system

Abstract

We provide the system. [Solution] A means of collecting data and monitoring business processes, A means of recording the progress of a task in real time and transmitting the data, A means of analyzing data and conducting a self-assessment after task completion, A means for generating efficiency improvement proposals based on evaluation results, A means of collecting user feedback and optimizing the system, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern business environment, although improvements in business efficiency and the quality of work products are required, there is a problem that it is difficult to quickly improve with the fixed performance and limited feedback of conventional work automation tools. This problem, especially in a busy business scene, is a time-consuming process for manual evaluation and improvement, which hinders the continuous improvement of business efficiency. Therefore, an AI system that can flexibly and quickly perform self-improvement is needed.

Means for Solving the Problems

[0005] This invention is a system that collects data and monitors business processes in real time, recording and transmitting task progress, and performing a self-evaluation after task completion. The evaluation results are output as efficiency improvement suggestions, and further optimization is possible by collecting user feedback. In addition, this system compares past results with new information sources, identifies areas for improvement, and proposes the optimal approach for the next task by adjusting the algorithm, thereby improving business efficiency and the quality of deliverables.

[0006] "Data" refers to information and records related to business processes, and is an element used for system analysis and evaluation.

[0007] "Business process" is a comprehensive concept that refers to the steps and activities that a company or organization takes when carrying out its business operations.

[0008] "Monitoring" refers to the actions taken by a system to continuously observe the status of business processes and data, and to identify anomalies or areas for improvement.

[0009] A "task" refers to a unit of activity or action necessary to achieve a specific goal within a business process.

[0010] "Real-time" is a term that describes a situation where data and information are processed almost instantly and used without delay.

[0011] "Record keeping" refers to the act of retaining actions and results in a business process as data for later analysis and evaluation.

[0012] "Transmission" refers to the act of transferring collected data or information to other systems or users.

[0013] "Self-assessment" is the process by which a system analyzes its own activities and results and autonomously identifies areas that need improvement.

[0014] "Efficiency improvement proposal" refers to the act of presenting improvement plans for business processes and proposing more effective methods.

[0015] "Feedback" refers to the opinions and evaluations provided by users regarding the proposals and results of the system.

[0016] "Optimization" refers to the action of adjusting the configuration and algorithms to improve the performance and results of the system.

[0017] "Comparison" refers to the act of finding differences and commonalities by contrasting different information sources and past results.

[0018] "Improvement points" refer to specific areas or features in the current system or process that require improvement.

[0019] "Approach" refers to the means and methods selected for problem-solving and goal achievement.

Brief Explanation of Drawings

[0020] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0021] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0022] First, the language used in the following description will be explained.

[0023] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0024] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0026] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0027] 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."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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".

[0041] In order to implement this invention, it is necessary to effectively utilize the roles of the server, terminal, and user.

[0042] First, the server operates as a foundation with a specialized software configuration. The server collects information about the user's work environment and receives various business process data registered in the system. It also accesses APIs and external data sources to obtain necessary information. This allows the server to perform necessary data analysis in real time and support the automated task evaluation and suggestion generation by the AI ​​agent.

[0043] Next, the terminal functions as a user interface, serving as a platform for users to initiate or record tasks. The terminal continuously records the progress of business processes and transmits this information to the server. Users can review suggested efficiency improvements through the terminal and submit feedback from there.

[0044] Finally, the user is responsible for performing business tasks and evaluating the system's suggestions. Users are encouraged to review the efficiency suggestions presented on their terminal and provide feedback on their effectiveness. This feedback is collected on the server and used to further improve the AI ​​agent's algorithms.

[0045] For example, in project management tasks, users input task details and schedules using a terminal. The server analyzes this data and suggests new task lists and priorities to improve time management efficiency. These suggestions are displayed on the terminal, and users can use them to optimize their work processes, ultimately improving the overall efficiency of the project.

[0046] This system configuration enables efficient task management and continuous improvement in business operations. Through repeated self-evaluation and optimization as tasks are completed, it becomes possible to respond flexibly and quickly to changing business environments.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server collects user work environment information and configures the necessary settings for business processes. This includes configuring APIs and registering data formats.

[0050] Step 2:

[0051] The user uses their device to initiate a work task. The user opens a project management application and enters the task details and schedule.

[0052] Step 3:

[0053] The terminal records user input data and sends it to the server. This data is used to track the progress of business processes in real time.

[0054] Step 4:

[0055] The server analyzes the received data and has the AI ​​agent determine the status of the task. Here, the performance of the business process is evaluated, and areas requiring optimization are identified.

[0056] Step 5:

[0057] The AI ​​agent performs a self-assessment process, identifying areas for improvement while comparing them with past data. The server generates suggestions from the AI ​​agent and develops appropriate task management methods.

[0058] Step 6:

[0059] The device displays suggested efficiency improvements to the user. The displayed information includes specific steps for implementing the improvements.

[0060] Step 7:

[0061] Users review the proposals via their devices and provide feedback to the server. This feedback includes opinions on the usefulness of the proposals and any issues they may have.

[0062] Step 8:

[0063] The server analyzes user feedback and adjusts the AI ​​agent's algorithm based on that feedback. This adjustment optimizes subsequent tasks.

[0064] (Example 1)

[0065] 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."

[0066] In business activities, it is necessary to efficiently collect and analyze information and appropriately evaluate task priorities, but conventional systems do not adequately handle real-time information processing or improve the accuracy of suggestions. Therefore, there are challenges in optimizing business efficiency and continuously improving the system.

[0067] 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.

[0068] In this invention, the server includes means for collecting information and monitoring business procedures, means for evaluating and suggesting task priorities using a generative AI model, and means for updating algorithms based on feedback. This enables efficient information processing and continuous optimization of business processes in business activities.

[0069] "Collecting information" refers to the process of acquiring and storing business-related data in a system.

[0070] "Means of monitoring business procedures" refers to technologies for tracking the progress of business processes and understanding their status in real time.

[0071] "Using a generative AI model" refers to a method that utilizes artificial intelligence to analyze data and generate task priorities and efficiency improvement suggestions.

[0072] "Evaluating and proposing task priorities" refers to the function of determining the importance and urgency of each task based on collected information and suggesting the optimal work order.

[0073] "Updating algorithms based on feedback" refers to the process of continuously improving the system's analysis algorithms by taking into account evaluations and opinions from users.

[0074] In order to implement this invention, it is necessary to effectively utilize the roles of the server, terminal, and user. Specifically, the system is configured as follows.

[0075] The server functions as a platform for collecting business information and monitoring business procedures. It interacts with external data sources via APIs to retrieve necessary data. The primary software used by the server is a generative AI model, which is used to analyze the collected data and perform efficient task management and prioritization. The server also regularly updates this AI model based on feedback to improve the accuracy of its analysis.

[0076] The terminal provides a user interface and is a device for users to operate and record work tasks. The terminal displays efficiency suggestions from the server and accepts input from the user. This input, such as task progress and feedback on suggestions, is sent to the server in real time.

[0077] Users are expected to perform their daily tasks and improve their work efficiency by utilizing suggestions from this system. Users review suggestions presented by the server via their terminals, evaluate their effectiveness, and provide feedback. This feedback is analyzed by the server and used to improve the AI ​​model.

[0078] As a concrete example, in project management tasks, users input task details and schedules using a terminal. The server analyzes this data and uses a generative AI model to suggest new task lists and priorities. These suggestions are displayed on the terminal, and users can review them to optimize their work processes.

[0079] An example of a prompt message could be, "Analyze the project data entered by the user and provide suggestions for efficiently managing tasks," which could be input into the generating AI model. This system enables increased operational efficiency and continuous improvement, allowing businesses to adapt to changing environments.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The server collects business-related data from users. The input consists of business process data obtained from APIs and terminals. The server stores this data in a database and prepares it for subsequent analysis steps. Specifically, it performs data format conversion and normalization.

[0083] Step 2:

[0084] The server provides business data to the AI ​​model for analysis. The input is the business data collected in Step 1. The AI ​​model uses this data to generate task priorities and suggestions for efficiency improvements. The output is a priority list and efficiency improvement suggestions. Specifically, its operation includes data analysis and inference using machine learning algorithms.

[0085] Step 3:

[0086] The terminal receives suggestions sent from the server and displays them to the user. The input consists of analysis results and suggestions sent from the server. The terminal displays these on an interface in a user-friendly format. The output is the user-confirmed content of the suggestions. Specific actions include updating the graphical user interface.

[0087] Step 4:

[0088] Users review suggestions via their terminals and provide feedback on the execution of work tasks. Input is the suggestions displayed on the terminal. Users evaluate the suggestions and provide feedback on their effectiveness and areas for improvement. Output is the information sent to the server as feedback. Specific actions include filling out and submitting a feedback form.

[0089] Step 5:

[0090] The server receives feedback from the user and updates the AI ​​model. The input is the feedback sent by the user in step 4. The server analyzes the feedback and uses it as data to improve the AI ​​model's algorithm. The output is the improved AI model. Specific actions include tuning the algorithm's parameters and retraining.

[0091] (Application Example 1)

[0092] 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."

[0093] In today's production environment, there is a demand for optimization of work efficiency and quality control. However, there is a lack of technology to respond quickly through real-time data analysis and efficiency improvement suggestions. As a result, traditional production methods often result in inefficient processes, making it difficult to conduct optimal production activities in factory operations.

[0094] 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.

[0095] In this invention, the server includes means for analyzing the progress of a specific activity and presenting suggestions to the user terminal to improve work efficiency, and means for determining the optimal timing for preventative maintenance using information from sensors. This makes it possible to prevent production line stoppages and equipment malfunctions.

[0096] "Means for collecting data and monitoring business processes" refers to a system that automatically acquires all information related to business operations and continuously monitors their progress.

[0097] "A means of recording task progress in real time and transmitting data" refers to a system that immediately records the progress of a task being worked on and transmits the information to a central database.

[0098] "A means of analyzing data and conducting a self-assessment after task completion" refers to a method of evaluating the results at the time the task is completed and comparing them with past data.

[0099] "Methods for generating efficiency proposals based on evaluation results" refers to techniques for creating specific proposals to improve the efficiency of operations based on analysis results.

[0100] "Methods for collecting user feedback and optimizing the system" refers to the process of gathering user opinions and evaluations and using them to improve the entire system.

[0101] "A means of analyzing the progress of a specific activity and presenting suggestions to the user's terminal to improve work efficiency" refers to a method of analyzing the current state of an activity and informing the user of recommendations for improving work efficiency.

[0102] "A method for determining the optimal timing for preventative maintenance using information from sensors" refers to a technology that uses data obtained from various sensors to make decisions about when to perform equipment maintenance.

[0103] To implement this invention, it is necessary to build a system in which the server, terminal, and user each play a specific role. The server is at the core of data processing and analysis, collecting all information related to the business. The server integrates information from various sensors and performs data analysis using Python and AI models. The data is processed in real time, particularly to calculate the optimal timing for efficiency improvements and preventive maintenance on production lines.

[0104] The terminal functions as an interface between the user and the server. Smartphones or tablets are commonly used, allowing users to view suggestions and provide feedback in real time. Users can use the terminal to review streamlined activities and obtain information to improve the efficiency of their own work.

[0105] The user accepts the efficiency suggestions provided by the system and performs their work based on them. Furthermore, the user's feedback is sent to the server, which continuously optimizes the AI ​​algorithm.

[0106] For example, in a factory, sensors are installed to monitor machine vibrations and temperatures, and this data is analyzed on a server. As a result of the analysis, suggestions such as "The optimal time to replace part B in machine A is one week from now" are sent to the user's terminal.

