input is data from the smart glasses, and the output is audio and video data stored on the terminal.

[0362] Step 2:

[0363] The terminal transmits the acquired audio and video data to the server using Bluetooth. This process is the transfer of data to prepare it for analysis. The input is the audio and video data stored on the terminal, and the output is the data transferred to the server.

[0364] Step 3:

[0365] The server analyzes the received audio and video data. This analysis uses generative AI models such as Google Cloud's Natural Language API and Amazon Rekognition. The input is the data transferred to the server, and the output is an evaluation of emotional state, including the generated analysis data. Specifically, it performs emotion extraction from audio and facial expression recognition from video.

[0366] Step 4:

[0367] The server generates appropriate feedback and training content for the employee based on the analyzed emotional state. The input is the analyzed data, and the output is the generated feedback content. Specifically, it generates text information and constructs prompt messages to provide to the employee.

[0368] Step 5:

[0369] The server sends the generated feedback to the terminal and displays it on the smart glasses' screen. The input is the generated feedback content, and the output is the feedback presented to the person performing the task. Specific actions include displaying text on the screen and executing simple animations.

[0370] Step 6:

[0371] Users (business personnel) adjust their customer interactions based on the real-time feedback provided. This feedback serves as a guideline for business personnel to confidently interact with customers during their work. Implementing feedback improves the quality of work.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] [Third Embodiment]

[0376] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0377] 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.

[0378] 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).

[0379] 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.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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".

[0388] This invention constructs a system that provides real-time feedback and optimized training content for the tasks performed by individual users. This system mainly consists of a server, terminals, and users.

[0389] The server plays a central role in processing data related to the tasks performed by the user. Specifically, the server receives audio, video, or text data sent from the terminal and analyzes it using natural language processing and machine learning technologies. Based on the results of this analysis, the server generates feedback for the user and selects the optimal training content considering the user's past data and performance.

[0390] The terminal functions as an interface for users performing their work. For example, it records the audio of a user giving a presentation and sends the data to a server. In this process, a secure protocol is typically used to ensure privacy. Additionally, the generated feedback and training content are sent to the terminal for the user to review.

[0391] Users can improve their work based on the feedback provided and implement recommended training. For example, immediately after a new employee finishes a presentation, the server analyzes it and generates feedback such as "making the introduction more concise would be more effective," and then training to improve introduction skills is provided via the terminal.

[0392] This system allows users to quickly and efficiently hone their skills and improve the quality of their work.

[0393] The following describes the processing flow.

[0394] Step 1:

[0395] Once the user completes a task, the terminal collects data related to that task. The data collected here includes audio information, video footage, and text content, which are selected based on the type of task.

[0396] Step 2:

[0397] The device sends the collected data to the server. Data transmission is typically done via security protocols, ensuring data integrity and privacy.

[0398] Step 3:

[0399] The server analyzes the received data. Analysis methods include converting audio data into text using natural language processing and speech recognition technologies, and then extracting information and evaluating its structure. In particular, keywords and their frequency of use are counted to measure the level of understanding of the business content.

[0400] Step 4:

[0401] The server generates feedback based on the analysis results. This includes specific comments on the strengths and areas for improvement of the work, as well as quantitative evaluations (e.g., presentation time allocation).

[0402] Step 5:

[0403] The server selects the optimal training content based on the user's past work history and feedback records. It utilizes machine learning algorithms to identify the user's weaknesses and areas for improvement, creating an optimized training plan.

[0404] Step 6:

[0405] The server sends generated feedback and recommended training content to the device. The device receives this information and notifies the user. The user can access this information to view details.

[0406] Step 7:

[0407] Users review notifications and improve their work processes based on the feedback and training content provided. Training plans are implemented as needed to enhance skills.

[0408] (Example 1)

[0409] 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."

[0410] To effectively improve the diverse tasks performed by individual users, it is essential to provide feedback and training tailored to each task and user's characteristics. However, conventional systems have difficulty providing real-time feedback, and have been insufficient in suggesting optimal training content using past data. Thus, the challenge lies in providing rapid and accurate support for improving users' skills.

[0411] 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.

[0412] In this invention, the server includes means for recording actions related to tasks performed by the user and collecting data; means for analyzing the data and generating feedback based on the task content; and means for generating optimal training content based on the feedback and past performance data. This enables the provision of rapid and accurate feedback and training content for tasks performed by the user.

[0413] "User" refers to an individual or organization that uses this system to improve its operations.

[0414] "Business activities" refers to a series of activities that users perform on a daily basis, including actions taken to achieve a specific purpose.

[0415] "Feedback" refers to information that indicates evaluations and areas for improvement regarding the results and processes of tasks performed by users.

[0416] "Training content" refers to educational resources and programs recommended to improve users' skills and knowledge.

[0417] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and generate human language.

[0418] "Machine learning technology" refers to the ability of computers to learn from data and improve, and is used for prediction and pattern recognition.

[0419] "Data" refers to information such as audio, text, and video that is generated or collected in connection with the user's business operations.

[0420] This invention is a system for improving the tasks performed by users and for rapidly and effectively enhancing their skills. The system consists of three main components: a server, a terminal, and a user.

[0421] First, the terminal functions as an interface for users to perform their work. For example, when a user is giving a presentation, the terminal records the audio and sends it to the server in real time. The data is sent using a secure protocol, ensuring privacy.

[0422] The server receives and analyzes audio, video, and text data sent from terminals. It utilizes natural language processing and machine learning technologies to analyze the data and generate feedback on the user's work. The analyzed information indicates areas where the user should improve their work, and the server selects the optimal training content based on past performance data.

[0423] On the other hand, users can review the feedback provided by the server and improve their work. For example, after a presentation, they might receive feedback such as "shorten the introduction," and specific training to address this will be provided through their device.

[0424] As a concrete example, one prompt that utilizes a generative AI model might be, "Please give me some specific tips on how to improve my presentation." Users can input such prompts and immediately receive feedback and training information.

