system

Abstract

We provide the system. [Solution] A means of providing a method for recording data in business activities, A means for analyzing the recorded business activity data and identifying potential areas for improvement, Means for generating specific improvement measures based on the identified improvement areas, A system including means for notifying users of the aforementioned improvement measures.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 order to improve business activities, it is necessary to identify inefficient parts and areas for improvement in the current process and appropriately address them. However, it is difficult for humans to notice this in daily work and devise and implement improvement measures. Also, many operations continue as they are without external pointing out, and as a result, there are cases where productivity decreases. To solve such problems, there is a demand for a system that can automatically analyze the logs of business activities and provide specific improvement proposals.

Means for Solving the Problems

[0005] This invention provides a system that includes means for recording data in business activities. It includes means for analyzing the recorded business activity data and identifying potential areas for improvement. Based on the identified areas for improvement, it includes means for automatically generating specific improvement measures using AI and notifying the user of these suggestions. Furthermore, by having a function to collect user feedback on implemented improvement measures, it enables continuous process optimization. The system configured in this way supports improved efficiency and quality in business operations.

[0006] "Business activity data" refers to all information, including the content, timing, and error information of operations that occur during business processes.

[0007] "Analysis" is the process of breaking down data into individual elements, investigating their interrelationships and patterns, and identifying potential problems and areas for improvement.

[0008] An "area for improvement" refers to a part of business activities that is inefficient or prone to frequent errors, and is therefore an area where improvement is possible.

[0009] "Specific improvement measures" refer to instructions that describe clear procedures and methods provided for identified areas of improvement.

[0010] "Notification" refers to the act of sending specific information or suggestions to a user through an appropriate medium.

[0011] "Feedback" refers to evaluations, opinions, and information about the results obtained after an activity or proposal has been implemented.

[0012] A "system" refers to a collection of hardware and software combined to achieve a specific purpose. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

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

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

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] This invention is a system that automates the recording, analysis, proposal of improvement measures, and notification of business activity data. This system performs its main functions on a server and provides the user interface through a terminal.

[0035] Data collection

[0036] The server collects log data from business applications and associated devices. This log data includes operation history, error information, timestamps, and user actions. Data collection is performed periodically and stored in the server's database.

[0037] Data Analysis

[0038] The server processes the collected business activity data and applies machine learning algorithms to identify areas for improvement. During the analysis, it detects signs of inefficiency in business processes and analyzes frequently occurring operational errors and time-consuming processes.

[0039] Generation of improvement measures

[0040] The server generates appropriate improvement measures for the identified areas for improvement. This process utilizes past success stories and a database of expert knowledge to generate improvement measures that include specific, actionable steps.

[0041] Notifications and feedback

[0042] Improvement suggestions are sent to the user via their device. These suggestions include steps for improvement and points to change. The user receives the notification and implements the improvements as needed. The user's results and feedback are also sent to the server via the device and collected. This feedback allows the server to improve the accuracy of its analysis and use it to inform future suggestions.

[0043] As a concrete example, in database management, the server collects and analyzes SQL query execution time data. If it identifies queries with excessively long execution times, it suggests improvement measures such as adding indexes or adjusting the query structure. These suggestions are notified to terminals, and administrators can improve performance by following them. In this way, the system enables continuous optimization, leading to improved operational efficiency.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects log data from business applications and associated devices. This includes methods such as API access and reading log files. The collected data is stored in a database.

[0047] Step 2:

[0048] The server preprocesses the collected log data to format it into a parseable form. This process includes standardizing timestamps, filtering out unnecessary data, and cleaning the data.

[0049] Step 3:

[0050] The server feeds pre-processed data into a machine learning algorithm to analyze inefficiencies and error patterns in business activities. The analysis results identify areas that require improvement.

[0051] Step 4:

[0052] Based on the analysis results, the server generates specific improvement measures for the areas that need improvement. These improvement measures utilize a pre-established knowledge base and past case data.

[0053] Step 5:

[0054] The server notifies the user's device of the generated improvement measures. The notification is provided in the form of a pop-up or email and includes specific improvement measures and recommended actions.

[0055] Step 6:

[0056] Users receive notifications and implement the suggested improvements in their work. After implementing the improvements, users report feedback to the server via their terminal.

[0057] Step 7:

[0058] The server collects user feedback and stores it as data to improve analysis accuracy. This feedback will be used to inform future analysis processes and suggest improvements.

[0059] (Example 1)

[0060] 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."

[0061] In conventional business processes, manual analysis of business activity data and consideration of improvement measures require considerable time and effort, sometimes leaving inefficient processes unaddressed for extended periods. Furthermore, it is difficult to consistently formulate quick and optimal improvement measures, resulting in decreased business efficiency. In contrast, the present invention aims to optimize business processes and improve efficiency by rapidly and accurately analyzing business-related information and automatically proposing effective improvement measures.

[0062] 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.

[0063] In this invention, the server includes means for collecting business-related information, means for preprocessing the collected business-related information and converting it into an analyzable format, and means for using the converted information to analyze trends using a machine learning algorithm and identify areas for improvement. This makes it possible to quickly detect inefficiencies in business processes and efficiently generate optimal improvement measures.

[0064] "Business-related information" refers to various data and records generated during business activities, including time data and details of operations.

[0065] "Means of collection" refers to technologies and methods for periodically or continuously acquiring necessary information from business applications and devices.

[0066] "Means of preprocessing and converting data into an analyzable format" refers to the process of preparing raw data into a format suitable for analysis, and includes techniques such as data cleaning and format standardization.

[0067] A "machine learning algorithm" refers to a computational method used to analyze business-related information and perform pattern recognition and anomaly detection, building an optimal model from training data.

[0068] "Means for identifying areas for improvement" refers to techniques that identify inefficient parts or areas with room for improvement in business processes based on analysis results.

[0069] "Methods for constructing improvement measures using generative AI models" refers to technologies that utilize AI technology to propose specific procedures and methods based on historical data and analysis.

[0070] "Means of notifying users via a device" refers to hardware and software interfaces that deliver improvement measures to users in a way that is easy to see and understand.

[0071] "Means for collecting and analyzing responses from users" refers to technologies that collect feedback provided by users and use that feedback to improve the accuracy of future analyses and improvement measures.

[0072] This invention is a system for improving business efficiency and mainly consists of three elements: a server, a terminal, and a user. The entire system automates a series of processes, including the collection, analysis, generation of improvement measures, and notification of these measures, based on business activity data.

[0073] The server performs primary processing and collects business-related information from business applications and associated devices. The hardware used is a standard server computer, and the software includes data collection APIs and log analysis programs. The collected data is formatted into an analyzable format by preprocessing algorithms, and duplicates and outliers are removed.

[0074] Data analysis utilizes machine learning algorithms, employing libraries such as Python's Scikit-learn and TENSORFLOW®. The server uses these algorithms to identify areas for improvement in business processes and then uses generative AI models to construct specific improvement measures. This allows for rapid identification of inefficient aspects of operations and the proposal of optimal improvement solutions to users.