[0107] An example of a prompt message might be, "Please provide instructions to minimize the expected production line downtime over the next week."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server collects data in real time from various sensors. It receives data such as temperature, vibration, and operating time from factory sensors as input. As output, it stores the raw data in a database, preparing it for subsequent processing. For data processing, it formats the data into the necessary format and converts it into time-based data points.

[0111] Step 2:

[0112] The server analyzes the collected data using Python and AI models (e.g., TENSORFLOW® or PyTorch). Formatted sensor data is used as input. The AI ​​model utilizes a generative AI model to evaluate the current state of the device. The output provides evaluation results regarding the operating status of the equipment. Machine learning algorithms are applied to the data to perform anomaly detection and predictive analysis.

[0113] Step 3:

[0114] The server generates efficiency improvement suggestions based on the analysis results and sends them to the terminal. It receives the evaluation results of the AI ​​model as input. As output, it generates specific suggestions for improving work efficiency and maintenance recommendations in text format. These suggestions can also function as prompts. For example, they might include statements like, "The recommended replacement time for part B in machine A is one week from now."

[0115] Step 4:

[0116] The terminal displays efficiency improvement suggestions received from the server to the user. It receives suggestions from the server in text format as input. As output, it displays the suggestions on the user interface in a format that is easy for the user to understand. Specifically, it provides information visually using graphs and lists to support the user in taking immediate action.

[0117] Step 5:

[0118] Users review suggestions displayed on their devices and perform tasks or submit feedback as needed. Input involves reviewing the suggested content. Output involves sending feedback to the server via the device after completing the task. Specifically, this includes entering comments regarding the effectiveness of the suggestions and areas for improvement.

[0119] Step 6:

[0120] The server receives feedback from users and uses it to optimize the AI ​​model. It receives user feedback data as input and generates an improved algorithm as output, which is then used for future analyses. Specifically, this involves adding the feedback to the data set and retraining the model.

[0121] 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.

[0122] This invention is a system that incorporates an emotion engine to recognize the user's emotional state and utilize it in business processes and task management. In this system, the roles of the server, terminal, and user work together to improve business efficiency and create a comfortable working environment for the user.

[0123] First, the server functions as the central hub for data collection and analysis. The server collects not only data related to business processes, but also emotional data based on users' facial expressions and voices, and analyzes it in real time. Furthermore, it combines this with historical data to understand current emotions and uses this information to optimize business tasks.

[0124] The terminal serves as a user interface, displaying work task data entered by the user and emotional information acquired by the emotion engine. This allows users to understand the impact of their emotional state on their work and enables them to make self-improvements based on that understanding.

[0125] While performing their tasks, users can manage their own emotional state using emotional data collected by the emotion engine. For example, if the emotion engine detects that a project deadline is approaching and the user is experiencing high stress, the server will suggest task redistribution or adjustments to reduce the user's burden. In this way, work processes are optimized based on the user's emotions.

[0126] As a concrete example, if the emotion engine detects a user's stress level, the terminal displays suggestions for relaxation techniques to reduce stress. The server selects efficient solutions from data from similar past situations and provides timely suggestions to the user, thereby achieving both work efficiency and the user's mental well-being. This system provides a new form of work management that utilizes emotional data.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server collects basic data related to business processes and performs initial setup to manage each user's work patterns and sentiment data. At this stage, connections to APIs and external data sources are also established.

[0130] Step 2:

[0131] Users initiate work tasks through their devices. This includes entering information about the task content, deadline, and priority.

[0132] Step 3:

[0133] An emotion engine operates on the device, recognizing the user's emotional state in real time from their facial expressions and voice. The obtained emotional data is transmitted to a server via the device.

[0134] Step 4:

[0135] The server analyzes the received emotional data and combines it with business data to evaluate the user's current emotional state. Based on this evaluation, improvement suggestions for streamlining operations are generated.

[0136] Step 5:

[0137] The device displays suggestions from the server to the user. These suggestions include things like changing task priorities and suggesting ways to relax, offering specific solutions tailored to the user's current emotional state.

[0138] Step 6:

[0139] The user reviews the suggestions on the device and adjusts their work processes accordingly. For example, if they are experiencing high stress levels, they can try the suggested relaxation techniques. The user then inputs the results as feedback into the device.

[0140] Step 7:

[0141] The server analyzes user feedback and adjusts the system-wide algorithms, including the emotion engine. This feedback is used to improve the accuracy of future suggestions.

[0142] Step 8:

[0143] Based on feedback, improved efficiency suggestions are generated and incorporated into the next work cycle. This continuously optimizes user emotional state and work performance.

[0144] (Example 2)

[0145] 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 will be referred to as the "terminal."

[0146] In today's work environment, employees' emotional states have a significant impact on work efficiency and productivity. However, existing systems lacked the means to analyze emotions in real time and optimize work processes based on users' feelings. Therefore, there is a need to provide efficient task management and work improvement methods that take emotional states into consideration.

[0147] 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.

[0148] In this invention, the server includes means for collecting data from a device to analyze emotional states, means for integrating emotional data and task data to optimize business processes, and means for displaying the analysis results through a user interface. This enables the optimization of business operations while taking emotional states into consideration.

[0149] "Emotional state" refers to data that indicates the emotions and psychological state that an individual user is experiencing at a specific point in time.

[0150] A "device" refers to a hardware device used to acquire emotional or work-related data.

[0151] A "server" refers to a computing system that centrally processes data collection, analysis, and integration.

[0152] A "user interface" refers to the software and hardware components that provide a means for a user to interact with a system.

[0153] "Efficiency improvement suggestions" refer to specific advice and methods generated based on user sentiment data to improve work processes and increase efficiency.

[0154] "Feedback" refers to information, including responses and reactions, collected from users, which helps improve the system.

[0155] An "algorithm" refers to a set of steps or calculations defined to solve a specific problem.

[0156] This invention provides a system that analyzes a user's emotional state in real time and integrates it with business tasks in order to improve efficiency in business processes. First, the server collects user emotional data from the device using speech recognition and facial recognition technologies. This uses hardware that captures emotional states, such as cameras and microphones. The server then uses machine learning platforms such as TensorFlow and PyTorch to analyze the collected data and understand the user's emotional state.

[0157] Next, the terminal acts as a user interface, displaying feedback from the server to the user. This information includes suggestions for work improvement based on the user's current emotional state, presented in a format that is easy for the user to understand directly. For example, if the user is experiencing high stress, specific suggestions such as relaxation techniques or task redistribution will be displayed on the terminal.

[0158] Users adjust their emotional state while performing their tasks, based on the provided work improvement suggestions. In particular, they can take actions to perform work tasks more efficiently by referring to the data detected by the emotion engine.

[0159] By utilizing generative AI models, the server can analyze patterns in past emotional data and business performance data to develop new approaches for optimizing future business processes. For example, a possible prompt might be: "Based on user emotional data, generate suggestions to improve the efficiency of task management. Specifically, provide task redistribution suggestions for when the user is in a high-stress state."

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The server collects audio and video data from the device to analyze the user's emotional state. The input consists of emotionally expressive audio and facial images, and a machine learning algorithm is used to extract emotional attributes based on this data. The output is the user's emotional state at a specific time (e.g., stress level, relaxation level). Specifically, real-time data is acquired through the camera and microphone and sent to the server for analysis.

[0163] Step 2:

[0164] The server integrates the analyzed sentiment attributes with historical business performance data. Current sentiment attributes and past business process history are used as input. Using this, a generative AI model compares sentiment data with past performance to discover patterns for improving business efficiency. The output is a proposal for business improvement based on sentiment. Specifically, the server provides prompt statements to the generative AI model, which then generates new proposals.

[0165] Step 3:

[0166] The terminal displays business improvement suggestions provided by the server to the user. The input at this stage is the business improvement suggestions sent from the server, and the output is specific improvement measures displayed on the user interface. Specific actions include suggesting task reallocation and relaxation techniques to reduce stress, which are displayed on the user's screen.

[0167] Step 4:

[0168] The user accepts and implements suggestions displayed on the terminal. The input is the work improvement suggestions displayed on the terminal, and based on these, the user adjusts their work tasks and manages their emotional state. The output is improved work performance and a stable emotional state. Specifically, the user puts the suggestions into action and inputs feedback into the terminal to evaluate the results.

[0169] Step 5:

[0170] The server collects user feedback and updates the database. The input is user feedback data, which the server uses to refine its algorithm and improve future accuracy. The output is the improved system performance. Specifically, the collected feedback is analyzed, and the algorithm is optimized to improve the accuracy of future suggestions.

[0171] (Application Example 2)

[0172] 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".

[0173] Traditional electronic payment services lack consideration for users' emotional states when making purchase suggestions, resulting in insufficient optimization of consumer behavior to align with user emotions. This can lead to users making inappropriate purchase decisions and potentially lowering their satisfaction.

[0174] 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.

[0175] In this invention, the server includes an emotion analysis means for recognizing the user's emotional state and optimizing business processes, a means for generating consumer behavior suggestions based on the emotional state, and a means for comparing past emotional data with purchase history and optimizing consumer behavior. This makes it possible to provide purchase suggestions that are appropriate to the user's emotions and promote optimal consumer behavior according to their emotional state.

[0176] "User emotional state" refers to the psychological and emotional state of the user, determined based on data such as facial expressions and voice.

[0177] "Optimizing business processes" refers to improving workflows and task allocation in order to enhance efficiency and productivity.

[0178] "Emotional analysis tools" refer to software or algorithms used to recognize a user's emotional state.

[0179] "Suggestions for consumer behavior" are suggestions that indicate the optimal purchasing action based on the user's emotional state.

[0180] "Data collection" refers to gathering information about business processes and user behavior.

[0181] "Real-time recording" means instantly recording the progress of business processes and the emotional state of users.

[0182] "Self-evaluation" is the process of reflecting on one's own actions and results based on data obtained after completing a task.

[0183] An "efficiency improvement proposal" is the presentation of specific improvement measures to enhance the efficiency of business operations or consumer behavior.

[0184] "Feedback collection" involves gathering opinions and reactions from users, which are used to improve the system.

[0185] An "emotion analysis algorithm" is a computational procedure used to analyze a user's emotional state.

[0186] The system for implementing this invention functions through the cooperation of a server, a terminal, and a user. The server uses emotion analysis means to understand the user's emotional state in real time. To this end, the server collects data through the camera and microphone of a smartphone or smart glasses and performs analysis using emotion analysis software and algorithms (e.g., FaceAPI, Google® Cloud Vision).

[0187] The device functions as a user interface, displaying the results of sentiment analysis and suggested consumer behaviors based on those analyses. This allows users to visualize their own emotional state and receive suggestions to optimize their purchasing behavior.

[0188] Users refer to suggestions derived from sentiment analysis while carrying out their daily tasks and purchasing activities. For example, if stress is detected, the server compares it with past sentiment data and displays discount information on the most suitable relaxation products and services on the device.

[0189] As a concrete example, to provide the psychological stability that users desire, the server inputs a prompt message into the AI ​​model such as, "The user's current emotional state is stressed; please generate suggestions for products and services suitable for this state." The AI ​​model then generates effective suggestions based on past data and market trends, improving the user's consumption experience. In this way, optimal consumption behavior based on the user's emotions becomes possible.

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The server collects user facial and audio data in real time through the camera and microphone of smartphones and smart glasses. The data is input into emotion analysis software, which analyzes the user's emotional state. The input is raw audio and image data, and the output is an emotional state score or label.

[0193] Step 2:

[0194] The server creates prompt statements for the AI ​​model based on the acquired emotional state data and inputs them. These prompt statements are constructed taking into account past emotional data and purchase history. The input consists of an emotional state score and past data, and the output is a suggestion for optimal consumption behavior. Through this procedure, the server generates specific suggestions tailored to the user's current state.