[0425] This system enables users to improve the quality of their work and continuously enhance their individual skills.

[0426] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0427] Step 1:

[0428] The terminal records the user's activities while they are performing their work. It collects data such as audio and video in real time as input. Specifically, it records audio during a presentation and sends that data to the server using a secure protocol. The output is the recorded data file.

[0429] Step 2:

[0430] The server receives audio data from the terminal as input. The server uses natural language processing techniques to convert the audio data into text data. At this stage, a speech recognition algorithm is used to output the result converted into text information. Specifically, the recognized text data is passed on to the next analysis step.

[0431] Step 3:

[0432] The server analyzes the converted text data as input and generates feedback related to the task. It utilizes a generative AI model to evaluate user performance from the text and identify areas for improvement. As output, it generates specific feedback for that task. A concrete example of this is feedback such as, "The introduction is too long; it would be more effective to halve the time."

[0433] Step 4:

[0434] The server selects the optimal training content based on feedback and the user's past performance data. Feedback and historical data are used as input. A machine learning model outputs a training program to improve the user's skills. Specifically, a particular training module for improving introduction skills is selected.

[0435] Step 5:

[0436] The device receives feedback and training content sent from the server. These are used as input and displayed in a user-friendly format. As output, the user reviews this information and decides on their next action. Specifically, the UI / UX ensures that feedback is displayed on the screen, providing an environment where the user can access it immediately.

[0437] Step 6:

[0438] The user takes action to improve their work based on the feedback and training content provided. As input, they receive all the provided information. As output, the user improves the quality of their work and moves on to the next task with enhanced skills. Specifically, they might implement improvements to the introduction, as pointed out, for their next presentation.

[0439] (Application Example 1)

[0440] 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."

[0441] In modern manufacturing environments, there is a constant need to improve work efficiency and product quality. However, evaluating the performance of each worker and piece of machinery in real time and providing immediate, appropriate feedback based on that evaluation is a challenging task. As a result, optimization of work processes and quality improvements can be delayed. In factories in particular, rapid improvement of work processes directly impacts product production, making the resolution of these challenges essential.

[0442] 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.

[0443] In this invention, the server includes means for acquiring information about the tasks performed by individual users, means for analyzing the information and creating a response to the tasks, and means for creating training content tailored to the user based on the responses. This enables workers and machinery to immediately adjust their operations through real-time work evaluation and feedback, thereby improving product quality and work efficiency.

[0444] A "user" is an entity that uses the system to obtain information and receive training.

[0445] "Information" refers to data related to the tasks completed by the user, and is acquired in various forms, including audio and video.

[0446] "Means of acquisition" refers to methods and devices for collecting information, such as cameras and sensors.

[0447] "Means of analysis" refers to the process of analyzing acquired information and generating a response based on that analysis.

[0448] A "response" is feedback or instructions generated based on the analysis results.

[0449] "Training content" refers to educational content provided with the aim of improving users' skills and work processes.

[0450] A "recording device" refers to hardware or software used to store information.

[0451] "Image recognition technology" is a technique for extracting features from image data and analyzing them.

[0452] "Mechanical equipment" refers to devices and robots used to perform specific tasks in factories and other similar facilities.

[0453] A "means of adjustment" is a mechanism that changes or corrects the operation of a device based on a response.

[0454] This invention provides a system for acquiring and analyzing information in real time to improve work efficiency and quality in workplaces such as factories. This system mainly consists of a server, a recording device, and mechanical devices.

[0455] The server plays a central role, primarily analyzing information and generating responses. It receives audio and video information transmitted from recording devices and analyzes it using a generative AI model. This model then generates responses and training content.

[0456] Recording devices primarily function as interfaces for acquiring information about the operation of machinery and the surrounding environment. For example, cameras and microphones attached to machinery operating in a factory capture the work in real time and transmit that information to a server. Secure protocols are used to ensure the safety of the data.

[0457] The machine adjusts and corrects its operation based on the response received from the server. For example, an assembly robot can improve work accuracy by adjusting the speed and angle at which it places parts. A specific example is optimizing the operation with the goal of increasing the rate at which parts are placed in the correct position to over 90%.

[0458] A concrete example of a response that might be generated is feedback such as, "It would be more effective to perform the introductory actions faster." This feedback allows the machine to speed up its operations, thereby improving production efficiency.

[0459] An example of a prompt would be, "Analyze a video of a robot assembling parts. The main objective is to obtain feedback to improve the accuracy and speed of the assembly." Using this prompt, the generative AI model can perform appropriate analysis and ultimately provide high-quality feedback.

[0460] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0461] Step 1:

[0462] The terminal captures the operation of machinery and equipment running in the factory in real time using cameras and microphones. This acquires audio and video data. This acquired data is then transmitted to the server using a secure protocol.

[0463] Step 2:

[0464] The server receives audio and video data from the terminal and analyzes it using a generated AI model. This allows for advanced data analysis using the acquired data in conjunction with prompt messages, extracting features related to work efficiency and quality.

[0465] Step 3:

[0466] The server generates a response to the user based on the analysis results. This response includes specific feedback, such as "It would be desirable to improve the assembly speed of the parts by 10%." The generated response is transmitted to the machine via the terminal.

[0467] Step 4:

[0468] The terminal receives a response sent from the server and applies it to the machine. Based on this response, the machine adjusts its own operation, optimizing its speed and accuracy. This allows the production line in the factory to improve its operation in real time.

[0469] Step 5:

[0470] Users can check the operational improvements of the machinery and equipment through their terminals. Based on this feedback, further training will be conducted to improve work efficiency and quality. Specifically, training content such as operation manuals and video materials will be provided.

[0471] 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.

[0472] This invention is a system that recognizes the emotions of users performing tasks and provides feedback and training content that takes those emotions into account. The system consists of a server, terminals, users, and an emotion recognition engine.