[0075] The terminal serves to notify the user of improvement measures, and a PC or smart device is used as the user interface. The notification is displayed in a format that the user can easily understand and provides details of the improvement measures.

[0076] As a concrete example, consider an improvement process in database management. The server collects execution time data for SQL queries, and if it identifies queries that are taking a long time, it proposes improvements to optimize those queries. These improvements are then notified to the terminal, and by implementing them, users can expect improved performance.

[0077] Examples of prompts generated using AI models include, "Analyze recent business data, identify areas needing improvement, and generate specific suggestions," and "Provide suggestions for improving the execution time of SQL queries when it is too long." These prompts serve as a starting point for data analysis.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server collects business-related data from business applications and devices. The input for this step is log data, including operation history and error information, obtained via APIs. The server periodically collects this data and records it in a database. Specifically, it performs data queries and scans log files to extract necessary information.

[0081] Step 2:

[0082] The server preprocesses the collected log data and converts it into a parseable format. The input for this step is the raw data collected in step 1, and the output is clean data that has been formatted by removing outliers and duplicates. Specifically, it applies a data cleaning algorithm and performs format conversion.

[0083] Step 3:

[0084] The server applies machine learning algorithms to clean data to analyze areas for business improvement. The input for this step is pre-processed data, and the output is identified inefficient processes and error patterns. Specifically, it performs clustering and regression analysis to detect data trends.

[0085] Step 4:

[0086] The server uses a generated AI model based on the analysis results to construct specific improvement measures. The input for this step is the analysis results obtained in step 3, and the output is actionable improvement steps. Specifically, the AI ​​model generates suggestions by referring to previous success stories and the knowledge base.

[0087] Step 5:

[0088] The terminal notifies the user of the improvement suggestions sent from the server. The input for this step is the improvement suggestion data from the server, and the output is the improvement suggestion notification received by the user. Specifically, the terminal displays a pop-up notification showing detailed instructions on the screen.

[0089] Step 6:

[0090] The user acts on the notified improvement measures and sends the results as feedback to the server via their device. The input in this step is the result of the user's improvement measures, and the output is the feedback data received by the server. Specifically, the user implements the improvement measures, records the results in an input form, and submits it.

[0091] Step 7:

[0092] The server uses the collected feedback for analysis to improve the accuracy of subsequent suggestions. The input for this step is user feedback data, and the output is a refined analysis model. Specifically, it analyzes the feedback and adjusts the parameters of the machine learning model.

[0093] (Application Example 1)

[0094] 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."

[0095] In factory automation systems, robots are required to operate efficiently. However, delays and inefficient movement patterns can occur, leading to decreased productivity. Therefore, a system is needed that monitors robot movements in real time, identifies inefficient movements, and proposes appropriate improvement measures. Furthermore, it is necessary to improve the accuracy of the system through feedback from operators.

[0096] 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.

[0097] In this invention, the server includes means for recording information related to business operations, means for analyzing the recorded business activity information and identifying inefficient areas, and means for generating specific improvement measures based on the identified inefficient areas. This makes it possible to efficiently manage the robot's movements and provide improvement suggestions in real time.

[0098] "Work information" refers to all information related to the execution of work, including action details and time information acquired in a work environment such as a factory.

[0099] An "inefficient area" is a part of a task or operation where more time or resources are consumed than usual, and which has been identified as an area where there is room for improvement.

[0100] An "operator" is a person who is responsible for monitoring and managing machinery and systems in a work environment such as a factory.

[0101] "Improvement measures" are specific, actionable suggestions or procedures for efficiency improvements generated for identified inefficient areas.

[0102] "Feedback" refers to information returned to the system by operators regarding the results and opinions of improvement measures implemented.

[0103] The system implementing this invention includes the following components: A server is used to collect and analyze operational information. The server is connected to sensors and control devices to receive operational data from the factory. The data is stored in a database on the server and analyzed using a machine learning framework such as TensorFlow. Inefficient areas identified through the analysis are proposed as specific improvement measures in an improvement measure generation module.

[0104] The terminal serves to notify the operator of improvement suggestions sent from the server. An application is installed on the terminal, visually displaying the improvement suggestions through its user interface. The operator can also input feedback via the terminal, which is then sent to the server. This feedback is used as reference information for subsequent data analysis.

[0105] For example, if an abnormal delay is detected when a factory robot arm is stacking pallets, the system performs data analysis and proposes optimized arm movement patterns and paths. This proposal is displayed on a terminal, and the operator can make corrections, thereby improving work efficiency.

[0106] An example of a prompt message for the AI ​​model generated by this system would be: "Analyze the operation logs of the factory robot and suggest inefficient areas and ways to improve them. Specifically, indicate which operations are delayed and how they can be optimized."

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server collects operational information from the factory through sensors and control devices. This information includes robot operation logs and time information. The collected data is stored in the server's database and prepared for subsequent analysis. The input is real-time data from factory equipment, and the output is operational information stored in the database.

[0110] Step 2:

[0111] The server retrieves business process information stored in the database and uses TensorFlow to analyze inefficient areas. Data processing includes calculations such as comparing patterns of operation and time. This analysis identifies which specific operations require optimization. The input is business process information retrieved from the database, and the output is a list of the analyzed inefficient areas.

[0112] Step 3:

[0113] The server generates improvement measures for inefficient areas based on the analysis results. These generated improvement measures are then compiled into concrete and actionable proposals using past success stories and machine learning models. A generative AI model is used in this process. The input is a list of inefficient areas, and the output is a proposal for specific improvement measures.

[0114] Step 4:

[0115] The server sends the generated improvement suggestions to the terminal. The terminal visually notifies the operator of the received improvement suggestions. The notification is displayed in an easy-to-understand manner, explaining the content of the improvement and serving as an operational guideline. The input is the improvement suggestion from the server, and the output is the notification content displayed on the terminal screen.

[0116] Step 5:

[0117] The user implements the improvement measures displayed on their device and sends the results and feedback to the server via the device. The feedback includes the effects of the implemented improvements and any additional comments. This feedback is stored in the server's database and used for future analysis and suggestions. The input is the result of implementing the improvement measures, and the output is the feedback information.

[0118] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0119] This invention is a system that identifies areas for improvement by recording and analyzing data from business activities, and then generates specific improvement measures and notifies users of them. By combining this system with an emotion engine, it recognizes the user's emotional state and realizes sophisticated improvement suggestions that utilize that information.

[0120] Collection and analysis of business data

[0121] The server collects user operation history and related business data from business applications and associated devices. This data includes time information and details of operations, and is stored in a database on the server.

[0122] The collected data is preprocessed by the server and analyzed using machine learning algorithms. This analysis identifies inefficient areas and error patterns in business processes.