[0195] Step 3:

[0196] The terminal displays consumer behavior suggestions sent from the server to the user. This allows the user to understand their current emotional state and make purchasing decisions based on the suggestions they receive. The input is suggestion data from the server, and the output is the display on the user interface.

[0197] Step 4:

[0198] The user checks the display on their device and selects products and services, referring to suggestions as needed. After deciding on their purchasing behavior, this information is sent to the system as feedback. The input is the user's selection information via the device, and the output is the feedback data sent to the server.

[0199] Step 5:

[0200] The server updates its database based on user feedback to improve future suggestions. The newly acquired data is used when creating the next prompt, ensuring continuous improvement. The input is feedback data, and the output is the updated database information.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] In order to implement this invention, it is necessary to effectively utilize the roles of the server, terminal, and user.

[0218] First, the server operates as a foundation with a specialized software configuration. The server collects information about the user's work environment and receives various business process data registered in the system. It also accesses APIs and external data sources to obtain necessary information. This allows the server to perform necessary data analysis in real time and support the automated task evaluation and suggestion generation by the AI ​​agent.

[0219] Next, the terminal functions as a user interface, serving as a platform for users to initiate or record tasks. The terminal continuously records the progress of business processes and transmits this information to the server. Users can review suggested efficiency improvements through the terminal and submit feedback from there.

[0220] Finally, the user is responsible for performing business tasks and evaluating the system's suggestions. Users are encouraged to review the efficiency suggestions presented on their terminal and provide feedback on their effectiveness. This feedback is collected on the server and used to further improve the AI ​​agent's algorithms.

[0221] For example, in project management tasks, users input task details and schedules using a terminal. The server analyzes this data and suggests new task lists and priorities to improve time management efficiency. These suggestions are displayed on the terminal, and users can use them to optimize their work processes, ultimately improving the overall efficiency of the project.

[0222] This system configuration enables efficient task management and continuous improvement in business operations. Through repeated self-evaluation and optimization as tasks are completed, it becomes possible to respond flexibly and quickly to changing business environments.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The server collects user work environment information and configures the necessary settings for business processes. This includes configuring APIs and registering data formats.

[0226] Step 2:

[0227] The user uses their device to initiate a work task. The user opens a project management application and enters the task details and schedule.

[0228] Step 3:

[0229] The terminal records user input data and sends it to the server. This data is used to track the progress of business processes in real time.

[0230] Step 4:

[0231] The server analyzes the received data and has the AI ​​agent determine the status of the task. Here, the performance of the business process is evaluated, and areas requiring optimization are identified.

[0232] Step 5:

[0233] The AI ​​agent performs a self-assessment process, identifying areas for improvement while comparing them with past data. The server generates suggestions from the AI ​​agent and develops appropriate task management methods.

[0234] Step 6:

[0235] The device displays suggested efficiency improvements to the user. The displayed information includes specific steps for implementing the improvements.

[0236] Step 7:

[0237] Users review the proposals via their devices and provide feedback to the server. This feedback includes opinions on the usefulness of the proposals and any issues they may have.

[0238] Step 8:

[0239] The server analyzes user feedback and adjusts the AI ​​agent's algorithm based on that feedback. This adjustment optimizes subsequent tasks.

[0240] (Example 1)

[0241] 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."

[0242] In business activities, it is necessary to efficiently collect and analyze information and appropriately evaluate task priorities, but conventional systems do not adequately handle real-time information processing or improve the accuracy of suggestions. Therefore, there are challenges in optimizing business efficiency and continuously improving the system.

[0243] 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.

[0244] In this invention, the server includes means for collecting information and monitoring business procedures, means for evaluating and suggesting task priorities using a generative AI model, and means for updating algorithms based on feedback. This enables efficient information processing and continuous optimization of business processes in business activities.

[0245] "Collecting information" refers to the process of acquiring and storing business-related data in a system.

[0246] "Means of monitoring business procedures" refers to technologies for tracking the progress of business processes and understanding their status in real time.

[0247] "Using a generative AI model" refers to a method that utilizes artificial intelligence to analyze data and generate task priorities and efficiency improvement suggestions.

[0248] "Evaluating and proposing task priorities" refers to the function of determining the importance and urgency of each task based on collected information and suggesting the optimal work order.

[0249] "Updating algorithms based on feedback" refers to the process of continuously improving the system's analysis algorithms by taking into account evaluations and opinions from users.

[0250] In order to implement this invention, it is necessary to effectively utilize the roles of the server, terminal, and user. Specifically, the system is configured as follows.

[0251] The server functions as a platform for collecting business information and monitoring business procedures. It interacts with external data sources via APIs to retrieve necessary data. The primary software used by the server is a generative AI model, which is used to analyze the collected data and perform efficient task management and prioritization. The server also regularly updates this AI model based on feedback to improve the accuracy of its analysis.

[0252] The terminal provides a user interface and is a device for users to operate and record work tasks. The terminal displays efficiency suggestions from the server and accepts input from the user. This input, such as task progress and feedback on suggestions, is sent to the server in real time.

[0253] Users are expected to perform their daily tasks and improve their work efficiency by utilizing suggestions from this system. Users review suggestions presented by the server via their terminals, evaluate their effectiveness, and provide feedback. This feedback is analyzed by the server and used to improve the AI ​​model.

[0254] As a concrete example, in project management tasks, users input task details and schedules using a terminal. The server analyzes this data and uses a generative AI model to suggest new task lists and priorities. These suggestions are displayed on the terminal, and users can review them to optimize their work processes.

[0255] An example of a prompt message could be, "Analyze the project data entered by the user and provide suggestions for efficiently managing tasks," which could be input into the generating AI model. This system enables increased operational efficiency and continuous improvement, allowing businesses to adapt to changing environments.

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] The server collects business-related data from users. The input consists of business process data obtained from APIs and terminals. The server stores this data in a database and prepares it for subsequent analysis steps. Specifically, it performs data format conversion and normalization.

[0259] Step 2:

[0260] The server provides business data to the AI ​​model for analysis. The input is the business data collected in Step 1. The AI ​​model uses this data to generate task priorities and suggestions for efficiency improvements. The output is a priority list and efficiency improvement suggestions. Specifically, its operation includes data analysis and inference using machine learning algorithms.

[0261] Step 3:

[0262] The terminal receives suggestions sent from the server and displays them to the user. The input consists of analysis results and suggestions sent from the server. The terminal displays these on an interface in a user-friendly format. The output is the user-confirmed content of the suggestions. Specific actions include updating the graphical user interface.

[0263] Step 4:

[0264] Users review suggestions via their terminals and provide feedback on the execution of work tasks. Input is the suggestions displayed on the terminal. Users evaluate the suggestions and provide feedback on their effectiveness and areas for improvement. Output is the information sent to the server as feedback. Specific actions include filling out and submitting a feedback form.

[0265] Step 5:

[0266] The server receives feedback from the user and updates the AI ​​model. The input is the feedback sent by the user in step 4. The server analyzes the feedback and uses it as data to improve the AI ​​model's algorithm. The output is the improved AI model. Specific actions include tuning the algorithm's parameters and retraining.

[0267] (Application Example 1)

[0268] 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."

[0269] In today's production environment, there is a demand for optimization of work efficiency and quality control. However, there is a lack of technology to respond quickly through real-time data analysis and efficiency improvement suggestions. As a result, traditional production methods often result in inefficient processes, making it difficult to conduct optimal production activities in factory operations.

[0270] 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.

[0271] In this invention, the server includes means for analyzing the progress of a specific activity and presenting suggestions to the user terminal to improve work efficiency, and means for determining the optimal timing for preventative maintenance using information from sensors. This makes it possible to prevent production line stoppages and equipment malfunctions.

[0272] "Means for collecting data and monitoring business processes" refers to a system that automatically acquires all information related to business operations and continuously monitors their progress.

[0273] "A means of recording task progress in real time and transmitting data" refers to a system that immediately records the progress of a task being worked on and transmits the information to a central database.

[0274] "A means of analyzing data and conducting a self-assessment after task completion" refers to a method of evaluating the results at the time the task is completed and comparing them with past data.

[0275] "Methods for generating efficiency proposals based on evaluation results" refers to techniques for creating specific proposals to improve the efficiency of operations based on analysis results.

[0276] "Methods for collecting user feedback and optimizing the system" refers to the process of gathering user opinions and evaluations and using them to improve the entire system.

[0277] "A means of analyzing the progress of a specific activity and presenting suggestions to the user's terminal to improve work efficiency" refers to a method of analyzing the current state of an activity and informing the user of recommendations for improving work efficiency.

[0278] "A method for determining the optimal timing for preventative maintenance using information from sensors" refers to a technology that uses data obtained from various sensors to make decisions about when to perform equipment maintenance.

[0279] To implement this invention, it is necessary to construct a system in which the server, terminal, and user each play a specific role. The server is responsible for the core of data processing and analysis, and collects all information related to the business. The server integrates information from various sensors and is executed using Python or AI models for data analysis. The data is processed in real-time, and the optimal timing for improving efficiency and preventive maintenance in the production line is calculated.

[0280] The terminal functions as an interface between the user and the server. Smartphones or tablets are commonly used, allowing users to confirm proposals and provide feedback in real-time. Users can use the terminal to check for optimized activities and obtain information to improve the efficiency of their work.

[0281] The user accepts the efficiency improvement proposals obtained from the system and conducts work based on them. Additionally, the user's feedback is sent to the server, which sequentially optimizes the AI algorithm.

[0282] For example, in a factory, sensors for monitoring the vibration and temperature of machines are installed, and the data is analyzed by the server. As an analysis result, a proposal such as "The replacement of part B in machine A is optimal in one week in the current state" is sent to the user terminal.

[0283] Examples of prompt sentences include "Please provide instructions to minimize the expected downtime of the production line in the next week."

[0284] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The server collects data in real time from various sensors. As input, it receives data such as temperature, vibration, and operating time obtained from the sensors in the factory. As output, it stores the raw data in a database and prepares it for subsequent processing. It formats the data into the required form for data processing and converts it into data points for each time period.

[0287] Step 2:

[0288] The server analyzes the collected data using Python and an AI model (e.g., TensorFlow or PyTorch). As input, it uses the formatted sensor data. The AI model utilizes a generative AI model to evaluate the current state of the device. As output, it obtains an evaluation result regarding the operating status of the equipment. As data operations, machine learning algorithms are applied to perform anomaly detection and predictive analysis.

[0289] Step 3:

[0290] The server generates an efficiency improvement proposal based on the analysis results and sends it to the terminal. As input, it receives the evaluation result of the AI model. As output, it generates specific proposals for improving work efficiency and maintenance recommendations in text format. This proposal can also be the content of a prompt sentence. Specifically, it includes statements such as "The recommended time to replace part B in machine A is in one week."

[0291] Step 4:

[0292] The terminal displays the efficiency improvement proposal received from the server to the user. As input, it receives the proposal sent from the server in text format. As output, it displays the content of the proposal on the user interface in a form that is easy for the user to understand. As a specific operation, it provides information visually in graphs or lists to support the user to respond immediately.

[0293] Step 5:

[0294] Users review suggestions displayed on their devices and perform tasks or submit feedback as needed. Input involves reviewing the suggested content. Output involves sending feedback to the server via the device after completing the task. Specifically, this includes entering comments regarding the effectiveness of the suggestions and areas for improvement.

[0295] Step 6:

[0296] The server receives feedback from users and uses it to optimize the AI ​​model. It receives user feedback data as input and generates an improved algorithm as output, which is then used for future analyses. Specifically, this involves adding the feedback to the data set and retraining the model.

[0297] 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.