[0473] The server plays a central role in analyzing data transmitted from terminals. It works in conjunction with the emotion recognition engine to identify the user's emotional state using audio and video data. This emotion analysis is performed based on the user's facial expressions and tone of voice when giving presentations or entering information. The emotion recognition engine utilizes natural language processing and computer vision technologies to evaluate the user's emotions (e.g., joy, tension, excitement) in real time.

[0474] The device functions as an interface to the user and collects composite data, including emotional data. For example, when a user is giving a presentation in a meeting, the device records both audio and video and sends them to a server. Based on this data, the server performs emotion recognition.

[0475] Users view server-generated feedback on their devices. This feedback, based on analyzed emotional states, can include not only performance-based feedback but also emotional care and support. For example, if a user shows signs of nervousness during a presentation, the server might offer advice such as, "Try these techniques to ease your tension." Conversely, if the presentation is successful, feedback is provided to amplify the user's satisfaction.

[0476] In this way, the system takes the user's emotional state into account, providing more effective and personalized feedback and training content. This can improve the user's work efficiency and satisfaction.

[0477] The following describes the processing flow.

[0478] Step 1:

[0479] As the user begins work, the device collects the user's voice and video data in real time. This includes speech and facial movements during presentations.

[0480] Step 2:

[0481] The device sends the collected audio and video data to the server. This transmission utilizes a secure communication protocol to ensure data privacy.

[0482] Step 3:

[0483] The server analyzes the received data and uses an emotion recognition engine to determine the user's emotional state from their facial expressions and tone of voice. For example, machine learning algorithms can be used to identify joy or tension from changes in voice pitch and facial expressions.

[0484] Step 4:

[0485] The server generates feedback based on the analysis results. This feedback takes into account the user's emotional state and includes areas for improvement and points of praise. The feedback is designed to be sensitive to the user's feelings.

[0486] Step 5:

[0487] The server selects the optimal training content based on the user's emotional state and work history. It utilizes machine learning models based on historical data to recommend personalized training methods for each user.

[0488] Step 6:

[0489] The server generates feedback and training content, which is then sent to the terminal and presented to the user. The user reviews this information and uses the feedback to improve their work or conduct further training.

[0490] Step 7:

[0491] Users improve their skills by putting the feedback they receive into practice and completing recommended training. Through this process, they can receive more effective and emotionally sensitive support.

[0492] (Example 2)

[0493] 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."

[0494] The emotional state of users performing tasks significantly impacts work efficiency and results. However, conventional systems have struggled to provide specific and personalized feedback that takes into account the emotional state of users. In particular, integrating and analyzing data in multiple formats, such as audio and video, to accurately identify a user's emotional state is technically difficult and has not yielded sufficient results.

[0495] 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.

[0496] In this invention, the server includes means for collecting information about tasks completed by individual users, means for analyzing the information and generating a response to the tasks, and means for identifying the user's emotional state using the generated audio and video information. This makes it possible to provide personalized feedback that corresponds to the user's emotional state.

[0497] "User" refers to an individual or organization that uses the system to perform business operations.

[0498] "Information" is a general term that includes data related to the user, such as audio data, video data, and text data.

[0499] "Response" refers to feedback or advice given to the user based on the analyzed information.

[0500] "Emotional state" is a concept that refers to the result of identifying and identifying the emotional responses and conditions exhibited by a user.

[0501] "Means" refers to a method, device, or mechanism used to achieve a specific function or purpose.

[0502] "Personalized feedback" refers to responses that are customized based on the emotional state and work content of each individual user.

[0503] This invention is a system that recognizes the emotional state of users performing tasks in real time and provides appropriate feedback and training content. The system consists of a server, terminals, users, and an emotion recognition engine.

[0504] The server plays a central role in the system, analyzing audio and video data transmitted from terminals. This data is analyzed through an emotion recognition engine that utilizes natural language processing and computer vision technologies. Specific hardware components include a camera, microphone, and a high-performance processor. Software components include natural language processing libraries and image analysis libraries.

[0505] The device functions as an interface to the user and collects composite data, including emotional data. For example, when a user gives a presentation in a meeting, the device records the audio and video and sends that data to a server.

[0506] Users view feedback generated by the server through their device. This feedback is based on analyzed emotional states and can provide specific advice and emotional support to help improve work efficiency. For example, if a user shows signs of nervousness during a presentation, the server might offer specific advice such as, "Try taking some deep breaths to ease your tension."

[0507] An example of a prompt might be: "Provide appropriate feedback based on the user's emotional state during the presentation. Include specific advice, especially if tension or excitement is observed." This instruction could be input into the AI ​​model.

[0508] In this way, the system aims to improve work performance by providing more personalized support while taking into account the user's emotional state.

[0509] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0510] Step 1:

[0511] The device collects audio and video data emitted by the user during work. Specifically, it uses the device's microphone and camera to record the user's speech and facial expressions in real time. This input data is important for evaluating the user's emotional state. The collected audio and video data is prepared for analysis after noise is removed using noise cancellation technology.

[0512] Step 2:

[0513] The device transmits the collected audio and video data to the server. Encryption technology is used during the transmission process to ensure data integrity and security. This transmitted data forms the basis for subsequent sentiment analysis and processing.

[0514] Step 3:

[0515] The server begins analyzing the received audio and video data using an emotion recognition engine. Specifically, it converts the audio data into text using natural language processing and identifies emotions based on the content. Furthermore, it analyzes the video data using computer vision technology, identifying emotional states by evaluating the user's facial expressions and body gestures. The output of this analysis is a detailed assessment of the user's emotions, such as joy, tension, and excitement.

[0516] Step 4:

[0517] Based on the analysis results, the server uses a generative AI model to create feedback to provide to the user. At this stage, it generates appropriate advice and training content based on the recognized emotional state. For example, if the analysis results indicate tension, it generates specific feedback such as, "To relieve tension, try the following breathing exercise." This feedback is output in text format.