[0123] Recognition of user emotions by an emotion engine

[0124] The emotion engine installed on the server analyzes the user's emotions based on sensor data and user interaction data acquired from the user's terminal. This emotion information is updated in real time and used to evaluate workload and stress levels.

[0125] Emotional data is linked to work activity data and considered when suggesting improvement measures. For example, if a user is experiencing high stress, improvement suggestions that reduce their workload will be generated.

[0126] Generation and notification of improvement measures

[0127] The server generates optimized, specific improvement measures based on the analysis results and emotional state. These improvement measures are then adjusted according to the user's work environment and emotional state.

[0128] The generated improvement measures are notified to the user's device. The notification clearly outlines the actionable steps and expected effects, making it easy for the user to implement them.

[0129] Collecting feedback and using it to inform future proposals.

[0130] After a user implements an improvement measure, feedback is sent from their device to the server. This feedback includes changes in the user's feelings and the degree of improvement in their work efficiency.

[0131] The server collects this feedback and uses it to improve the model. This feedback loop allows the system to continuously improve its accuracy and provide more effective improvement suggestions to users.

[0132] As a concrete example, in customer support operations, the server analyzes the operator's work logs to identify frequently occurring problem areas. Simultaneously, if the emotion engine detects that the operator's stress level is high, it proposes process adjustments to mitigate the root cause, enabling workload management. In this way, the present invention functions as a system that balances improved operational efficiency with user-friendliness.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The server periodically collects operation data and error logs from business applications. This is done using methods such as data retrieval via APIs and reading log files. The collected data is stored in the server's database.

[0136] Step 2:

[0137] The server receives real-time emotional data from the user through the emotion engine installed in the terminal. This emotional data is analyzed based on factors such as voice tone, text input speed, and facial sensor data to evaluate stress levels and emotional states.

[0138] Step 3:

[0139] The server first preprocesses the collected business data, standardizing it and removing unnecessary data. Then, it applies machine learning algorithms to identify inefficient business processes and potential areas for improvement.

[0140] Step 4:

[0141] The server combines emotional data with the analysis results. Based on these results, it generates specific improvement measures that take into account the user's emotional state. For example, if stress levels are high, this may include suggesting workload redistribution or temporary breaks.

[0142] Step 5:

[0143] The generated improvement suggestions are sent from the server to the user's device. The notification includes recommended actions and explanations of their effects, and is designed to be easy for the user to implement.

[0144] Step 6:

[0145] Users record the results of their attempts at improvement measures on their devices and send them to the server as feedback. This feedback includes changes in their emotions after implementation and the degree of improvement in work efficiency.

[0146] Step 7:

[0147] The server improves its analysis model based on feedback data and incorporates these improvements into future recommendations. This allows the system to continuously improve, increasing the accuracy of recommendations to users.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] Conventional systems struggled to provide effective business improvement measures simply by recording and analyzing business activity data, and they also lacked the ability to provide individualized support that considered users' emotional states. As a result, users' stress and workload could not be properly managed, limiting efficient work performance.

[0151] 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.

[0152] In this invention, the server includes means for recording information related to business activities, means for analyzing the recorded business activity information and identifying potential areas for improvement, and means for acquiring emotional data from the terminal and analyzing the user's emotions. This enables the generation of advanced improvement measures that comprehensively consider business activity data and user emotional data, and notification to users.

[0153] "Business activity information" refers to all operation history and related data recorded in business processes, including time information and specific details of operations.

[0154] "Potential areas for improvement" refer to parts of business processes identified through the analysis of business activity information where improvements in efficiency or reductions in errors are expected.

[0155] "Specific improvement measures" refer to specific steps and methods for improving a particular business process, generated based on business activity information and user sentiment data.

[0156] "Emotional data" refers to information that indicates the user's emotional state, and is obtained from sensor data and interaction data.

[0157] "Feedback" refers to information about changes in users' feelings and work efficiency in response to implemented improvements, and is collected to improve the accuracy of the system.

[0158] This system aims to improve the efficiency of business operations and is realized by combining multiple technological elements.

[0159] The server collects user operation history and related data from business applications and associated devices. This data is stored in a database and includes time information and details of the operations performed. The server preprocesses this data, removing noise and formatting it to convert it into a format that is easy to analyze.

[0160] Subsequently, the server uses machine learning algorithms to analyze the pre-processed data. The analysis utilizes libraries such as Python's Scikit-learn to identify inefficient areas in business processes. Based on this identified information, the server generates specific improvement measures.

[0161] Simultaneously, the terminal acquires sensor data from the user and sends it to the server's emotion engine. The server uses this data to analyze the user's emotional state. The emotional information is updated in real time, and the user's stress level and workload are evaluated. The server combines this emotional information with work data to inform improvement measures.

[0162] The generated improvement measures are notified from the server to the user's terminal. The notification includes specific steps and an explanation of the effects that the improvements will have. The user can adjust their business processes based on this information.

[0163] After the user implements the improvement measures, they send feedback from their device to the server. The server analyzes this feedback and uses the collected feedback information to update the machine learning model, improving the accuracy of future analyses.

[0164] As a concrete example, in customer support operations, the server analyzes the operator's work logs to identify frequently occurring problem areas. Simultaneously, if the emotion engine detects a high stress level in the operator, it proposes process adjustments to mitigate the root cause. An example of a prompt message would be, "Considering the emotional state of the operators in customer support operations, please propose process improvements to reduce their workload."

[0165] Thus, this system aims to achieve both improved operational efficiency and reduced user stress.

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

[0167] Step 1:

[0168] The server collects data from business applications and related devices. Inputs include user operation history and application event logs. The server retrieves this data via APIs and stores it in a database, where time information and operation details are recorded.

[0169] Step 2:

[0170] The server preprocesses the collected data. The input is the raw data obtained in step 1. Data processing includes noise reduction and format standardization. The output is data formatted for analysis. Specific operations include the removal of outliers and the conversion of timestamp formats.

[0171] Step 3:

[0172] The server analyzes data using machine learning algorithms. The input data is pre-processed business data. The server uses clustering techniques to identify inefficient areas and error patterns. The output generates information about business areas that need improvement. Specifically, cluster analysis is performed using the Python Scikit-learn library.

[0173] Step 4:

[0174] The device acquires sensor data from the user and sends it to the server. Inputs include the user's biometric information and various sensor data. By sending sensor data, the server can analyze the user's emotional state as output. Specific operations include data capture using the device's camera and microphone.

[0175] Step 5:

[0176] The server analyzes the user's emotions using an emotion engine. Sensor data obtained in the previous step is used as input. An emotion recognition model is used for data processing, and the user's emotion score is obtained as output. This score is updated in real time and used to evaluate workload.

[0177] Step 6:

[0178] The server generates improvement measures based on analysis results and emotional states. Analysis information and emotional scores are taken as inputs simultaneously. The output is a revised business process proposal. Specific examples include suggestions for load reduction and task schedule adjustments. The generated AI model is used to optimize the proposal content.