[0298] This invention is a system that incorporates an emotion engine to recognize the user's emotional state and utilize it in business processes and task management. In this system, the roles of the server, terminal, and user work together to improve business efficiency and create a comfortable working environment for the user.

[0299] First, the server functions as the central hub for data collection and analysis. The server collects not only data related to business processes, but also emotional data based on users' facial expressions and voices, and analyzes it in real time. Furthermore, it combines this with historical data to understand current emotions and uses this information to optimize business tasks.

[0300] The terminal serves as a user interface and displays the business task data input by the user and the emotion information obtained by the emotion engine. In this way, the user can grasp the impact of their own emotional state on the business and can perform self-improvement based on it.

[0301] While performing the business, the user can manage their own emotional state using the emotional data captured by the emotion engine. For example, when the emotion engine detects that the deadline of a project is approaching and the user is in a high-stress state, the server will perform task redistribution or adjustment proposals to reduce the user's burden. In this way, the business process is optimized based on the user's emotions.

[0302] As a specific example, when the emotion engine detects the user's stress state, the terminal displays a proposal for relaxation techniques to reduce stress. The server selects an efficient solution from the data under the same situation in the past and makes a timely proposal to the user, so as to achieve both the efficiency of the business and the mental health of the user. This system provides a new form of business management that utilizes emotional data.

[0303] The following describes the processing flow.

[0304] Step 1:

[0305] The server collects the basic data related to the business process and performs initial settings for managing the business patterns and emotional data of each user. At this stage, the connection to the API and external data sources is also established.

[0306] Step 2:

[0307] The user starts a business task through the terminal. This includes the input of information regarding the content, deadline, and priority of the task.

[0308] Step 3:

[0309] An emotion engine operates on the device, recognizing the user's emotional state in real time from their facial expressions and voice. The obtained emotional data is transmitted to a server via the device.

[0310] Step 4:

[0311] The server analyzes the received emotional data and combines it with business data to evaluate the user's current emotional state. Based on this evaluation, improvement suggestions for streamlining operations are generated.

[0312] Step 5:

[0313] The device displays suggestions from the server to the user. These suggestions include things like changing task priorities and suggesting ways to relax, offering specific solutions tailored to the user's current emotional state.

[0314] Step 6:

[0315] The user reviews the suggestions on the device and adjusts their work processes accordingly. For example, if they are experiencing high stress levels, they can try the suggested relaxation techniques. The user then inputs the results as feedback into the device.

[0316] Step 7:

[0317] The server analyzes user feedback and adjusts the system-wide algorithms, including the emotion engine. This feedback is used to improve the accuracy of future suggestions.

[0318] Step 8:

[0319] Based on feedback, improved efficiency suggestions are generated and incorporated into the next work cycle. This continuously optimizes user emotional state and work performance.

[0320] (Example 2)

[0321] 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".

[0322] In today's work environment, employees' emotional states have a significant impact on work efficiency and productivity. However, existing systems lacked the means to analyze emotions in real time and optimize work processes based on users' feelings. Therefore, there is a need to provide efficient task management and work improvement methods that take emotional states into consideration.

[0323] 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.

[0324] In this invention, the server includes means for collecting data from a device to analyze emotional states, means for integrating emotional data and task data to optimize business processes, and means for displaying the analysis results through a user interface. This enables the optimization of business operations while taking emotional states into consideration.

[0325] "Emotional state" refers to data that indicates the emotions and psychological state that an individual user is experiencing at a specific point in time.

[0326] A "device" refers to a hardware device used to acquire emotional or work-related data.

[0327] A "server" refers to a computing system that centrally processes data collection, analysis, and integration.

[0328] A "user interface" refers to the software and hardware components that provide a means for a user to interact with a system.

[0329] "Efficiency improvement suggestions" refer to specific advice and methods generated based on user sentiment data to improve work processes and increase efficiency.

[0330] "Feedback" refers to information, including responses and reactions, collected from users, which helps improve the system.

[0331] An "algorithm" refers to a set of steps or calculations defined to solve a specific problem.

[0332] This invention provides a system that analyzes a user's emotional state in real time and integrates it with business tasks in order to improve efficiency in business processes. First, the server collects user emotional data from the device using speech recognition and facial recognition technologies. This uses hardware that captures emotional states, such as cameras and microphones. The server then uses machine learning platforms such as TensorFlow and PyTorch to analyze the collected data and understand the user's emotional state.

[0333] Next, the terminal acts as a user interface, displaying feedback from the server to the user. This information includes suggestions for work improvement based on the user's current emotional state, presented in a format that is easy for the user to understand directly. For example, if the user is experiencing high stress, specific suggestions such as relaxation techniques or task redistribution will be displayed on the terminal.

[0334] Users adjust their emotional state while performing their tasks, based on the provided work improvement suggestions. In particular, they can take actions to perform work tasks more efficiently by referring to the data detected by the emotion engine.

[0335] By utilizing generative AI models, the server can analyze patterns in past emotional data and business performance data to develop new approaches for optimizing future business processes. For example, a possible prompt might be: "Based on user emotional data, generate suggestions to improve the efficiency of task management. Specifically, provide task redistribution suggestions for when the user is in a high-stress state."

[0336] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0337] Step 1:

[0338] The server collects audio and video data from the device to analyze the user's emotional state. The input consists of emotionally expressive audio and facial images, and a machine learning algorithm is used to extract emotional attributes based on this data. The output is the user's emotional state at a specific time (e.g., stress level, relaxation level). Specifically, real-time data is acquired through the camera and microphone and sent to the server for analysis.

[0339] Step 2:

[0340] The server integrates the analyzed sentiment attributes with historical business performance data. Current sentiment attributes and past business process history are used as input. Using this, a generative AI model compares sentiment data with past performance to discover patterns for improving business efficiency. The output is a proposal for business improvement based on sentiment. Specifically, the server provides prompt statements to the generative AI model, which then generates new proposals.

[0341] Step 3:

[0342] The terminal displays business improvement suggestions provided by the server to the user. The input at this stage is the business improvement suggestions sent from the server, and the output is specific improvement measures displayed on the user interface. Specific actions include suggesting task reallocation and relaxation techniques to reduce stress, which are displayed on the user's screen.

[0343] Step 4:

[0344] The user accepts and implements suggestions displayed on the terminal. The input is the work improvement suggestions displayed on the terminal, and based on these, the user adjusts their work tasks and manages their emotional state. The output is improved work performance and a stable emotional state. Specifically, the user puts the suggestions into action and inputs feedback into the terminal to evaluate the results.

[0345] Step 5:

[0346] The server collects user feedback and updates the database. The input is user feedback data, which the server uses to refine its algorithm and improve future accuracy. The output is the improved system performance. Specifically, the collected feedback is analyzed, and the algorithm is optimized to improve the accuracy of future suggestions.

[0347] (Application Example 2)

[0348] 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."

[0349] Traditional electronic payment services lack consideration for users' emotional states when making purchase suggestions, resulting in insufficient optimization of consumer behavior to align with user emotions. This can lead to users making inappropriate purchase decisions and potentially lowering their satisfaction.

[0350] 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.

[0351] In this invention, the server includes an emotion analysis means for recognizing the user's emotional state and optimizing business processes, a means for generating consumer behavior suggestions based on the emotional state, and a means for comparing past emotional data with purchase history and optimizing consumer behavior. This makes it possible to provide purchase suggestions that are appropriate to the user's emotions and promote optimal consumer behavior according to their emotional state.

[0352] "User emotional state" refers to the psychological and emotional state of the user, determined based on data such as facial expressions and voice.

[0353] "Optimizing business processes" refers to improving workflows and task allocation in order to enhance efficiency and productivity.

[0354] "Emotional analysis tools" refer to software or algorithms used to recognize a user's emotional state.

[0355] "Suggestions for consumer behavior" are suggestions that indicate the optimal purchasing action based on the user's emotional state.

[0356] "Data collection" refers to gathering information about business processes and user behavior.

[0357] "Real-time recording" means instantly recording the progress of business processes and the emotional state of users.

[0358] "Self-evaluation" is the process of reflecting on one's own actions and results based on data obtained after completing a task.

[0359] An "efficiency improvement proposal" is the presentation of specific improvement measures to enhance the efficiency of business operations or consumer behavior.

[0360] "Feedback collection" involves gathering opinions and reactions from users, which are used to improve the system.

[0361] An "emotion analysis algorithm" is a computational procedure used to analyze a user's emotional state.

[0362] The system for implementing this invention functions through the cooperation of a server, a terminal, and a user. The server uses emotion analysis means to understand the user's emotional state in real time. To this end, the server collects data through the camera and microphone of a smartphone or smart glasses and performs analysis using emotion analysis software and algorithms (e.g., FaceAPI, Google Cloud Vision).

[0363] The device functions as a user interface, displaying the results of sentiment analysis and suggested consumer behaviors based on those analyses. This allows users to visualize their own emotional state and receive suggestions to optimize their purchasing behavior.

[0364] Users refer to suggestions derived from sentiment analysis while carrying out their daily tasks and purchasing activities. For example, if stress is detected, the server compares it with past sentiment data and displays discount information on the most suitable relaxation products and services on the device.

[0365] As a concrete example, to provide the psychological stability that users desire, the server inputs a prompt message into the AI ​​model such as, "The user's current emotional state is stressed; please generate suggestions for products and services suitable for this state." The AI ​​model then generates effective suggestions based on past data and market trends, improving the user's consumption experience. In this way, optimal consumption behavior based on the user's emotions becomes possible.

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The server collects user facial and audio data in real time through the camera and microphone of smartphones and smart glasses. The data is input into emotion analysis software, which analyzes the user's emotional state. The input is raw audio and image data, and the output is an emotional state score or label.

[0369] Step 2:

[0370] The server creates prompt statements for the AI ​​model based on the acquired emotional state data and inputs them. These prompt statements are constructed taking into account past emotional data and purchase history. The input consists of an emotional state score and past data, and the output is a suggestion for optimal consumption behavior. Through this procedure, the server generates specific suggestions tailored to the user's current state.

[0371] Step 3:

[0372] The terminal displays consumer behavior suggestions sent from the server to the user. This allows the user to understand their current emotional state and make purchasing decisions based on the suggestions they receive. The input is suggestion data from the server, and the output is the display on the user interface.

[0373] Step 4:

[0374] The user checks the display on their device and selects products and services, referring to suggestions as needed. After deciding on their purchasing behavior, this information is sent to the system as feedback. The input is the user's selection information via the device, and the output is the feedback data sent to the server.

[0375] Step 5:

[0376] The server updates its database based on user feedback to improve future suggestions. The newly acquired data is used when creating the next prompt, ensuring continuous improvement. The input is feedback data, and the output is the updated database information.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] 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.

[0383] 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).

[0384] 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.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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".

[0393] In order to implement this invention, it is necessary to effectively utilize the roles of the server, terminal, and user.

[0394] First, the server operates as a foundation with a specialized software configuration. The server collects information about the user's work environment and receives various business process data registered in the system. It also accesses APIs and external data sources to obtain necessary information. This allows the server to perform necessary data analysis in real time and support the automated task evaluation and suggestion generation by the AI ​​agent.

[0395] Next, the terminal functions as a user interface, serving as a platform for users to initiate or record tasks. The terminal continuously records the progress of business processes and transmits this information to the server. Users can review suggested efficiency improvements through the terminal and submit feedback from there.

[0396] Finally, the user is responsible for performing business tasks and evaluating the system's suggestions. Users are encouraged to review the efficiency suggestions presented on their terminal and provide feedback on their effectiveness. This feedback is collected on the server and used to further improve the AI ​​agent's algorithms.