[0518] Step 5:

[0519] The user receives feedback from the server via their device. The device displays the received feedback for the user to review. In this step, the user is expected to actually use the feedback in their work. Based on the advice provided, the user adjusts their behavior during work, aiming to improve their emotional state and work efficiency.

[0520] (Application Example 2)

[0521] 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."

[0522] In many commercial facilities and service providers, the emotions of staff members significantly impact customer satisfaction. Traditional systems fail to provide real-time feedback based on staff members' emotional states, leading to decreased operational efficiency and customer service capabilities. Therefore, to improve the quality of the customer experience, there is a need for a system that appropriately analyzes staff members' emotional states and provides effective feedback in real time.

[0523] 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.

[0524] In this invention, the server includes means for collecting information on operations completed by the person in charge of operations; means for analyzing the information and generating an evaluation result for the operations; means for generating training content suitable for the person in charge based on the evaluation result; means for analyzing the emotional state of the person in charge of operations; and means for providing real-time feedback during operations based on the emotional state. This enables real-time feedback that takes into account the emotional state of the person in charge of operations, thereby improving customer service capabilities.

[0525] A "business representative" refers to a person whose job is to interact with customers at a commercial facility or service provider.

[0526] "Information related to operations" refers to specific data about the tasks and activities that employees perform on a daily basis.

[0527] "Evaluation results" refer to judgments regarding the performance and efficiency of tasks, based on analysis of information related to operations.

[0528] "Educational content" refers to training programs and instruction provided to improve the skills and operational efficiency of employees.

[0529] "Emotional state" refers to the current mental and emotional state of the person performing the task, and includes emotions such as tension, joy, and anxiety.

[0530] "Real-time feedback" refers to evaluations and advice provided immediately to employees while they are performing their tasks, with the aim of improving operations and enhancing the quality of customer service.

[0531] This system aims to improve the performance of business personnel by efficiently managing their emotional state in customer interactions and providing real-time feedback.

[0532] The server collects operational information from smart glasses or devices carried by the service provider. The smart glasses have a built-in camera and microphone to record the service provider's voice and video through interactions with customers. This data is transmitted to the device via Bluetooth and then uploaded to the server. The server uses generative AI models such as Google Cloud's Natural Language API and Amazon Rekognition to analyze the voice and video in real time to identify the service provider's emotional state.

[0533] After analyzing the employee's emotions, the server generates appropriate training content for the employee and displays it on the smart glasses or device screen. For example, if the system detects that the employee is feeling nervous, advice such as "Take a deep breath and try to relax" might appear on the glasses. This encourages the employee to interact with customers more naturally.

[0534] For example, if the person in charge of the task is a newly hired barista at a cafe, real-time feedback can provide specific advice on how to respond with a smile when a customer asks a particular question. An example of a prompt to the generative AI model would be, "Please provide advice on how to respond with a smile when a customer asks a specific question."

[0535] In this way, the system can analyze emotional states in real time and provide feedback to staff, thereby improving customer service capabilities.

[0536] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0537] Step 1:

[0538] The terminal acquires audio and video data from smart glasses worn by the employee. This data is used to capture the employee's actions and facial expressions during work. The input is data from the smart glasses, and the output is audio and video data stored on the terminal.

[0539] Step 2:

[0540] The terminal transmits the acquired audio and video data to the server using Bluetooth. This process is the transfer of data to prepare it for analysis. The input is the audio and video data stored on the terminal, and the output is the data transferred to the server.

[0541] Step 3:

[0542] The server analyzes the received audio and video data. This analysis uses generative AI models such as Google Cloud's Natural Language API and Amazon Rekognition. The input is the data transferred to the server, and the output is an evaluation of emotional state, including the generated analysis data. Specifically, it performs emotion extraction from audio and facial expression recognition from video.

[0543] Step 4:

[0544] The server generates appropriate feedback and training content for the employee based on the analyzed emotional state. The input is the analyzed data, and the output is the generated feedback content. Specifically, it generates text information and constructs prompt messages to provide to the employee.

[0545] Step 5:

[0546] The server sends the generated feedback to the terminal and displays it on the smart glasses' screen. The input is the generated feedback content, and the output is the feedback presented to the person performing the task. Specific actions include displaying text on the screen and executing simple animations.

[0547] Step 6:

[0548] Users (business personnel) adjust their customer interactions based on the real-time feedback provided. This feedback serves as a guideline for business personnel to confidently interact with customers during their work. Implementing feedback improves the quality of work.

[0549] 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.

[0550] 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.

[0551] 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.

[0552] [Fourth Embodiment]

[0553] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0554] 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.

[0555] 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).

[0556] 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.

[0557] 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.

[0558] 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).

[0559] 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.

[0560] 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.

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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".

[0566] This invention constructs a system that provides real-time feedback and optimized training content for the tasks performed by individual users. This system mainly consists of a server, terminals, and users.

[0567] The server plays a central role in processing data related to the tasks performed by the user. Specifically, the server receives audio, video, or text data sent from the terminal and analyzes it using natural language processing and machine learning technologies. Based on the results of this analysis, the server generates feedback for the user and selects the optimal training content considering the user's past data and performance.

[0568] The terminal functions as an interface for users performing their work. For example, it records the audio of a user giving a presentation and sends the data to a server. In this process, a secure protocol is typically used to ensure privacy. Additionally, the generated feedback and training content are sent to the terminal for the user to review.

[0569] Users can improve their work based on the feedback provided and implement recommended training. For example, immediately after a new employee finishes a presentation, the server analyzes it and generates feedback such as "making the introduction more concise would be more effective," and then training to improve introduction skills is provided via the terminal.

[0570] This system allows users to quickly and efficiently hone their skills and improve the quality of their work.

[0571] The following describes the processing flow.

[0572] Step 1:

[0573] Once the user completes a task, the terminal collects data related to that task. The data collected here includes audio information, video footage, and text content, which are selected based on the type of task.