[0179] Step 7:

[0180] The server notifies the user's terminal of the generated improvement measures. The input is the generated improvement measures. The output is a notification message displayed to the user. Specific actions include pop-up notifications on the terminal and email notifications.

[0181] Step 8:

[0182] After implementing improvements, users generate feedback and send it to the server via their terminal. The input consists of feedback information about the user's work experience and emotional changes. This allows the server to improve the accuracy of future suggestions. As output, the feedback data is used to improve the server's model.

[0183] (Application Example 2)

[0184] 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".

[0185] In daily life, there is a need to improve the quality of life by providing optimal improvement suggestions that take into account the emotional state of the user. However, current systems have struggled to provide effective improvement suggestions by integrating the recognition of the user's emotions with data analysis. To solve this problem, a system is needed that generates real-time improvement measures that reflect the emotional state.

[0186] 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.

[0187] In this invention, the server includes means for providing a method for recording data, means for analyzing the recorded data and identifying potential areas for improvement, means for generating specific improvement measures based on the identified areas for improvement, means for recognizing the user's emotional state, and means for generating optimized improvement suggestions that take the emotional state into consideration. This makes it possible to provide more accurate improvement suggestions based on the user's emotional state and daily data.

[0188] "Data recording methods" refer to technical methods for effectively collecting various types of information related to business activities and daily life, and storing them in a format that can be used for subsequent processing.

[0189] "Means for identifying potential areas for improvement" refers to a processing method that analyzes collected data to identify areas where efficient improvement is expected.

[0190] "Means for generating specific improvement measures" refers to a system for creating specific improvement proposals tailored to the situation, based on identified areas for improvement.

[0191] "Means for recognizing a user's emotional state" refers to technologies that use sensors or devices to measure and analyze a user's emotional state.

[0192] "Means for generating optimized improvement suggestions" refers to a method that considers various data, including the user's emotional state, to provide the most appropriate improvement measures for the given situation.

[0193] This invention aims to build a system that makes the user's daily life more comfortable, centered around a smart home assistant robot installed in the home. The system uses a Raspberry Pi as its main hardware and utilizes software such as TensorFlow and OpenCV to enable emotion recognition and behavioral analysis.

[0194] Specifically, the robot, acting as the terminal, collects information from devices within the home, integrates the data, and sends it to a server. The server analyzes this data to determine daily behavioral patterns and emotional states in real time. During this process, it acquires video data using a camera, performs facial recognition and emotion estimation using OpenCV, and quantifies this as an emotional state using TensorFlow. This generates potential improvement suggestions based on the data analysis, which are then notified to the user at an appropriate time.

[0195] For example, if a user feels tired after dinner and their expression is cloudy, the system might determine this is a sign of stress and suggest changing the lighting to a softer tone and playing relaxing music. Furthermore, since these suggestions are tailored based on a generative AI model, personalized improvements are provided for each user.

[0196] An example of a prompt message utilizing a generative AI model would be, "As a smart home assistant robot, please determine the user's fatigue level and suggest the optimal relaxation plan." In this way, the invention aims to create a comfortable living environment by providing specific suggestions tailored to the user's emotions and lifestyle.

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The robot, acting as the terminal, collects data from smart devices and sensors within the home. Inputs include temperature, humidity, illuminance, and camera video data. Outputs include sending this data to a server. The terminal formats the data and transmits it to the server over the network.

[0200] Step 2:

[0201] The server receives data sent from the terminal and stores it in the database. During this process, the data is timestamped and organized based on identifiers. The input is the output data from step 1, and the output is the organized dataset.

[0202] Step 3:

[0203] The server uses a machine learning model to recognize the user's face from video data and perform emotion analysis. Specifically, it extracts facial features using OpenCV and estimates emotions using a TensorFlow model. The input is the video data from step 2, and the output is numerical data of the estimated emotion state.

[0204] Step 4:

[0205] Based on the collected environmental and emotional data, the server analyzes potential areas for improvement. If emotional states or environmental changes exceed a certain threshold, these are recognized as important areas for improvement. The input is the data from steps 2 and 3, and the output is suggestions for improvement.

[0206] Step 5:

[0207] Using a generative AI model, the server generates optimized improvement suggestions. The generated prompt might include something like, "The user is emotionally fatigued; please adjust the lighting temperature and play relaxing music." The input is the suggestion data from step 4, and the output is a specific suggestion for improvement.

[0208] Step 6:

[0209] Finally, the server sends the generated improvements to the terminal, which then notifies the user. The terminal displays the suggestions via audio or on screen, prompting the user for confirmation. The input is the suggestions from step 5, and the output is the user's action or feedback.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] This invention is a system that automates the recording, analysis, proposal of improvement measures, and notification of business activity data. This system performs its main functions on a server and provides the user interface through a terminal.

[0227] Data collection

[0228] The server collects log data from business applications and associated devices. This log data includes operation history, error information, timestamps, and user actions. Data collection is performed periodically and stored in the server's database.

[0229] Data Analysis

[0230] The server processes the collected business activity data and applies machine learning algorithms to identify areas for improvement. During the analysis, it detects signs of inefficiency in business processes and analyzes frequently occurring operational errors and time-consuming processes.

[0231] Generation of improvement measures

[0232] The server generates appropriate improvement measures for the identified areas for improvement. This process utilizes past success stories and a database of expert knowledge to generate improvement measures that include specific, actionable steps.

[0233] Notifications and feedback

[0234] Improvement suggestions are sent to the user via their device. These suggestions include steps for improvement and points to change. The user receives the notification and implements the improvements as needed. The user's results and feedback are also sent to the server via the device and collected. This feedback allows the server to improve the accuracy of its analysis and use it to inform future suggestions.

[0235] As a concrete example, in database management, the server collects and analyzes SQL query execution time data. If it identifies queries with excessively long execution times, it suggests improvement measures such as adding indexes or adjusting the query structure. These suggestions are notified to terminals, and administrators can improve performance by following them. In this way, the system enables continuous optimization, leading to improved operational efficiency.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The server collects log data from business applications and associated devices. This includes methods such as API access and reading log files. The collected data is stored in a database.

[0239] Step 2:

[0240] The server preprocesses the collected log data to format it into a parseable form. This process includes standardizing timestamps, filtering out unnecessary data, and cleaning the data.

[0241] Step 3:

[0242] The server feeds pre-processed data into a machine learning algorithm to analyze inefficiencies and error patterns in business activities. The analysis results identify areas that require improvement.

[0243] Step 4:

[0244] Based on the analysis results, the server generates specific improvement measures for the areas that need improvement. These improvement measures utilize a pre-established knowledge base and past case data.

[0245] Step 5:

[0246] The server notifies the user's device of the generated improvement measures. The notification is provided in the form of a pop-up or email and includes specific improvement measures and recommended actions.

[0247] Step 6:

[0248] Users receive notifications and implement the suggested improvements in their work. After implementing the improvements, users report feedback to the server via their terminal.