[0397] For example, in project management tasks, users input task details and schedules using a terminal. The server analyzes this data and suggests new task lists and priorities to improve time management efficiency. These suggestions are displayed on the terminal, and users can use them to optimize their work processes, ultimately improving the overall efficiency of the project.

[0398] This system configuration enables efficient task management and continuous improvement in business operations. Through repeated self-evaluation and optimization as tasks are completed, it becomes possible to respond flexibly and quickly to changing business environments.

[0399] The following describes the processing flow.

[0400] Step 1:

[0401] The server collects user work environment information and configures the necessary settings for business processes. This includes configuring APIs and registering data formats.

[0402] Step 2:

[0403] The user uses their device to initiate a work task. The user opens a project management application and enters the task details and schedule.

[0404] Step 3:

[0405] The terminal records user input data and sends it to the server. This data is used to track the progress of business processes in real time.

[0406] Step 4:

[0407] The server analyzes the received data and has the AI ​​agent determine the status of the task. Here, the performance of the business process is evaluated, and areas requiring optimization are identified.

[0408] Step 5:

[0409] The AI ​​agent performs a self-assessment process, identifying areas for improvement while comparing them with past data. The server generates suggestions from the AI ​​agent and develops appropriate task management methods.

[0410] Step 6:

[0411] The device displays suggested efficiency improvements to the user. The displayed information includes specific steps for implementing the improvements.

[0412] Step 7:

[0413] Users review the proposals via their devices and provide feedback to the server. This feedback includes opinions on the usefulness of the proposals and any issues they may have.

[0414] Step 8:

[0415] The server analyzes user feedback and adjusts the AI ​​agent's algorithm based on that feedback. This adjustment optimizes subsequent tasks.

[0416] (Example 1)

[0417] 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."

[0418] In business activities, it is necessary to efficiently collect and analyze information and appropriately evaluate task priorities, but conventional systems do not adequately handle real-time information processing or improve the accuracy of suggestions. Therefore, there are challenges in optimizing business efficiency and continuously improving the system.

[0419] 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.

[0420] In this invention, the server includes means for collecting information and monitoring business procedures, means for evaluating and suggesting task priorities using a generative AI model, and means for updating algorithms based on feedback. This enables efficient information processing and continuous optimization of business processes in business activities.

[0421] "Collecting information" refers to the process of acquiring and storing business-related data in a system.

[0422] "Means of monitoring business procedures" refers to technologies for tracking the progress of business processes and understanding their status in real time.

[0423] "Using a generative AI model" refers to a method that utilizes artificial intelligence to analyze data and generate task priorities and efficiency improvement suggestions.

[0424] "Evaluating and proposing task priorities" refers to the function of determining the importance and urgency of each task based on collected information and suggesting the optimal work order.

[0425] "Updating algorithms based on feedback" refers to the process of continuously improving the system's analysis algorithms by taking into account evaluations and opinions from users.

[0426] In order to implement this invention, it is necessary to effectively utilize the roles of the server, terminal, and user. Specifically, the system is configured as follows.

[0427] The server functions as a platform for collecting business information and monitoring business procedures. It interacts with external data sources via APIs to retrieve necessary data. The primary software used by the server is a generative AI model, which is used to analyze the collected data and perform efficient task management and prioritization. The server also regularly updates this AI model based on feedback to improve the accuracy of its analysis.

[0428] The terminal provides a user interface and is a device for users to operate and record work tasks. The terminal displays efficiency suggestions from the server and accepts input from the user. This input, such as task progress and feedback on suggestions, is sent to the server in real time.

[0429] Users are expected to perform their daily tasks and improve their work efficiency by utilizing suggestions from this system. Users review suggestions presented by the server via their terminals, evaluate their effectiveness, and provide feedback. This feedback is analyzed by the server and used to improve the AI ​​model.

[0430] As a concrete example, in project management tasks, users input task details and schedules using a terminal. The server analyzes this data and uses a generative AI model to suggest new task lists and priorities. These suggestions are displayed on the terminal, and users can review them to optimize their work processes.

[0431] An example of a prompt message could be, "Analyze the project data entered by the user and provide suggestions for efficiently managing tasks," which could be input into the generating AI model. This system enables increased operational efficiency and continuous improvement, allowing businesses to adapt to changing environments.

[0432] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0433] Step 1:

[0434] The server collects business-related data from users. The input consists of business process data obtained from APIs and terminals. The server stores this data in a database and prepares it for subsequent analysis steps. Specifically, it performs data format conversion and normalization.

[0435] Step 2:

[0436] The server provides business data to the AI ​​model for analysis. The input is the business data collected in Step 1. The AI ​​model uses this data to generate task priorities and suggestions for efficiency improvements. The output is a priority list and efficiency improvement suggestions. Specifically, its operation includes data analysis and inference using machine learning algorithms.

[0437] Step 3:

[0438] The terminal receives suggestions sent from the server and displays them to the user. The input consists of analysis results and suggestions sent from the server. The terminal displays these on an interface in a user-friendly format. The output is the user-confirmed content of the suggestions. Specific actions include updating the graphical user interface.

[0439] Step 4:

[0440] Users review suggestions via their terminals and provide feedback on the execution of work tasks. Input is the suggestions displayed on the terminal. Users evaluate the suggestions and provide feedback on their effectiveness and areas for improvement. Output is the information sent to the server as feedback. Specific actions include filling out and submitting a feedback form.

[0441] Step 5:

[0442] The server receives feedback from the user and updates the AI ​​model. The input is the feedback sent by the user in step 4. The server analyzes the feedback and uses it as data to improve the AI ​​model's algorithm. The output is the improved AI model. Specific actions include tuning the algorithm's parameters and retraining.

[0443] (Application Example 1)

[0444] 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."

[0445] In today's production environment, there is a demand for optimization of work efficiency and quality control. However, there is a lack of technology to respond quickly through real-time data analysis and efficiency improvement suggestions. As a result, traditional production methods often result in inefficient processes, making it difficult to conduct optimal production activities in factory operations.

[0446] 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.

[0447] In this invention, the server includes means for analyzing the progress of a specific activity and presenting suggestions to the user terminal to improve work efficiency, and means for determining the optimal timing for preventative maintenance using information from sensors. This makes it possible to prevent production line stoppages and equipment malfunctions.

[0448] "Means for collecting data and monitoring business processes" refers to a system that automatically acquires all information related to business operations and continuously monitors their progress.

[0449] "A means of recording task progress in real time and transmitting data" refers to a system that immediately records the progress of a task being worked on and transmits the information to a central database.

[0450] "A means of analyzing data and conducting a self-assessment after task completion" refers to a method of evaluating the results at the time the task is completed and comparing them with past data.

[0451] "Methods for generating efficiency proposals based on evaluation results" refers to techniques for creating specific proposals to improve the efficiency of operations based on analysis results.

[0452] "Methods for collecting user feedback and optimizing the system" refers to the process of gathering user opinions and evaluations and using them to improve the entire system.

[0453] "A means of analyzing the progress of a specific activity and presenting suggestions to the user's terminal to improve work efficiency" refers to a method of analyzing the current state of an activity and informing the user of recommendations for improving work efficiency.

[0454] "A method for determining the optimal timing for preventative maintenance using information from sensors" refers to a technology that uses data obtained from various sensors to make decisions about when to perform equipment maintenance.

[0455] To implement this invention, it is necessary to build a system in which the server, terminal, and user each play a specific role. The server is at the core of data processing and analysis, collecting all information related to the business. The server integrates information from various sensors and performs data analysis using Python and AI models. The data is processed in real time, particularly to calculate the optimal timing for efficiency improvements and preventive maintenance on production lines.

[0456] The terminal functions as an interface between the user and the server. Smartphones or tablets are commonly used, allowing users to view suggestions and provide feedback in real time. Users can use the terminal to review streamlined activities and obtain information to improve the efficiency of their own work.

[0457] The user accepts the efficiency suggestions provided by the system and performs their work based on them. Furthermore, the user's feedback is sent to the server, which continuously optimizes the AI ​​algorithm.

[0458] For example, in a factory, sensors are installed to monitor machine vibrations and temperatures, and this data is analyzed on a server. As a result of the analysis, suggestions such as "The optimal time to replace part B in machine A is one week from now" are sent to the user's terminal.

[0459] An example of a prompt message might be, "Please provide instructions to minimize the expected production line downtime over the next week."

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

[0461] Step 1:

[0462] The server collects data in real time from various sensors. It receives data such as temperature, vibration, and operating time from factory sensors as input. As output, it stores the raw data in a database, preparing it for subsequent processing. For data processing, it formats the data into the necessary format and converts it into time-based data points.

[0463] Step 2:

[0464] The server analyzes the collected data using Python and an AI model (e.g., TensorFlow or PyTorch). It uses formatted sensor data as input. The AI ​​model utilizes a generative AI model to evaluate the current state of the device. The output is an evaluation result regarding the operating status of the equipment. Machine learning algorithms are applied to the data to perform anomaly detection and predictive analysis.

[0465] Step 3:

[0466] The server generates efficiency improvement suggestions based on the analysis results and sends them to the terminal. It receives the evaluation results of the AI ​​model as input. As output, it generates specific suggestions for improving work efficiency and maintenance recommendations in text format. These suggestions can also function as prompts. For example, they might include statements like, "The recommended replacement time for part B in machine A is one week from now."

[0467] Step 4:

[0468] The terminal displays efficiency improvement suggestions received from the server to the user. It receives suggestions from the server in text format as input. As output, it displays the suggestions on the user interface in a format that is easy for the user to understand. Specifically, it provides information visually using graphs and lists to support the user in taking immediate action.

[0469] Step 5:

[0470] Users review suggestions displayed on their devices and perform tasks or submit feedback as needed. Input involves reviewing the suggested content. Output involves sending feedback to the server via the device after completing the task. Specifically, this includes entering comments regarding the effectiveness of the suggestions and areas for improvement.

[0471] Step 6:

[0472] The server receives feedback from users and uses it to optimize the AI ​​model. It receives user feedback data as input and generates an improved algorithm as output, which is then used for future analyses. Specifically, this involves adding the feedback to the data set and retraining the model.

[0473] 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.

[0474] This invention is a system that incorporates an emotion engine to recognize the user's emotional state and utilize it in business processes and task management. In this system, the roles of the server, terminal, and user work together to improve business efficiency and create a comfortable working environment for the user.

[0475] First, the server functions as the central hub for data collection and analysis. The server collects not only data related to business processes, but also emotional data based on users' facial expressions and voices, and analyzes it in real time. Furthermore, it combines this with historical data to understand current emotions and uses this information to optimize business tasks.

[0476] The terminal serves as a user interface, displaying work task data entered by the user and emotional information acquired by the emotion engine. This allows users to understand the impact of their emotional state on their work and enables them to make self-improvements based on that understanding.

[0477] While performing their tasks, users can manage their own emotional state using emotional data collected by the emotion engine. For example, if the emotion engine detects that a project deadline is approaching and the user is experiencing high stress, the server will suggest task redistribution or adjustments to reduce the user's burden. In this way, work processes are optimized based on the user's emotions.

[0478] As a concrete example, if the emotion engine detects a user's stress level, the terminal displays suggestions for relaxation techniques to reduce stress. The server selects efficient solutions from data from similar past situations and provides timely suggestions to the user, thereby achieving both work efficiency and the user's mental well-being. This system provides a new form of work management that utilizes emotional data.

[0479] The following describes the processing flow.

[0480] Step 1:

[0481] The server collects basic data related to business processes and performs initial setup to manage each user's work patterns and sentiment data. At this stage, connections to APIs and external data sources are also established.

[0482] Step 2:

[0483] Users initiate work tasks through their devices. This includes entering information about the task content, deadline, and priority.