[0574] Step 2:

[0575] The device sends the collected data to the server. Data transmission is typically done via security protocols, ensuring data integrity and privacy.

[0576] Step 3:

[0577] The server analyzes the received data. Analysis methods include converting audio data into text using natural language processing and speech recognition technologies, and then extracting information and evaluating its structure. In particular, keywords and their frequency of use are counted to measure the level of understanding of the business content.

[0578] Step 4:

[0579] The server generates feedback based on the analysis results. This includes specific comments on the strengths and areas for improvement of the work, as well as quantitative evaluations (e.g., presentation time allocation).

[0580] Step 5:

[0581] The server selects the optimal training content based on the user's past work history and feedback records. It utilizes machine learning algorithms to identify the user's weaknesses and areas for improvement, creating an optimized training plan.

[0582] Step 6:

[0583] The server sends generated feedback and recommended training content to the device. The device receives this information and notifies the user. The user can access this information to view details.

[0584] Step 7:

[0585] Users review notifications and improve their work processes based on the feedback and training content provided. Training plans are implemented as needed to enhance skills.

[0586] (Example 1)

[0587] 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".

[0588] To effectively improve the diverse tasks performed by individual users, it is essential to provide feedback and training tailored to each task and user's characteristics. However, conventional systems have difficulty providing real-time feedback, and have been insufficient in suggesting optimal training content using past data. Thus, the challenge lies in providing rapid and accurate support for improving users' skills.

[0589] 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.

[0590] In this invention, the server includes means for recording actions related to tasks performed by the user and collecting data; means for analyzing the data and generating feedback based on the task content; and means for generating optimal training content based on the feedback and past performance data. This enables the provision of rapid and accurate feedback and training content for tasks performed by the user.

[0591] "User" refers to an individual or organization that uses this system to improve its operations.

[0592] "Business activities" refers to a series of activities that users perform on a daily basis, including actions taken to achieve a specific purpose.

[0593] "Feedback" refers to information that indicates evaluations and areas for improvement regarding the results and processes of tasks performed by users.

[0594] "Training content" refers to educational resources and programs recommended to improve users' skills and knowledge.

[0595] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and generate human language.

[0596] "Machine learning technology" refers to the ability of computers to learn from data and improve, and is used for prediction and pattern recognition.

[0597] "Data" refers to information such as audio, text, and video that is generated or collected in connection with the user's business operations.

[0598] This invention is a system for improving the tasks performed by users and for rapidly and effectively enhancing their skills. The system consists of three main components: a server, a terminal, and a user.

[0599] First, the terminal functions as an interface for users to perform their work. For example, when a user is giving a presentation, the terminal records the audio and sends it to the server in real time. The data is sent using a secure protocol, ensuring privacy.

[0600] The server receives and analyzes audio, video, and text data sent from terminals. It utilizes natural language processing and machine learning technologies to analyze the data and generate feedback on the user's work. The analyzed information indicates areas where the user should improve their work, and the server selects the optimal training content based on past performance data.

[0601] On the other hand, users can review the feedback provided by the server and improve their work. For example, after a presentation, they might receive feedback such as "shorten the introduction," and specific training to address this will be provided through their device.

[0602] As a concrete example, one prompt that utilizes a generative AI model might be, "Please give me some specific tips on how to improve my presentation." Users can input such prompts and immediately receive feedback and training information.

[0603] This system enables users to improve the quality of their work and continuously enhance their individual skills.

[0604] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0605] Step 1:

[0606] The terminal records the user's activities while they are performing their work. It collects data such as audio and video in real time as input. Specifically, it records audio during a presentation and sends that data to the server using a secure protocol. The output is the recorded data file.

[0607] Step 2:

[0608] The server receives audio data from the terminal as input. The server uses natural language processing techniques to convert the audio data into text data. At this stage, a speech recognition algorithm is used to output the result converted into text information. Specifically, the recognized text data is passed on to the next analysis step.

[0609] Step 3:

[0610] The server analyzes the converted text data as input and generates feedback related to the task. It utilizes a generative AI model to evaluate user performance from the text and identify areas for improvement. As output, it generates specific feedback for that task. A concrete example of this is feedback such as, "The introduction is too long; it would be more effective to halve the time."

[0611] Step 4:

[0612] The server selects the optimal training content based on feedback and the user's past performance data. Feedback and historical data are used as input. A machine learning model outputs a training program to improve the user's skills. Specifically, a particular training module for improving introduction skills is selected.

[0613] Step 5:

[0614] The device receives feedback and training content sent from the server. These are used as input and displayed in a user-friendly format. As output, the user reviews this information and decides on their next action. Specifically, the UI / UX ensures that feedback is displayed on the screen, providing an environment where the user can access it immediately.

[0615] Step 6:

[0616] The user takes action to improve their work based on the feedback and training content provided. As input, they receive all the provided information. As output, the user improves the quality of their work and moves on to the next task with enhanced skills. Specifically, they might implement improvements to the introduction, as pointed out, for their next presentation.

[0617] (Application Example 1)

[0618] 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".

[0619] In modern manufacturing environments, there is a constant need to improve work efficiency and product quality. However, evaluating the performance of each worker and piece of machinery in real time and providing immediate, appropriate feedback based on that evaluation is a challenging task. As a result, optimization of work processes and quality improvements can be delayed. In factories in particular, rapid improvement of work processes directly impacts product production, making the resolution of these challenges essential.

[0620] 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.

[0621] In this invention, the server includes means for acquiring information about the tasks performed by individual users, means for analyzing the information and creating a response to the tasks, and means for creating training content tailored to the user based on the responses. This enables workers and machinery to immediately adjust their operations through real-time work evaluation and feedback, thereby improving product quality and work efficiency.

[0622] A "user" is an entity that uses the system to obtain information and receive training.

[0623] "Information" refers to data related to the tasks completed by the user, and is acquired in various forms, including audio and video.

[0624] "Means of acquisition" refers to methods and devices for collecting information, such as cameras and sensors.