[0249] Step 7:

[0250] The server collects user feedback and stores it as data to improve analysis accuracy. This feedback will be used to inform future analysis processes and suggest improvements.

[0251] (Example 1)

[0252] 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."

[0253] In conventional business processes, manual analysis of business activity data and consideration of improvement measures require considerable time and effort, sometimes leaving inefficient processes unaddressed for extended periods. Furthermore, it is difficult to consistently formulate quick and optimal improvement measures, resulting in decreased business efficiency. In contrast, the present invention aims to optimize business processes and improve efficiency by rapidly and accurately analyzing business-related information and automatically proposing effective improvement measures.

[0254] 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.

[0255] In this invention, the server includes means for collecting business-related information, means for preprocessing the collected business-related information and converting it into an analyzable format, and means for using the converted information to analyze trends using a machine learning algorithm and identify areas for improvement. This makes it possible to quickly detect inefficiencies in business processes and efficiently generate optimal improvement measures.

[0256] "Business-related information" refers to various data and records generated during business activities, including time data and details of operations.

[0257] "Means of collection" refers to technologies and methods for periodically or continuously acquiring necessary information from business applications and devices.

[0258] "Means of preprocessing and converting data into an analyzable format" refers to the process of preparing raw data into a format suitable for analysis, and includes techniques such as data cleaning and format standardization.

[0259] A "machine learning algorithm" refers to a computational method used to analyze business-related information and perform pattern recognition and anomaly detection, building an optimal model from training data.

[0260] "Means for identifying areas for improvement" refers to techniques that identify inefficient parts or areas with room for improvement in business processes based on analysis results.

[0261] "Methods for constructing improvement measures using generative AI models" refers to technologies that utilize AI technology to propose specific procedures and methods based on historical data and analysis.

[0262] "Means of notifying users via a device" refers to hardware and software interfaces that deliver improvement measures to users in a way that is easy to see and understand.

[0263] "Means for collecting and analyzing responses from users" refers to technologies that collect feedback provided by users and use that feedback to improve the accuracy of future analyses and improvement measures.

[0264] This invention is a system for improving business efficiency and mainly consists of three elements: a server, a terminal, and a user. The entire system automates a series of processes, including the collection, analysis, generation of improvement measures, and notification of these measures, based on business activity data.

[0265] The server performs primary processing and collects business-related information from business applications and associated devices. The hardware used is a standard server computer, and the software includes data collection APIs and log analysis programs. The collected data is formatted into an analyzable format by preprocessing algorithms, and duplicates and outliers are removed.

[0266] For data analysis, machine learning algorithms are utilized, employing libraries such as Python's Scikit-learn and TensorFlow. The server uses these algorithms to identify areas for improvement in business processes and then uses generative AI models to construct specific improvement measures. This allows for rapid identification of inefficiencies in operations and the proposal of optimal improvement solutions to users.

[0267] The terminal serves to notify the user of improvement measures, and a PC or smart device is used as the user interface. The notification is displayed in a format that the user can easily understand and provides details of the improvement measures.

[0268] As a concrete example, consider an improvement process in database management. The server collects execution time data for SQL queries, and if it identifies queries that are taking a long time, it proposes improvements to optimize those queries. These improvements are then notified to the terminal, and by implementing them, users can expect improved performance.

[0269] Examples of prompts generated using AI models include, "Analyze recent business data, identify areas needing improvement, and generate specific suggestions," and "Provide suggestions for improving the execution time of SQL queries when it is too long." These prompts serve as a starting point for data analysis.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The server collects business-related data from business applications and devices. The input for this step is log data, including operation history and error information, obtained via APIs. The server periodically collects this data and records it in a database. Specifically, it performs data queries and scans log files to extract necessary information.

[0273] Step 2:

[0274] The server preprocesses the collected log data and converts it into a parseable format. The input for this step is the raw data collected in step 1, and the output is clean data that has been formatted by removing outliers and duplicates. Specifically, it applies a data cleaning algorithm and performs format conversion.

[0275] Step 3:

[0276] The server applies machine learning algorithms to clean data to analyze areas for business improvement. The input for this step is pre-processed data, and the output is identified inefficient processes and error patterns. Specifically, it performs clustering and regression analysis to detect data trends.

[0277] Step 4:

[0278] The server uses a generated AI model based on the analysis results to construct specific improvement measures. The input for this step is the analysis results obtained in step 3, and the output is actionable improvement steps. Specifically, the AI ​​model generates suggestions by referring to previous success stories and the knowledge base.

[0279] Step 5:

[0280] The terminal notifies the user of the improvement measures sent from the server. The input for this step is the data of the improvement measures from the server, and the output is the notification of the improvement proposal received by the user. As a specific operation, a pop-up notification is displayed on the terminal, and detailed procedures are shown on the screen.

[0281] Step 6:

[0282] The user acts based on the notified improvement measures and sends the results as feedback to the server through the terminal. The input for this step is the result of the user's implementation of the improvement measures, and the output is the feedback data received by the server. As a specific operation, the user implements the improvement measures, describes the results in an input form, and sends them.

[0283] Step 7:

[0284] The server utilizes the collected feedback for analysis to improve the accuracy of subsequent proposals. The input for this step is the feedback data from the user, and the output is the adjusted analysis model. As a specific operation, the feedback is analyzed and the parameters of the machine learning model are adjusted.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] In an automated system in a factory, it is required that robots operate efficiently. However, delays in operation and inefficient operation patterns may occur, which may lead to a decrease in productivity. Therefore, a system is needed that monitors the operation of robots in real time, identifies inefficient operations, and proposes appropriate improvement measures. It is also required to improve the accuracy of the system through feedback from operators.

[0288] 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.

[0289] In this invention, the server includes means for recording information related to business operations, means for analyzing the recorded business activity information and identifying inefficient areas, and means for generating specific improvement measures based on the identified inefficient areas. This makes it possible to efficiently manage the robot's movements and provide improvement suggestions in real time.

[0290] "Work information" refers to all information related to the execution of work, including action details and time information acquired in a work environment such as a factory.

[0291] An "inefficient area" is a part of a task or operation where more time or resources are consumed than usual, and which has been identified as an area where there is room for improvement.

[0292] An "operator" is a person who is responsible for monitoring and managing machinery and systems in a work environment such as a factory.

[0293] "Improvement measures" are specific, actionable suggestions or procedures for efficiency improvements generated for identified inefficient areas.

[0294] "Feedback" refers to information returned to the system by operators regarding the results and opinions of improvement measures implemented.

[0295] The system implementing this invention includes the following components: A server is used to collect and analyze operational information. The server is connected to sensors and control devices to receive operational data from the factory. The data is stored in a database on the server and analyzed using a machine learning framework such as TensorFlow. Inefficient areas identified through the analysis are proposed as specific improvement measures in an improvement measure generation module.

[0296] The terminal serves to notify the operator of improvement suggestions sent from the server. An application is installed on the terminal, visually displaying the improvement suggestions through its user interface. The operator can also input feedback via the terminal, which is then sent to the server. This feedback is used as reference information for subsequent data analysis.