[0484] Step 3:

[0485] An emotion engine operates on the device, recognizing the user's emotional state in real time from their facial expressions and voice. The obtained emotional data is transmitted to a server via the device.

[0486] Step 4:

[0487] The server analyzes the received emotional data and combines it with business data to evaluate the user's current emotional state. Based on this evaluation, improvement suggestions for streamlining operations are generated.

[0488] Step 5:

[0489] The device displays suggestions from the server to the user. These suggestions include things like changing task priorities and suggesting ways to relax, offering specific solutions tailored to the user's current emotional state.

[0490] Step 6:

[0491] The user reviews the suggestions on the device and adjusts their work processes accordingly. For example, if they are experiencing high stress levels, they can try the suggested relaxation techniques. The user then inputs the results as feedback into the device.

[0492] Step 7:

[0493] The server analyzes user feedback and adjusts the system-wide algorithms, including the emotion engine. This feedback is used to improve the accuracy of future suggestions.

[0494] Step 8:

[0495] Based on feedback, improved efficiency suggestions are generated and incorporated into the next work cycle. This continuously optimizes user emotional state and work performance.

[0496] (Example 2)

[0497] 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."

[0498] In today's work environment, employees' emotional states have a significant impact on work efficiency and productivity. However, existing systems lacked the means to analyze emotions in real time and optimize work processes based on users' feelings. Therefore, there is a need to provide efficient task management and work improvement methods that take emotional states into consideration.

[0499] 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.

[0500] In this invention, the server includes means for collecting data from a device to analyze emotional states, means for integrating emotional data and task data to optimize business processes, and means for displaying the analysis results through a user interface. This enables the optimization of business operations while taking emotional states into consideration.

[0501] "Emotional state" refers to data that indicates the emotions and psychological state that an individual user is experiencing at a specific point in time.

[0502] A "device" refers to a hardware device used to acquire emotional or work-related data.

[0503] A "server" refers to a computing system that centrally processes data collection, analysis, and integration.

[0504] A "user interface" refers to the software and hardware components that provide a means for a user to interact with a system.

[0505] "Efficiency improvement suggestions" refer to specific advice and methods generated based on user sentiment data to improve work processes and increase efficiency.

[0506] "Feedback" refers to information, including responses and reactions, collected from users, which helps improve the system.

[0507] An "algorithm" refers to a set of steps or calculations defined to solve a specific problem.

[0508] This invention provides a system that analyzes a user's emotional state in real time and integrates it with business tasks in order to improve efficiency in business processes. First, the server collects user emotional data from the device using speech recognition and facial recognition technologies. This uses hardware that captures emotional states, such as cameras and microphones. The server then uses machine learning platforms such as TensorFlow and PyTorch to analyze the collected data and understand the user's emotional state.

[0509] Next, the terminal acts as a user interface, displaying feedback from the server to the user. This information includes suggestions for work improvement based on the user's current emotional state, presented in a format that is easy for the user to understand directly. For example, if the user is experiencing high stress, specific suggestions such as relaxation techniques or task redistribution will be displayed on the terminal.

[0510] Users adjust their emotional state while performing their tasks, based on the provided work improvement suggestions. In particular, they can take actions to perform work tasks more efficiently by referring to the data detected by the emotion engine.

[0511] By utilizing generative AI models, the server can analyze patterns in past emotional data and business performance data to develop new approaches for optimizing future business processes. For example, a possible prompt might be: "Based on user emotional data, generate suggestions to improve the efficiency of task management. Specifically, provide task redistribution suggestions for when the user is in a high-stress state."

[0512] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0513] Step 1:

[0514] The server collects audio and video data from the device to analyze the user's emotional state. The input consists of emotionally expressive audio and facial images, and a machine learning algorithm is used to extract emotional attributes based on this data. The output is the user's emotional state at a specific time (e.g., stress level, relaxation level). Specifically, real-time data is acquired through the camera and microphone and sent to the server for analysis.

[0515] Step 2:

[0516] The server integrates the analyzed sentiment attributes with historical business performance data. Current sentiment attributes and past business process history are used as input. Using this, a generative AI model compares sentiment data with past performance to discover patterns for improving business efficiency. The output is a proposal for business improvement based on sentiment. Specifically, the server provides prompt statements to the generative AI model, which then generates new proposals.

[0517] Step 3:

[0518] The terminal displays business improvement suggestions provided by the server to the user. The input at this stage is the business improvement suggestions sent from the server, and the output is specific improvement measures displayed on the user interface. Specific actions include suggesting task reallocation and relaxation techniques to reduce stress, which are displayed on the user's screen.

[0519] Step 4:

[0520] The user accepts and implements suggestions displayed on the terminal. The input is the work improvement suggestions displayed on the terminal, and based on these, the user adjusts their work tasks and manages their emotional state. The output is improved work performance and a stable emotional state. Specifically, the user puts the suggestions into action and inputs feedback into the terminal to evaluate the results.

[0521] Step 5:

[0522] The server collects user feedback and updates the database. The input is user feedback data, which the server uses to refine its algorithm and improve future accuracy. The output is the improved system performance. Specifically, the collected feedback is analyzed, and the algorithm is optimized to improve the accuracy of future suggestions.

[0523] (Application Example 2)

[0524] 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."

[0525] Traditional electronic payment services lack consideration for users' emotional states when making purchase suggestions, resulting in insufficient optimization of consumer behavior to align with user emotions. This can lead to users making inappropriate purchase decisions and potentially lowering their satisfaction.

[0526] 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.

[0527] In this invention, the server includes an emotion analysis means for recognizing the user's emotional state and optimizing business processes, a means for generating consumer behavior suggestions based on the emotional state, and a means for comparing past emotional data with purchase history and optimizing consumer behavior. This makes it possible to provide purchase suggestions that are appropriate to the user's emotions and promote optimal consumer behavior according to their emotional state.

[0528] "User emotional state" refers to the psychological and emotional state of the user, determined based on data such as facial expressions and voice.

[0529] "Optimizing business processes" refers to improving workflows and task allocation in order to enhance efficiency and productivity.

[0530] "Emotional analysis tools" refer to software or algorithms used to recognize a user's emotional state.

[0531] "Suggestions for consumer behavior" are suggestions that indicate the optimal purchasing action based on the user's emotional state.

[0532] "Data collection" refers to gathering information about business processes and user behavior.

[0533] "Real-time recording" means instantly recording the progress of business processes and the emotional state of users.

[0534] "Self-evaluation" is the process of reflecting on one's own actions and results based on data obtained after completing a task.

[0535] An "efficiency improvement proposal" is the presentation of specific improvement measures to enhance the efficiency of business operations or consumer behavior.

[0536] "Feedback collection" involves gathering opinions and reactions from users, which are used to improve the system.

[0537] An "emotion analysis algorithm" is a computational procedure used to analyze a user's emotional state.

[0538] The system for implementing this invention functions through the cooperation of a server, a terminal, and a user. The server uses emotion analysis means to understand the user's emotional state in real time. To this end, the server collects data through the camera and microphone of a smartphone or smart glasses and performs analysis using emotion analysis software and algorithms (e.g., FaceAPI, Google Cloud Vision).

[0539] The device functions as a user interface, displaying the results of sentiment analysis and suggested consumer behaviors based on those analyses. This allows users to visualize their own emotional state and receive suggestions to optimize their purchasing behavior.

[0540] Users refer to suggestions derived from sentiment analysis while carrying out their daily tasks and purchasing activities. For example, if stress is detected, the server compares it with past sentiment data and displays discount information on the most suitable relaxation products and services on the device.

[0541] As a concrete example, to provide the psychological stability that users desire, the server inputs a prompt message into the AI ​​model such as, "The user's current emotional state is stressed; please generate suggestions for products and services suitable for this state." The AI ​​model then generates effective suggestions based on past data and market trends, improving the user's consumption experience. In this way, optimal consumption behavior based on the user's emotions becomes possible.

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

[0543] Step 1:

[0544] The server collects user facial and audio data in real time through the camera and microphone of smartphones and smart glasses. The data is input into emotion analysis software, which analyzes the user's emotional state. The input is raw audio and image data, and the output is an emotional state score or label.

[0545] Step 2:

[0546] The server creates prompt statements for the AI ​​model based on the acquired emotional state data and inputs them. These prompt statements are constructed taking into account past emotional data and purchase history. The input consists of an emotional state score and past data, and the output is a suggestion for optimal consumption behavior. Through this procedure, the server generates specific suggestions tailored to the user's current state.

[0547] Step 3:

[0548] The terminal displays consumer behavior suggestions sent from the server to the user. This allows the user to understand their current emotional state and make purchasing decisions based on the suggestions they receive. The input is suggestion data from the server, and the output is the display on the user interface.

[0549] Step 4:

[0550] The user checks the display on their device and selects products and services, referring to suggestions as needed. After deciding on their purchasing behavior, this information is sent to the system as feedback. The input is the user's selection information via the device, and the output is the feedback data sent to the server.

[0551] Step 5:

[0552] The server updates its database based on user feedback to improve future suggestions. The newly acquired data is used when creating the next prompt, ensuring continuous improvement. The input is feedback data, and the output is the updated database information.

[0553] 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.

[0554] 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.

[0555] 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.

[0556] [Fourth Embodiment]

[0557] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0558] 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.

[0559] 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).

[0560] 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.

[0561] 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.

[0562] 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).

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] 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".

[0570] In order to implement this invention, it is necessary to effectively utilize the roles of the server, terminal, and user.

[0571] First, the server operates as a foundation with a specialized software configuration. The server collects information about the user's work environment and receives various business process data registered in the system. It also accesses APIs and external data sources to obtain necessary information. This allows the server to perform necessary data analysis in real time and support the automated task evaluation and suggestion generation by the AI ​​agent.

[0572] Next, the terminal functions as a user interface, serving as a platform for users to initiate or record tasks. The terminal continuously records the progress of business processes and transmits this information to the server. Users can review suggested efficiency improvements through the terminal and submit feedback from there.

[0573] Finally, the user is responsible for performing business tasks and evaluating the system's suggestions. Users are encouraged to review the efficiency suggestions presented on their terminal and provide feedback on their effectiveness. This feedback is collected on the server and used to further improve the AI ​​agent's algorithms.

[0574] For example, in project management tasks, users input task details and schedules using a terminal. The server analyzes this data and suggests new task lists and priorities to improve time management efficiency. These suggestions are displayed on the terminal, and users can use them to optimize their work processes, ultimately improving the overall efficiency of the project.

[0575] This system configuration enables efficient task management and continuous improvement in business operations. Through repeated self-evaluation and optimization as tasks are completed, it becomes possible to respond flexibly and quickly to changing business environments.

[0576] The following describes the processing flow.

[0577] Step 1:

[0578] The server collects user work environment information and configures the necessary settings for business processes. This includes configuring APIs and registering data formats.

[0579] Step 2:

[0580] The user uses their device to initiate a work task. The user opens a project management application and enters the task details and schedule.

[0581] Step 3:

[0582] The terminal records user input data and sends it to the server. This data is used to track the progress of business processes in real time.

[0583] Step 4:

[0584] The server analyzes the received data and has the AI ​​agent determine the status of the task. Here, the performance of the business process is evaluated, and areas requiring optimization are identified.

[0585] Step 5:

[0586] The AI ​​agent performs a self-assessment process, identifying areas for improvement while comparing them with past data. The server generates suggestions from the AI ​​agent and develops appropriate task management methods.

[0587] Step 6:

[0588] The device displays suggested efficiency improvements to the user. The displayed information includes specific steps for implementing the improvements.

[0589] Step 7:

[0590] Users review the proposals via their devices and provide feedback to the server. This feedback includes opinions on the usefulness of the proposals and any issues they may have.