[0625] "Means of analysis" refers to the process of analyzing acquired information and generating a response based on that analysis.

[0626] A "response" is feedback or instructions generated based on the analysis results.

[0627] "Training content" refers to educational content provided with the aim of improving users' skills and work processes.

[0628] A "recording device" refers to hardware or software used to store information.

[0629] "Image recognition technology" is a technique for extracting features from image data and analyzing them.

[0630] "Mechanical equipment" refers to devices and robots used to perform specific tasks in factories and other similar facilities.

[0631] A "means of adjustment" is a mechanism that changes or corrects the operation of a device based on a response.

[0632] This invention provides a system for acquiring and analyzing information in real time to improve work efficiency and quality in workplaces such as factories. This system mainly consists of a server, a recording device, and mechanical devices.

[0633] The server plays a central role, primarily analyzing information and generating responses. It receives audio and video information transmitted from recording devices and analyzes it using a generative AI model. This model then generates responses and training content.

[0634] Recording devices primarily function as interfaces for acquiring information about the operation of machinery and the surrounding environment. For example, cameras and microphones attached to machinery operating in a factory capture the work in real time and transmit that information to a server. Secure protocols are used to ensure the safety of the data.

[0635] The machine adjusts and corrects its operation based on the response received from the server. For example, an assembly robot can improve work accuracy by adjusting the speed and angle at which it places parts. A specific example is optimizing the operation with the goal of increasing the rate at which parts are placed in the correct position to over 90%.

[0636] A concrete example of a response that might be generated is feedback such as, "It would be more effective to perform the introductory actions faster." This feedback allows the machine to speed up its operations, thereby improving production efficiency.

[0637] An example of a prompt would be, "Analyze a video of a robot assembling parts. The main objective is to obtain feedback to improve the accuracy and speed of the assembly." Using this prompt, the generative AI model can perform appropriate analysis and ultimately provide high-quality feedback.

[0638] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0639] Step 1:

[0640] The terminal captures the operation of machinery and equipment running in the factory in real time using cameras and microphones. This acquires audio and video data. This acquired data is then transmitted to the server using a secure protocol.

[0641] Step 2:

[0642] The server receives audio and video data from the terminal and analyzes it using a generated AI model. This allows for advanced data analysis using the acquired data in conjunction with prompt messages, extracting features related to work efficiency and quality.

[0643] Step 3:

[0644] The server generates a response to the user based on the analysis results. This response includes specific feedback, such as "It would be desirable to improve the assembly speed of the parts by 10%." The generated response is transmitted to the machine via the terminal.

[0645] Step 4:

[0646] The terminal receives a response sent from the server and applies it to the machine. Based on this response, the machine adjusts its own operation, optimizing its speed and accuracy. This allows the production line in the factory to improve its operation in real time.

[0647] Step 5:

[0648] Users can check the operational improvements of the machinery and equipment through their terminals. Based on this feedback, further training will be conducted to improve work efficiency and quality. Specifically, training content such as operation manuals and video materials will be provided.

[0649] 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.

[0650] This invention is a system that recognizes the emotions of users performing tasks and provides feedback and training content that takes those emotions into account. The system consists of a server, terminals, users, and an emotion recognition engine.

[0651] The server plays a central role in analyzing data transmitted from terminals. It works in conjunction with the emotion recognition engine to identify the user's emotional state using audio and video data. This emotion analysis is performed based on the user's facial expressions and tone of voice when giving presentations or entering information. The emotion recognition engine utilizes natural language processing and computer vision technologies to evaluate the user's emotions (e.g., joy, tension, excitement) in real time.

[0652] The device functions as an interface to the user and collects composite data, including emotional data. For example, when a user is giving a presentation in a meeting, the device records both audio and video and sends them to a server. Based on this data, the server performs emotion recognition.

[0653] Users view server-generated feedback on their devices. This feedback, based on analyzed emotional states, can include not only performance-based feedback but also emotional care and support. For example, if a user shows signs of nervousness during a presentation, the server might offer advice such as, "Try these techniques to ease your tension." Conversely, if the presentation is successful, feedback is provided to amplify the user's satisfaction.

[0654] In this way, the system takes the user's emotional state into account, providing more effective and personalized feedback and training content. This can improve the user's work efficiency and satisfaction.

[0655] The following describes the processing flow.

[0656] Step 1:

[0657] As the user begins work, the device collects the user's voice and video data in real time. This includes speech and facial movements during presentations.

[0658] Step 2:

[0659] The device sends the collected audio and video data to the server. This transmission utilizes a secure communication protocol to ensure data privacy.

[0660] Step 3:

[0661] The server analyzes the received data and uses an emotion recognition engine to determine the user's emotional state from their facial expressions and tone of voice. For example, machine learning algorithms can be used to identify joy or tension from changes in voice pitch and facial expressions.

[0662] Step 4:

[0663] The server generates feedback based on the analysis results. This feedback takes into account the user's emotional state and includes areas for improvement and points of praise. The feedback is designed to be sensitive to the user's feelings.

[0664] Step 5:

[0665] The server selects the optimal training content based on the user's emotional state and work history. It utilizes machine learning models based on historical data to recommend personalized training methods for each user.

[0666] Step 6:

[0667] The server generates feedback and training content, which is then sent to the terminal and presented to the user. The user reviews this information and uses the feedback to improve their work or conduct further training.

[0668] Step 7:

[0669] Users improve their skills by putting the feedback they receive into practice and completing recommended training. Through this process, they can receive more effective and emotionally sensitive support.

[0670] (Example 2)

[0671] 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".

[0672] The emotional state of users performing tasks significantly impacts work efficiency and results. However, conventional systems have struggled to provide specific and personalized feedback that takes into account the emotional state of users. In particular, integrating and analyzing data in multiple formats, such as audio and video, to accurately identify a user's emotional state is technically difficult and has not yielded sufficient results.

[0673] 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.