[0297] For example, if an abnormal delay is detected when a factory robot arm is stacking pallets, the system performs data analysis and proposes optimized arm movement patterns and paths. This proposal is displayed on a terminal, and the operator can make corrections, thereby improving work efficiency.

[0298] An example of a prompt message for the AI ​​model generated by this system would be: "Analyze the operation logs of the factory robot and suggest inefficient areas and ways to improve them. Specifically, indicate which operations are delayed and how they can be optimized."

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The server collects operational information from the factory through sensors and control devices. This information includes robot operation logs and time information. The collected data is stored in the server's database and prepared for subsequent analysis. The input is real-time data from factory equipment, and the output is operational information stored in the database.

[0302] Step 2:

[0303] The server retrieves the business operation information stored in the database and analyzes the inefficient areas using TensorFlow. Data processing includes operations such as comparing operation patterns and time. Through this analysis, specifically which operations require optimization is identified. The input is the business operation information obtained from the database, and the output is a list of the analyzed inefficient areas.

[0304] Step 3:

[0305] Based on the analysis results, the server generates improvement measures for the inefficient areas. The generated improvement measures are summarized as specific and executable proposals using past success cases and machine learning models. In this process, a generative AI model is used. The input is a list of inefficient areas, and the output is a proposal for specific improvement measures.

[0306] Step 4:

[0307] The server sends the generated improvement measures to the terminal. On the terminal, the received improvement measures are visually notified to the operator. The notification is displayed in a form that clearly explains the content of the improvement and functions as an operation guideline. The input is the proposal for improvement measures from the server, and the output is the content of the notification displayed on the terminal screen.

[0308] Step 5:

[0309] The user implements the improvement measures displayed on the terminal and sends the results and feedback to the server via the terminal. The feedback includes the effects after implementing the improvement measures and additional opinions. This feedback is stored in the server's database and utilized for the next analysis and proposal. The input is the result of implementing the improvement measures, and the output is the feedback information.

[0310] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0311] This invention is a system that identifies areas for improvement by recording and analyzing data from business activities, and then generates specific improvement measures and notifies users of them. By combining this system with an emotion engine, it recognizes the user's emotional state and realizes sophisticated improvement suggestions that utilize that information.

[0312] Collection and analysis of business data

[0313] The server collects user operation history and related business data from business applications and associated devices. This data includes time information and details of operations, and is stored in a database on the server.

[0314] The collected data is preprocessed by the server and analyzed using machine learning algorithms. This analysis identifies inefficient areas and error patterns in business processes.

[0315] Recognition of user emotions by an emotion engine

[0316] The emotion engine installed on the server analyzes the user's emotions based on sensor data and user interaction data acquired from the user's terminal. This emotion information is updated in real time and used to evaluate workload and stress levels.

[0317] Emotional data is linked to work activity data and considered when suggesting improvement measures. For example, if a user is experiencing high stress, improvement suggestions that reduce their workload will be generated.

[0318] Generation and notification of improvement measures

[0319] The server generates optimized, specific improvement measures based on the analysis results and emotional state. These improvement measures are then adjusted according to the user's work environment and emotional state.

[0320] The generated improvement measures are notified to the user's device. The notification clearly outlines the actionable steps and expected effects, making it easy for the user to implement them.

[0321] Collecting feedback and using it to inform future proposals.

[0322] After a user implements an improvement measure, feedback is sent from their device to the server. This feedback includes changes in the user's feelings and the degree of improvement in their work efficiency.

[0323] The server collects this feedback and uses it to improve the model. This feedback loop allows the system to continuously improve its accuracy and provide more effective improvement suggestions to users.

[0324] As a concrete example, in customer support operations, the server analyzes the operator's work logs to identify frequently occurring problem areas. Simultaneously, if the emotion engine detects that the operator's stress level is high, it proposes process adjustments to mitigate the root cause, enabling workload management. In this way, the present invention functions as a system that balances improved operational efficiency with user-friendliness.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The server periodically collects operation data and error logs from business applications. This is done using methods such as data retrieval via APIs and reading log files. The collected data is stored in the server's database.

[0328] Step 2:

[0329] The server receives real-time emotional data from the user through the emotion engine installed in the terminal. This emotional data is analyzed based on factors such as voice tone, text input speed, and facial sensor data to evaluate stress levels and emotional states.

[0330] Step 3:

[0331] The server first preprocesses the collected business data, standardizing it and removing unnecessary data. Then, it applies machine learning algorithms to identify inefficient business processes and potential areas for improvement.

[0332] Step 4:

[0333] The server combines emotional data with the analysis results. Based on these results, it generates specific improvement measures that take into account the user's emotional state. For example, if stress levels are high, this may include suggesting workload redistribution or temporary breaks.

[0334] Step 5:

[0335] The generated improvement suggestions are sent from the server to the user's device. The notification includes recommended actions and explanations of their effects, and is designed to be easy for the user to implement.

[0336] Step 6:

[0337] Users record the results of their attempts at improvement measures on their devices and send them to the server as feedback. This feedback includes changes in their emotions after implementation and the degree of improvement in work efficiency.

[0338] Step 7:

[0339] The server improves its analysis model based on feedback data and incorporates these improvements into future recommendations. This allows the system to continuously improve, increasing the accuracy of recommendations to users.

[0340] (Example 2)

[0341] 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".

[0342] Conventional systems struggled to provide effective business improvement measures simply by recording and analyzing business activity data, and they also lacked the ability to provide individualized support that considered users' emotional states. As a result, users' stress and workload could not be properly managed, limiting efficient work performance.

[0343] 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.

[0344] In this invention, the server includes means for recording information related to business activities, means for analyzing the recorded business activity information and identifying potential areas for improvement, and means for acquiring emotional data from the terminal and analyzing the user's emotions. This enables the generation of advanced improvement measures that comprehensively consider business activity data and user emotional data, and notification to users.

[0345] "Business activity information" refers to all operation history and related data recorded in business processes, including time information and specific details of operations.

[0346] "Potential areas for improvement" refer to parts of business processes identified through the analysis of business activity information where improvements in efficiency or reductions in errors are expected.

[0347] "Specific improvement measures" refer to specific steps and methods for improving a particular business process, generated based on business activity information and user sentiment data.

[0348] "Emotional data" refers to information that indicates the user's emotional state, and is obtained from sensor data and interaction data.

[0349] "Feedback" refers to information about changes in users' feelings and work efficiency in response to implemented improvements, and is collected to improve the accuracy of the system.

[0350] This system aims to improve the efficiency of business operations and is realized by combining multiple technological elements.

[0351] The server collects user operation history and related data from business applications and associated devices. This data is stored in a database and includes time information and details of the operations performed. The server preprocesses this data, removing noise and formatting it to convert it into a format that is easy to analyze.