[0591] Step 8:

[0592] The server analyzes user feedback and adjusts the AI ​​agent's algorithm based on that feedback. This adjustment optimizes subsequent tasks.

[0593] (Example 1)

[0594] 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".

[0595] In business activities, it is necessary to efficiently collect and analyze information and appropriately evaluate task priorities, but conventional systems do not adequately handle real-time information processing or improve the accuracy of suggestions. Therefore, there are challenges in optimizing business efficiency and continuously improving the system.

[0596] 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.

[0597] In this invention, the server includes means for collecting information and monitoring business procedures, means for evaluating and suggesting task priorities using a generative AI model, and means for updating algorithms based on feedback. This enables efficient information processing and continuous optimization of business processes in business activities.

[0598] "Collecting information" refers to the process of acquiring and storing business-related data in a system.

[0599] "Means of monitoring business procedures" refers to technologies for tracking the progress of business processes and understanding their status in real time.

[0600] "Using a generative AI model" refers to a method that utilizes artificial intelligence to analyze data and generate task priorities and efficiency improvement suggestions.

[0601] "Evaluating and proposing task priorities" refers to the function of determining the importance and urgency of each task based on collected information and suggesting the optimal work order.

[0602] "Updating algorithms based on feedback" refers to the process of continuously improving the system's analysis algorithms by taking into account evaluations and opinions from users.

[0603] In order to implement this invention, it is necessary to effectively utilize the roles of the server, terminal, and user. Specifically, the system is configured as follows.

[0604] The server functions as a platform for collecting business information and monitoring business procedures. It interacts with external data sources via APIs to retrieve necessary data. The primary software used by the server is a generative AI model, which is used to analyze the collected data and perform efficient task management and prioritization. The server also regularly updates this AI model based on feedback to improve the accuracy of its analysis.

[0605] The terminal provides a user interface and is a device for users to operate and record work tasks. The terminal displays efficiency suggestions from the server and accepts input from the user. This input, such as task progress and feedback on suggestions, is sent to the server in real time.

[0606] Users are expected to perform their daily tasks and improve their work efficiency by utilizing suggestions from this system. Users review suggestions presented by the server via their terminals, evaluate their effectiveness, and provide feedback. This feedback is analyzed by the server and used to improve the AI ​​model.

[0607] As a concrete example, in project management tasks, users input task details and schedules using a terminal. The server analyzes this data and uses a generative AI model to suggest new task lists and priorities. These suggestions are displayed on the terminal, and users can review them to optimize their work processes.

[0608] An example of a prompt message could be, "Analyze the project data entered by the user and provide suggestions for efficiently managing tasks," which could be input into the generating AI model. This system enables increased operational efficiency and continuous improvement, allowing businesses to adapt to changing environments.

[0609] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0610] Step 1:

[0611] The server collects business-related data from users. The input consists of business process data obtained from APIs and terminals. The server stores this data in a database and prepares it for subsequent analysis steps. Specifically, it performs data format conversion and normalization.

[0612] Step 2:

[0613] The server provides business data to the AI ​​model for analysis. The input is the business data collected in Step 1. The AI ​​model uses this data to generate task priorities and suggestions for efficiency improvements. The output is a priority list and efficiency improvement suggestions. Specifically, its operation includes data analysis and inference using machine learning algorithms.

[0614] Step 3:

[0615] The terminal receives suggestions sent from the server and displays them to the user. The input consists of analysis results and suggestions sent from the server. The terminal displays these on an interface in a user-friendly format. The output is the user-confirmed content of the suggestions. Specific actions include updating the graphical user interface.

[0616] Step 4:

[0617] Users review suggestions via their terminals and provide feedback on the execution of work tasks. Input is the suggestions displayed on the terminal. Users evaluate the suggestions and provide feedback on their effectiveness and areas for improvement. Output is the information sent to the server as feedback. Specific actions include filling out and submitting a feedback form.

[0618] Step 5:

[0619] The server receives feedback from the user and updates the AI ​​model. The input is the feedback sent by the user in step 4. The server analyzes the feedback and uses it as data to improve the AI ​​model's algorithm. The output is the improved AI model. Specific actions include tuning the algorithm's parameters and retraining.

[0620] (Application Example 1)

[0621] 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".

[0622] In today's production environment, there is a demand for optimization of work efficiency and quality control. However, there is a lack of technology to respond quickly through real-time data analysis and efficiency improvement suggestions. As a result, traditional production methods often result in inefficient processes, making it difficult to conduct optimal production activities in factory operations.

[0623] 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.

[0624] In this invention, the server includes means for analyzing the progress of a specific activity and presenting suggestions to the user terminal to improve work efficiency, and means for determining the optimal timing for preventative maintenance using information from sensors. This makes it possible to prevent production line stoppages and equipment malfunctions.

[0625] "Means for collecting data and monitoring business processes" refers to a system that automatically acquires all information related to business operations and continuously monitors their progress.

[0626] "A means of recording task progress in real time and transmitting data" refers to a system that immediately records the progress of a task being worked on and transmits the information to a central database.

[0627] "A means of analyzing data and conducting a self-assessment after task completion" refers to a method of evaluating the results at the time the task is completed and comparing them with past data.

[0628] "Methods for generating efficiency proposals based on evaluation results" refers to techniques for creating specific proposals to improve the efficiency of operations based on analysis results.

[0629] "Methods for collecting user feedback and optimizing the system" refers to the process of gathering user opinions and evaluations and using them to improve the entire system.

[0630] "A means of analyzing the progress of a specific activity and presenting suggestions to the user's terminal to improve work efficiency" refers to a method of analyzing the current state of an activity and informing the user of recommendations for improving work efficiency.

[0631] "A method for determining the optimal timing for preventative maintenance using information from sensors" refers to a technology that uses data obtained from various sensors to make decisions about when to perform equipment maintenance.

[0632] To implement this invention, it is necessary to build a system in which the server, terminal, and user each play a specific role. The server is at the core of data processing and analysis, collecting all information related to the business. The server integrates information from various sensors and performs data analysis using Python and AI models. The data is processed in real time, particularly to calculate the optimal timing for efficiency improvements and preventive maintenance on production lines.

[0633] The terminal functions as an interface between the user and the server. Smartphones or tablets are commonly used, allowing users to view suggestions and provide feedback in real time. Users can use the terminal to review streamlined activities and obtain information to improve the efficiency of their own work.

[0634] The user accepts the efficiency suggestions provided by the system and performs their work based on them. Furthermore, the user's feedback is sent to the server, which continuously optimizes the AI ​​algorithm.

[0635] For example, in a factory, sensors are installed to monitor machine vibrations and temperatures, and this data is analyzed on a server. As a result of the analysis, suggestions such as "The optimal time to replace part B in machine A is one week from now" are sent to the user's terminal.

[0636] An example of a prompt message might be, "Please provide instructions to minimize the expected production line downtime over the next week."

[0637] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0638] Step 1:

[0639] The server collects data in real time from various sensors. It receives data such as temperature, vibration, and operating time from factory sensors as input. As output, it stores the raw data in a database, preparing it for subsequent processing. For data processing, it formats the data into the necessary format and converts it into time-based data points.

[0640] Step 2:

[0641] The server analyzes the collected data using Python and an AI model (e.g., TensorFlow or PyTorch). It uses formatted sensor data as input. The AI ​​model utilizes a generative AI model to evaluate the current state of the device. The output is an evaluation result regarding the operating status of the equipment. Machine learning algorithms are applied to the data to perform anomaly detection and predictive analysis.

[0642] Step 3:

[0643] The server generates efficiency improvement suggestions based on the analysis results and sends them to the terminal. It receives the evaluation results of the AI ​​model as input. As output, it generates specific suggestions for improving work efficiency and maintenance recommendations in text format. These suggestions can also function as prompts. For example, they might include statements like, "The recommended replacement time for part B in machine A is one week from now."

[0644] Step 4:

[0645] The terminal displays efficiency improvement suggestions received from the server to the user. It receives suggestions from the server in text format as input. As output, it displays the suggestions on the user interface in a format that is easy for the user to understand. Specifically, it provides information visually using graphs and lists to support the user in taking immediate action.

[0646] Step 5:

[0647] Users review suggestions displayed on their devices and perform tasks or submit feedback as needed. Input involves reviewing the suggested content. Output involves sending feedback to the server via the device after completing the task. Specifically, this includes entering comments regarding the effectiveness of the suggestions and areas for improvement.

[0648] Step 6:

[0649] The server receives feedback from users and uses it to optimize the AI ​​model. It receives user feedback data as input and generates an improved algorithm as output, which is then used for future analyses. Specifically, this involves adding the feedback to the data set and retraining the model.

[0650] 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.

[0651] This invention is a system that incorporates an emotion engine to recognize the user's emotional state and utilize it in business processes and task management. In this system, the roles of the server, terminal, and user work together to improve business efficiency and create a comfortable working environment for the user.

[0652] First, the server functions as the central hub for data collection and analysis. The server collects not only data related to business processes, but also emotional data based on users' facial expressions and voices, and analyzes it in real time. Furthermore, it combines this with historical data to understand current emotions and uses this information to optimize business tasks.

[0653] The terminal serves as a user interface, displaying work task data entered by the user and emotional information acquired by the emotion engine. This allows users to understand the impact of their emotional state on their work and enables them to make self-improvements based on that understanding.

[0654] While performing their tasks, users can manage their own emotional state using emotional data collected by the emotion engine. For example, if the emotion engine detects that a project deadline is approaching and the user is experiencing high stress, the server will suggest task redistribution or adjustments to reduce the user's burden. In this way, work processes are optimized based on the user's emotions.

[0655] As a concrete example, if the emotion engine detects a user's stress level, the terminal displays suggestions for relaxation techniques to reduce stress. The server selects efficient solutions from data from similar past situations and provides timely suggestions to the user, thereby achieving both work efficiency and the user's mental well-being. This system provides a new form of work management that utilizes emotional data.

[0656] The following describes the processing flow.

[0657] Step 1:

[0658] The server collects basic data related to business processes and performs initial setup to manage each user's work patterns and sentiment data. At this stage, connections to APIs and external data sources are also established.

[0659] Step 2:

[0660] Users initiate work tasks through their devices. This includes entering information about the task content, deadline, and priority.

[0661] Step 3:

[0662] An emotion engine operates on the device, recognizing the user's emotional state in real time from their facial expressions and voice. The obtained emotional data is transmitted to a server via the device.

[0663] Step 4:

[0664] The server analyzes the received emotional data and combines it with business data to evaluate the user's current emotional state. Based on this evaluation, improvement suggestions for streamlining operations are generated.

[0665] Step 5:

[0666] The device displays suggestions from the server to the user. These suggestions include things like changing task priorities and suggesting ways to relax, offering specific solutions tailored to the user's current emotional state.

[0667] Step 6:

[0668] The user reviews the suggestions on the device and adjusts their work processes accordingly. For example, if they are experiencing high stress levels, they can try the suggested relaxation techniques. The user then inputs the results as feedback into the device.

[0669] Step 7:

[0670] The server analyzes user feedback and adjusts the system-wide algorithms, including the emotion engine. This feedback is used to improve the accuracy of future suggestions.

[0671] Step 8:

[0672] Based on feedback, improved efficiency suggestions are generated and incorporated into the next work cycle. This continuously optimizes user emotional state and work performance.

[0673] (Example 2)

[0674] 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".

[0675] In today's work environment, employees' emotional states have a significant impact on work efficiency and productivity. However, existing systems lacked the means to analyze emotions in real time and optimize work processes based on users' feelings. Therefore, there is a need to provide efficient task management and work improvement methods that take emotional states into consideration.

[0676] 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.