[0674] In this invention, the server includes means for collecting information about tasks completed by individual users, means for analyzing the information and generating a response to the tasks, and means for identifying the user's emotional state using the generated audio and video information. This makes it possible to provide personalized feedback that corresponds to the user's emotional state.

[0675] "User" refers to an individual or organization that uses the system to perform business operations.

[0676] "Information" is a general term that includes data related to the user, such as audio data, video data, and text data.

[0677] "Response" refers to feedback or advice given to the user based on the analyzed information.

[0678] "Emotional state" is a concept that refers to the result of identifying and identifying the emotional responses and conditions exhibited by a user.

[0679] "Means" refers to a method, device, or mechanism used to achieve a specific function or purpose.

[0680] "Personalized feedback" refers to responses that are customized based on the emotional state and work content of each individual user.

[0681] This invention is a system that recognizes the emotional state of users performing tasks in real time and provides appropriate feedback and training content. The system consists of a server, terminals, users, and an emotion recognition engine.

[0682] The server plays a central role in the system, analyzing audio and video data transmitted from terminals. This data is analyzed through an emotion recognition engine that utilizes natural language processing and computer vision technologies. Specific hardware components include a camera, microphone, and a high-performance processor. Software components include natural language processing libraries and image analysis libraries.

[0683] The device functions as an interface to the user and collects composite data, including emotional data. For example, when a user gives a presentation in a meeting, the device records the audio and video and sends that data to a server.

[0684] Users view feedback generated by the server through their device. This feedback is based on analyzed emotional states and can provide specific advice and emotional support to help improve work efficiency. For example, if a user shows signs of nervousness during a presentation, the server might offer specific advice such as, "Try taking some deep breaths to ease your tension."

[0685] An example of a prompt might be: "Provide appropriate feedback based on the user's emotional state during the presentation. Include specific advice, especially if tension or excitement is observed." This instruction could be input into the AI ​​model.

[0686] In this way, the system aims to improve work performance by providing more personalized support while taking into account the user's emotional state.

[0687] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0688] Step 1:

[0689] The device collects audio and video data emitted by the user during work. Specifically, it uses the device's microphone and camera to record the user's speech and facial expressions in real time. This input data is important for evaluating the user's emotional state. The collected audio and video data is prepared for analysis after noise is removed using noise cancellation technology.

[0690] Step 2:

[0691] The device transmits the collected audio and video data to the server. Encryption technology is used during the transmission process to ensure data integrity and security. This transmitted data forms the basis for subsequent sentiment analysis and processing.

[0692] Step 3:

[0693] The server begins analyzing the received audio and video data using an emotion recognition engine. Specifically, it converts the audio data into text using natural language processing and identifies emotions based on the content. Furthermore, it analyzes the video data using computer vision technology, identifying emotional states by evaluating the user's facial expressions and body gestures. The output of this analysis is a detailed assessment of the user's emotions, such as joy, tension, and excitement.

[0694] Step 4:

[0695] Based on the analysis results, the server uses a generative AI model to create feedback to provide to the user. At this stage, it generates appropriate advice and training content based on the recognized emotional state. For example, if the analysis results indicate tension, it generates specific feedback such as, "To relieve tension, try the following breathing exercise." This feedback is output in text format.

[0696] Step 5:

[0697] The user receives feedback from the server via their device. The device displays the received feedback for the user to review. In this step, the user is expected to actually use the feedback in their work. Based on the advice provided, the user adjusts their behavior during work, aiming to improve their emotional state and work efficiency.

[0698] (Application Example 2)

[0699] 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".

[0700] In many commercial facilities and service providers, the emotions of staff members significantly impact customer satisfaction. Traditional systems fail to provide real-time feedback based on staff members' emotional states, leading to decreased operational efficiency and customer service capabilities. Therefore, to improve the quality of the customer experience, there is a need for a system that appropriately analyzes staff members' emotional states and provides effective feedback in real time.

[0701] 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.

[0702] In this invention, the server includes means for collecting information on operations completed by the person in charge of operations; means for analyzing the information and generating an evaluation result for the operations; means for generating training content suitable for the person in charge based on the evaluation result; means for analyzing the emotional state of the person in charge of operations; and means for providing real-time feedback during operations based on the emotional state. This enables real-time feedback that takes into account the emotional state of the person in charge of operations, thereby improving customer service capabilities.

[0703] A "business representative" refers to a person whose job is to interact with customers at a commercial facility or service provider.

[0704] "Information related to operations" refers to specific data about the tasks and activities that employees perform on a daily basis.

[0705] "Evaluation results" refer to judgments regarding the performance and efficiency of tasks, based on analysis of information related to operations.

[0706] "Educational content" refers to training programs and instruction provided to improve the skills and operational efficiency of employees.

[0707] "Emotional state" refers to the current mental and emotional state of the person performing the task, and includes emotions such as tension, joy, and anxiety.

[0708] "Real-time feedback" refers to evaluations and advice provided immediately to employees while they are performing their tasks, with the aim of improving operations and enhancing the quality of customer service.

[0709] This system aims to improve the performance of business personnel by efficiently managing their emotional state in customer interactions and providing real-time feedback.

[0710] The server collects operational information from smart glasses or devices carried by the service provider. The smart glasses have a built-in camera and microphone to record the service provider's voice and video through interactions with customers. This data is transmitted to the device via Bluetooth and then uploaded to the server. The server uses generative AI models such as Google Cloud's Natural Language API and Amazon Rekognition to analyze the voice and video in real time to identify the service provider's emotional state.

[0711] After analyzing the employee's emotions, the server generates appropriate training content for the employee and displays it on the smart glasses or device screen. For example, if the system detects that the employee is feeling nervous, advice such as "Take a deep breath and try to relax" might appear on the glasses. This encourages the employee to interact with customers more naturally.

[0712] For example, if the person in charge of the task is a newly hired barista at a cafe, real-time feedback can provide specific advice on how to respond with a smile when a customer asks a particular question. An example of a prompt to the generative AI model would be, "Please provide advice on how to respond with a smile when a customer asks a specific question."