[0352] Subsequently, the server uses machine learning algorithms to analyze the pre-processed data. The analysis utilizes libraries such as Python's Scikit-learn to identify inefficient areas in business processes. Based on this identified information, the server generates specific improvement measures.

[0353] Simultaneously, the terminal acquires sensor data from the user and sends it to the server's emotion engine. The server uses this data to analyze the user's emotional state. The emotional information is updated in real time, and the user's stress level and workload are evaluated. The server combines this emotional information with work data to inform improvement measures.

[0354] The generated improvement measures are notified from the server to the user's terminal. The notification includes specific steps and an explanation of the effects that the improvements will have. The user can adjust their business processes based on this information.

[0355] After the user implements the improvement measures, they send feedback from their device to the server. The server analyzes this feedback and uses the collected feedback information to update the machine learning model, improving the accuracy of future analyses.

[0356] As a concrete example, in customer support operations, the server analyzes the operator's work logs to identify frequently occurring problem areas. Simultaneously, if the emotion engine detects a high stress level in the operator, it proposes process adjustments to mitigate the root cause. An example of a prompt message would be, "Considering the emotional state of the operators in customer support operations, please propose process improvements to reduce their workload."

[0357] Thus, this system aims to achieve both improved operational efficiency and reduced user stress.

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

[0359] Step 1:

[0360] The server collects data from business applications and related devices. Inputs include user operation history and application event logs. The server retrieves this data via APIs and stores it in a database, where time information and operation details are recorded.

[0361] Step 2:

[0362] The server preprocesses the collected data. The input is the raw data obtained in step 1. Data processing includes noise reduction and format standardization. The output is data formatted for analysis. Specific operations include the removal of outliers and the conversion of timestamp formats.

[0363] Step 3:

[0364] The server analyzes data using machine learning algorithms. The input data is pre-processed business data. The server uses clustering techniques to identify inefficient areas and error patterns. The output generates information about business areas that need improvement. Specifically, cluster analysis is performed using the Python Scikit-learn library.

[0365] Step 4:

[0366] The device acquires sensor data from the user and sends it to the server. Inputs include the user's biometric information and various sensor data. By sending sensor data, the server can analyze the user's emotional state as output. Specific operations include data capture using the device's camera and microphone.

[0367] Step 5:

[0368] The server analyzes the user's emotions using an emotion engine. Sensor data obtained in the previous step is used as input. An emotion recognition model is used for data processing, and the user's emotion score is obtained as output. This score is updated in real time and used to evaluate workload.

[0369] Step 6:

[0370] The server generates improvement measures based on analysis results and emotional states. Analysis information and emotional scores are taken as inputs simultaneously. The output is a revised business process proposal. Specific examples include suggestions for load reduction and task schedule adjustments. The generated AI model is used to optimize the proposal content.

[0371] Step 7:

[0372] The server notifies the user's terminal of the generated improvement measures. The input is the generated improvement measures. The output is a notification message displayed to the user. Specific actions include pop-up notifications on the terminal and email notifications.

[0373] Step 8:

[0374] After implementing improvements, users generate feedback and send it to the server via their terminal. The input consists of feedback information about the user's work experience and emotional changes. This allows the server to improve the accuracy of future suggestions. As output, the feedback data is used to improve the server's model.

[0375] (Application Example 2)

[0376] 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."

[0377] In daily life, there is a need to improve the quality of life by providing optimal improvement suggestions that take into account the emotional state of the user. However, current systems have struggled to provide effective improvement suggestions by integrating the recognition of the user's emotions with data analysis. To solve this problem, a system is needed that generates real-time improvement measures that reflect the emotional state.

[0378] 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.

[0379] In this invention, the server includes means for providing a method for recording data, means for analyzing the recorded data and identifying potential areas for improvement, means for generating specific improvement measures based on the identified areas for improvement, means for recognizing the user's emotional state, and means for generating optimized improvement suggestions that take the emotional state into consideration. This makes it possible to provide more accurate improvement suggestions based on the user's emotional state and daily data.

[0380] "Data recording methods" refer to technical methods for effectively collecting various types of information related to business activities and daily life, and storing them in a format that can be used for subsequent processing.

[0381] "Means for identifying potential areas for improvement" refers to a processing method that analyzes collected data to identify areas where efficient improvement is expected.

[0382] "Means for generating specific improvement measures" refers to a system for creating specific improvement proposals tailored to the situation, based on identified areas for improvement.

[0383] "Means for recognizing a user's emotional state" refers to technologies that use sensors or devices to measure and analyze a user's emotional state.

[0384] "Means for generating optimized improvement suggestions" refers to a method that considers various data, including the user's emotional state, to provide the most appropriate improvement measures for the given situation.

[0385] This invention aims to build a system that makes the user's daily life more comfortable, centered around a smart home assistant robot installed in the home. The system uses a Raspberry Pi as its main hardware and utilizes software such as TensorFlow and OpenCV to enable emotion recognition and behavioral analysis.

[0386] Specifically, the robot, acting as the terminal, collects information from devices within the home, integrates the data, and sends it to a server. The server analyzes this data to determine daily behavioral patterns and emotional states in real time. During this process, it acquires video data using a camera, performs facial recognition and emotion estimation using OpenCV, and quantifies this as an emotional state using TensorFlow. This generates potential improvement suggestions based on the data analysis, which are then notified to the user at an appropriate time.

[0387] For example, if a user feels tired after dinner and their expression is cloudy, the system might determine this is a sign of stress and suggest changing the lighting to a softer tone and playing relaxing music. Furthermore, since these suggestions are tailored based on a generative AI model, personalized improvements are provided for each user.

[0388] An example of a prompt message utilizing a generative AI model would be, "As a smart home assistant robot, please determine the user's fatigue level and suggest the optimal relaxation plan." In this way, the invention aims to create a comfortable living environment by providing specific suggestions tailored to the user's emotions and lifestyle.

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The robot, acting as the terminal, collects data from smart devices and sensors within the home. Inputs include temperature, humidity, illuminance, and camera video data. Outputs include sending this data to a server. The terminal formats the data and transmits it to the server over the network.

[0392] Step 2:

[0393] The server receives data sent from the terminal and stores it in the database. During this process, the data is timestamped and organized based on identifiers. The input is the output data from step 1, and the output is the organized dataset.

[0394] Step 3:

[0395] The server uses a machine learning model to recognize the user's face from video data and perform emotion analysis. Specifically, it extracts facial features using OpenCV and estimates emotions using a TensorFlow model. The input is the video data from step 2, and the output is numerical data of the estimated emotion state.

[0396] Step 4:

[0397] Based on the collected environmental and emotional data, the server analyzes potential areas for improvement. If emotional states or environmental changes exceed a certain threshold, these are recognized as important areas for improvement. The input is the data from steps 2 and 3, and the output is suggestions for improvement.