[0677] In this invention, the server includes means for collecting data from a device to analyze emotional states, means for integrating emotional data and task data to optimize business processes, and means for displaying the analysis results through a user interface. This enables the optimization of business operations while taking emotional states into consideration.

[0678] "Emotional state" refers to data that indicates the emotions and psychological state that an individual user is experiencing at a specific point in time.

[0679] A "device" refers to a hardware device used to acquire emotional or work-related data.

[0680] A "server" refers to a computing system that centrally processes data collection, analysis, and integration.

[0681] A "user interface" refers to the software and hardware components that provide a means for a user to interact with a system.

[0682] "Efficiency improvement suggestions" refer to specific advice and methods generated based on user sentiment data to improve work processes and increase efficiency.

[0683] "Feedback" refers to information, including responses and reactions, collected from users, which helps improve the system.

[0684] An "algorithm" refers to a set of steps or calculations defined to solve a specific problem.

[0685] This invention provides a system that analyzes a user's emotional state in real time and integrates it with business tasks in order to improve efficiency in business processes. First, the server collects user emotional data from the device using speech recognition and facial recognition technologies. This uses hardware that captures emotional states, such as cameras and microphones. The server then uses machine learning platforms such as TensorFlow and PyTorch to analyze the collected data and understand the user's emotional state.

[0686] Next, the terminal acts as a user interface, displaying feedback from the server to the user. This information includes suggestions for work improvement based on the user's current emotional state, presented in a format that is easy for the user to understand directly. For example, if the user is experiencing high stress, specific suggestions such as relaxation techniques or task redistribution will be displayed on the terminal.

[0687] Users adjust their emotional state while performing their tasks, based on the provided work improvement suggestions. In particular, they can take actions to perform work tasks more efficiently by referring to the data detected by the emotion engine.

[0688] By utilizing generative AI models, the server can analyze patterns in past emotional data and business performance data to develop new approaches for optimizing future business processes. For example, a possible prompt might be: "Based on user emotional data, generate suggestions to improve the efficiency of task management. Specifically, provide task redistribution suggestions for when the user is in a high-stress state."

[0689] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0690] Step 1:

[0691] The server collects audio and video data from the device to analyze the user's emotional state. The input consists of emotionally expressive audio and facial images, and a machine learning algorithm is used to extract emotional attributes based on this data. The output is the user's emotional state at a specific time (e.g., stress level, relaxation level). Specifically, real-time data is acquired through the camera and microphone and sent to the server for analysis.

[0692] Step 2:

[0693] The server integrates the analyzed sentiment attributes with historical business performance data. Current sentiment attributes and past business process history are used as input. Using this, a generative AI model compares sentiment data with past performance to discover patterns for improving business efficiency. The output is a proposal for business improvement based on sentiment. Specifically, the server provides prompt statements to the generative AI model, which then generates new proposals.

[0694] Step 3:

[0695] The terminal displays business improvement suggestions provided by the server to the user. The input at this stage is the business improvement suggestions sent from the server, and the output is specific improvement measures displayed on the user interface. Specific actions include suggesting task reallocation and relaxation techniques to reduce stress, which are displayed on the user's screen.

[0696] Step 4:

[0697] The user accepts and implements suggestions displayed on the terminal. The input is the work improvement suggestions displayed on the terminal, and based on these, the user adjusts their work tasks and manages their emotional state. The output is improved work performance and a stable emotional state. Specifically, the user puts the suggestions into action and inputs feedback into the terminal to evaluate the results.

[0698] Step 5:

[0699] The server collects user feedback and updates the database. The input is user feedback data, which the server uses to refine its algorithm and improve future accuracy. The output is the improved system performance. Specifically, the collected feedback is analyzed, and the algorithm is optimized to improve the accuracy of future suggestions.

[0700] (Application Example 2)

[0701] 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".

[0702] Traditional electronic payment services lack consideration for users' emotional states when making purchase suggestions, resulting in insufficient optimization of consumer behavior to align with user emotions. This can lead to users making inappropriate purchase decisions and potentially lowering their satisfaction.

[0703] 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.

[0704] In this invention, the server includes an emotion analysis means for recognizing the user's emotional state and optimizing business processes, a means for generating consumer behavior suggestions based on the emotional state, and a means for comparing past emotional data with purchase history and optimizing consumer behavior. This makes it possible to provide purchase suggestions that are appropriate to the user's emotions and promote optimal consumer behavior according to their emotional state.

[0705] "User emotional state" refers to the psychological and emotional state of the user, determined based on data such as facial expressions and voice.

[0706] "Optimizing business processes" refers to improving workflows and task allocation in order to enhance efficiency and productivity.

[0707] "Emotional analysis tools" refer to software or algorithms used to recognize a user's emotional state.

[0708] "Suggestions for consumer behavior" are suggestions that indicate the optimal purchasing action based on the user's emotional state.

[0709] "Data collection" refers to gathering information about business processes and user behavior.

[0710] "Real-time recording" means instantly recording the progress of business processes and the emotional state of users.

[0711] "Self-evaluation" is the process of reflecting on one's own actions and results based on data obtained after completing a task.

[0712] An "efficiency improvement proposal" is the presentation of specific improvement measures to enhance the efficiency of business operations or consumer behavior.

[0713] "Feedback collection" involves gathering opinions and reactions from users, which are used to improve the system.

[0714] An "emotion analysis algorithm" is a computational procedure used to analyze a user's emotional state.

[0715] The system for implementing this invention functions through the cooperation of a server, a terminal, and a user. The server uses emotion analysis means to understand the user's emotional state in real time. To this end, the server collects data through the camera and microphone of a smartphone or smart glasses and performs analysis using emotion analysis software and algorithms (e.g., FaceAPI, Google Cloud Vision).

[0716] The device functions as a user interface, displaying the results of sentiment analysis and suggested consumer behaviors based on those analyses. This allows users to visualize their own emotional state and receive suggestions to optimize their purchasing behavior.

[0717] Users refer to suggestions derived from sentiment analysis while carrying out their daily tasks and purchasing activities. For example, if stress is detected, the server compares it with past sentiment data and displays discount information on the most suitable relaxation products and services on the device.

[0718] As a concrete example, to provide the psychological stability that users desire, the server inputs a prompt message into the AI ​​model such as, "The user's current emotional state is stressed; please generate suggestions for products and services suitable for this state." The AI ​​model then generates effective suggestions based on past data and market trends, improving the user's consumption experience. In this way, optimal consumption behavior based on the user's emotions becomes possible.

[0719] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0720] Step 1:

[0721] The server collects user facial and audio data in real time through the camera and microphone of smartphones and smart glasses. The data is input into emotion analysis software, which analyzes the user's emotional state. The input is raw audio and image data, and the output is an emotional state score or label.

[0722] Step 2:

[0723] The server creates prompt statements for the AI ​​model based on the acquired emotional state data and inputs them. These prompt statements are constructed taking into account past emotional data and purchase history. The input consists of an emotional state score and past data, and the output is a suggestion for optimal consumption behavior. Through this procedure, the server generates specific suggestions tailored to the user's current state.

[0724] Step 3:

[0725] The terminal displays consumer behavior suggestions sent from the server to the user. This allows the user to understand their current emotional state and make purchasing decisions based on the suggestions they receive. The input is suggestion data from the server, and the output is the display on the user interface.

[0726] Step 4:

[0727] The user checks the display on their device and selects products and services, referring to suggestions as needed. After deciding on their purchasing behavior, this information is sent to the system as feedback. The input is the user's selection information via the device, and the output is the feedback data sent to the server.

[0728] Step 5:

[0729] The server updates its database based on user feedback to improve future suggestions. The newly acquired data is used when creating the next prompt, ensuring continuous improvement. The input is feedback data, and the output is the updated database information.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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."

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] The following is further disclosed regarding the embodiments described above.

[0752] (Claim 1)

[0753] A means of collecting data and monitoring business processes,

[0754] A means of recording the progress of a task in real time and transmitting the data,

[0755] A means of analyzing data and conducting a self-assessment after task completion,

[0756] A means for generating efficiency improvement proposals based on evaluation results,

[0757] A means of collecting user feedback and optimizing the system,

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, which compares past results with new information sources and identifies areas for improvement.

[0761] (Claim 3)

[0762] The system according to claim 1, which proposes the optimal approach for the next task by adjusting the algorithm.

[0763] "Example 1"

[0764] (Claim 1)

[0765] Means for collecting information and monitoring work procedures,

[0766] A means of recording the progress of business processes in real time and transmitting the information,

[0767] A means of analyzing information after completing a task and conducting a self-assessment,

[0768] A means for generating efficiency improvement proposals based on analysis results,

[0769] A means of collecting user feedback and improving the system,

[0770] A method for evaluating and proposing task priorities using a generative AI model,

[0771] Means of accessing external data sources to obtain necessary information,

[0772] A means of updating the algorithm based on feedback,

[0773] A system that includes this.

[0774] (Claim 2)

[0775] The system according to claim 1, which compares past results with new information sources and identifies areas for improvement.

[0776] (Claim 3)

[0777] The system according to claim 1, which derives the optimal work procedure based on the analyzed information.

[0778] "Application Example 1"

[0779] (Claim 1)

[0780] A means of collecting data and monitoring business processes,

[0781] A means of recording the progress of a task in real time and transmitting the data,

[0782] A means of analyzing data and conducting a self-assessment after task completion,

[0783] A means for generating efficiency improvement proposals based on evaluation results,

[0784] A means of collecting user feedback and optimizing the system,

[0785] A means of analyzing the progress of a specific activity and presenting suggestions to the user terminal to improve work efficiency,

[0786] A method for determining the optimal timing for preventative maintenance using information from sensors,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, which compares past results with new information sources and identifies areas for improvement.

[0790] (Claim 3)

[0791] The system according to claim 1, which proposes the optimal approach for the next task by adjusting the algorithm.

[0792] "Example 2 of combining an emotion engine"

[0793] (Claim 1)

[0794] A means of collecting data from a device in order to analyze emotional states,

[0795] A means of optimizing business processes by integrating emotional data and task data,

[0796] A means for displaying analysis results through a user interface,

[0797] A means of generating efficiency suggestions based on emotions and promoting business improvement,

[0798] A means of optimizing the system by collecting user feedback,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, which compares past emotional states with new emotional data to identify areas for improvement.

[0802] (Claim 3)

[0803] The system according to claim 1, which adjusts the algorithm based on emotional state and proposes the optimal approach for the next work task.

[0804] "Application example 2 when combining with an emotional engine"

[0805] (Claim 1)

[0806] A means of sentiment analysis for recognizing the emotional state of users and optimizing business processes,

[0807] A means for generating consumer behavior suggestions based on emotional states,

[0808] A means of collecting data and monitoring business processes,

[0809] A means of recording the progress of a task in real time and transmitting the data,

[0810] A means of analyzing data and conducting a self-assessment after task completion,

[0811] A means for generating efficiency improvement proposals based on evaluation results,

[0812] A means of collecting user feedback and optimizing the system,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, which matches past emotional data with purchase history to optimize consumer behavior.

[0816] (Claim 3)

[0817] The system according to claim 1, which proposes optimal consumer behavior based on the next emotional state by adjusting the emotion analysis algorithm. [Explanation of Symbols]

[0818] 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 and monitoring business processes, A means of recording the progress of a task in real time and transmitting the data, A means of analyzing data and conducting a self-assessment after task completion, A means for generating efficiency improvement proposals based on evaluation results, A means of collecting user feedback and optimizing the system, A system that includes this.

2. The system according to claim 1, which compares past results with new information sources and identifies areas for improvement.

3. The system according to claim 1, which proposes the optimal approach for the next task by adjusting the algorithm.

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