[0713] In this way, the system can analyze emotional states in real time and provide feedback to staff, thereby improving customer service capabilities.

[0714] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0715] Step 1:

[0716] The terminal acquires audio and video data from smart glasses worn by the employee. This data is used to capture the employee's actions and facial expressions during work. The input is data from the smart glasses, and the output is audio and video data stored on the terminal.

[0717] Step 2:

[0718] The terminal transmits the acquired audio and video data to the server using Bluetooth. This process is the transfer of data to prepare it for analysis. The input is the audio and video data stored on the terminal, and the output is the data transferred to the server.

[0719] Step 3:

[0720] The server analyzes the received audio and video data. This analysis uses generative AI models such as Google Cloud's Natural Language API and Amazon Rekognition. The input is the data transferred to the server, and the output is an evaluation of emotional state, including the generated analysis data. Specifically, it performs emotion extraction from audio and facial expression recognition from video.

[0721] Step 4:

[0722] The server generates appropriate feedback and training content for the employee based on the analyzed emotional state. The input is the analyzed data, and the output is the generated feedback content. Specifically, it generates text information and constructs prompt messages to provide to the employee.

[0723] Step 5:

[0724] The server sends the generated feedback to the terminal and displays it on the smart glasses' screen. The input is the generated feedback content, and the output is the feedback presented to the person performing the task. Specific actions include displaying text on the screen and executing simple animations.

[0725] Step 6:

[0726] Users (business personnel) adjust their customer interactions based on the real-time feedback provided. This feedback serves as a guideline for business personnel to confidently interact with customers during their work. Implementing feedback improves the quality of work.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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."

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] The following is further disclosed regarding the embodiments described above.

[0749] (Claim 1)

[0750] A means of collecting data on tasks completed by individual users,

[0751] A means for analyzing the aforementioned data and generating feedback for the work,

[0752] A means for generating training content suitable for the user based on the aforementioned feedback,

[0753] Means for providing the aforementioned feedback and training content to the user,

[0754] A system that includes this.

[0755] (Claim 2)

[0756] The system according to claim 1, further comprising means for converting and analyzing data, including audio data, into natural language.

[0757] (Claim 3)

[0758] The system according to claim 1, further comprising means for making predictions using machine learning technology based on the user's past work history and feedback history, and providing optimal training content based on the prediction.

[0759] "Example 1"

[0760] (Claim 1)

[0761] A means of recording actions related to tasks performed by users and collecting data,

[0762] A means for analyzing the aforementioned data and generating feedback based on the work content,

[0763] A means for generating optimal training content based on the aforementioned feedback and past performance data,

[0764] A means of providing the aforementioned feedback and training content to the user in real time,

[0765] A system that includes this.

[0766] (Claim 2)

[0767] The system according to claim 1, further comprising means for analyzing speech data and converting it into text data using natural language processing technology.

[0768] (Claim 3)

[0769] The system according to claim 1, further comprising a means for making predictions useful for future work based on the user's past work history and feedback history using machine learning technology, and providing optimal training content based on the predictions.

[0770] "Application Example 1"

[0771] (Claim 1)

[0772] A means of obtaining information about the tasks completed by individual users,

[0773] A means for analyzing the aforementioned information and creating a response to the task,

[0774] Based on the above response, a means for creating training content tailored to the user,

[0775] Means for providing the aforementioned response and training content to the user,

[0776] A means for acquiring the aforementioned information from a recording device and analyzing it using image recognition technology,

[0777] Means for adjusting the operation of a machine based on the analysis results,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, further comprising means for converting and analyzing information, including audio and video information, into natural language.

[0781] (Claim 3)

[0782] The system according to claim 1, further comprising means for making predictions using machine learning technology based on the user's past work history and response history, and providing optimal training content based on the prediction.

[0783] "Example 2 of combining an emotion engine"

[0784] (Claim 1)

[0785] A means of collecting information about tasks completed by individual users,

[0786] A means for analyzing the aforementioned information and generating a response to the task,

[0787] A means for generating training content suitable for the user based on the above response,

[0788] Means for providing the aforementioned response and training content to the user,

[0789] A means for identifying the user's emotional state using generated audio and video information,

[0790] A means of providing an individualized response tailored to the user's state based on identified emotional states,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, further comprising means for converting and analyzing information, including audio information, into natural language.

[0794] (Claim 3)

[0795] The system according to claim 1, further comprising means for making predictions using machine learning technology based on the user's past work history and response history, and providing optimal training content based on the prediction.

[0796] "Application example 2 when combining with an emotional engine"

[0797] (Claim 1)

[0798] A means of collecting information about operations completed by individual task personnel,

[0799] A means for analyzing the aforementioned information and generating evaluation results for the work,

[0800] Based on the aforementioned evaluation results, a means for generating training content suitable for the person in charge of the work,

[0801] A means of providing the aforementioned evaluation results and training content to the person in charge of the work,

[0802] A means of analyzing the emotional state of the person in charge of the work,

[0803] A means for providing real-time feedback during work based on the aforementioned emotional state,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, further comprising means for converting the information, including audio information, into a coded language and analyzing it.

[0807] (Claim 3)

[0808] The system according to claim 1, further comprising means for making predictions using machine learning technology based on the past operation history and evaluation result history of the person in charge of operations, and providing optimal training content based on the prediction. [Explanation of symbols]

[0809] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting data on tasks completed by individual users, A means for analyzing the aforementioned data and generating feedback for the work, A means for generating training content suitable for the user based on the aforementioned feedback, Means for providing the aforementioned feedback and training content to the user, A system that includes this.

2. The system according to claim 1, further comprising means for converting and analyzing data, including audio data, into natural language.

3. The system according to claim 1, further comprising means for making predictions using machine learning technology based on the user's past work history and feedback history, and providing optimal training content based on the prediction.

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