[0398] Step 5:

[0399] Using a generative AI model, the server generates optimized improvement suggestions. The generated prompt might include something like, "The user is emotionally fatigued; please adjust the lighting temperature and play relaxing music." The input is the suggestion data from step 4, and the output is a specific suggestion for improvement.

[0400] Step 6:

[0401] Finally, the server sends the generated improvements to the terminal, which then notifies the user. The terminal displays the suggestions via audio or on screen, prompting the user for confirmation. The input is the suggestions from step 5, and the output is the user's action or feedback.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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".

[0418] This invention is a system that automates the recording, analysis, proposal of improvement measures, and notification of business activity data. This system performs its main functions on a server and provides the user interface through a terminal.

[0419] Data collection

[0420] The server collects log data from business applications and associated devices. This log data includes operation history, error information, timestamps, and user actions. Data collection is performed periodically and stored in the server's database.

[0421] Data Analysis

[0422] The server processes the collected business activity data and applies machine learning algorithms to identify areas for improvement. During the analysis, it detects signs of inefficiency in business processes and analyzes frequently occurring operational errors and time-consuming processes.

[0423] Generation of improvement measures

[0424] The server generates appropriate improvement measures for the identified areas for improvement. This process utilizes past success stories and a database of expert knowledge to generate improvement measures that include specific, actionable steps.

[0425] Notifications and feedback

[0426] Improvement suggestions are sent to the user via their device. These suggestions include steps for improvement and points to change. The user receives the notification and implements the improvements as needed. The user's results and feedback are also sent to the server via the device and collected. This feedback allows the server to improve the accuracy of its analysis and use it to inform future suggestions.

[0427] As a concrete example, in database management, the server collects and analyzes SQL query execution time data. If it identifies queries with excessively long execution times, it suggests improvement measures such as adding indexes or adjusting the query structure. These suggestions are notified to terminals, and administrators can improve performance by following them. In this way, the system enables continuous optimization, leading to improved operational efficiency.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The server collects log data from business applications and associated devices. This includes methods such as API access and reading log files. The collected data is stored in a database.

[0431] Step 2:

[0432] The server preprocesses the collected log data to format it into a parseable form. This process includes standardizing timestamps, filtering out unnecessary data, and cleaning the data.

[0433] Step 3:

[0434] The server feeds pre-processed data into a machine learning algorithm to analyze inefficiencies and error patterns in business activities. The analysis results identify areas that require improvement.

[0435] Step 4:

[0436] Based on the analysis results, the server generates specific improvement measures for the areas that need improvement. These improvement measures utilize a pre-established knowledge base and past case data.

[0437] Step 5:

[0438] The server notifies the user's device of the generated improvement measures. The notification is provided in the form of a pop-up or email and includes specific improvement measures and recommended actions.

[0439] Step 6:

[0440] Users receive notifications and implement the suggested improvements in their work. After implementing the improvements, users report feedback to the server via their terminal.

[0441] Step 7:

[0442] The server collects user feedback and stores it as data to improve analysis accuracy. This feedback will be used to inform future analysis processes and suggest improvements.

[0443] (Example 1)

[0444] 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."

[0445] In conventional business processes, manual analysis of business activity data and consideration of improvement measures require considerable time and effort, sometimes leaving inefficient processes unaddressed for extended periods. Furthermore, it is difficult to consistently formulate quick and optimal improvement measures, resulting in decreased business efficiency. In contrast, the present invention aims to optimize business processes and improve efficiency by rapidly and accurately analyzing business-related information and automatically proposing effective improvement measures.

[0446] 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.

[0447] In this invention, the server includes means for collecting business-related information, means for preprocessing the collected business-related information and converting it into an analyzable format, and means for using the converted information to analyze trends using a machine learning algorithm and identify areas for improvement. This makes it possible to quickly detect inefficiencies in business processes and efficiently generate optimal improvement measures.

[0448] "Business-related information" refers to various data and records generated during business activities, including time data and details of operations.

[0449] "Means of collection" refers to technologies and methods for periodically or continuously acquiring necessary information from business applications and devices.

[0450] "Means of preprocessing and converting data into an analyzable format" refers to the process of preparing raw data into a format suitable for analysis, and includes techniques such as data cleaning and format standardization.

[0451] A "machine learning algorithm" refers to a computational method used to analyze business-related information and perform pattern recognition and anomaly detection, building an optimal model from training data.

[0452] "Means for identifying areas for improvement" refers to techniques that identify inefficient parts or areas with room for improvement in business processes based on analysis results.

[0453] "Methods for constructing improvement measures using generative AI models" refers to technologies that utilize AI technology to propose specific procedures and methods based on historical data and analysis.

[0454] "Means of notifying users via a device" refers to hardware and software interfaces that deliver improvement measures to users in a way that is easy to see and understand.

[0455] "Means for collecting and analyzing responses from users" refers to technologies that collect feedback provided by users and use that feedback to improve the accuracy of future analyses and improvement measures.

[0456] This invention is a system for improving business efficiency and mainly consists of three elements: a server, a terminal, and a user. The entire system automates a series of processes, including the collection, analysis, generation of improvement measures, and notification of these measures, based on business activity data.

[0457] The server performs primary processing and collects business-related information from business applications and associated devices. The hardware used is a standard server computer, and the software includes data collection APIs and log analysis programs. The collected data is formatted into an analyzable format by preprocessing algorithms, and duplicates and outliers are removed.

[0458] For data analysis, machine learning algorithms are utilized, employing libraries such as Python's Scikit-learn and TensorFlow. The server uses these algorithms to identify areas for improvement in business processes and then uses generative AI models to construct specific improvement measures. This allows for rapid identification of inefficiencies in operations and the proposal of optimal improvement solutions to users.

[0459] The terminal serves to notify the user of improvement measures, and a PC or smart device is used as the user interface. The notification is displayed in a format that the user can easily understand and provides details of the improvement measures.

[0460] As a concrete example, consider an improvement process in database management. The server collects execution time data for SQL queries, and if it identifies queries that are taking a long time, it proposes improvements to optimize those queries. These improvements are then notified to the terminal, and by implementing them, users can expect improved performance.

[0461] Examples of prompts generated using AI models include, "Analyze recent business data, identify areas needing improvement, and generate specific suggestions," and "Provide suggestions for improving the execution time of SQL queries when it is too long." These prompts serve as a starting point for data analysis.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] The server collects business-related data from business applications and devices. The input for this step is log data, including operation history and error information, obtained via APIs. The server periodically collects this data and records it in a database. Specifically, it performs data queries and scans log files to extract necessary information.

[0465] Step 2:

[0466] The server preprocesses the collected log data and converts it into a parseable format. The input for this step is the raw data collected in step 1, and the output is clean data that has been formatted by removing outliers and duplicates. Specifically, it applies a data cleaning algorithm and performs format conversion.

[0467] Step 3:

[0468] The server applies machine learning algorithms to clean data to analyze areas for business improvement. The input for this step is pre-processed data, and the output is identified inefficient processes and error patterns. Specifically, it performs clustering and regression analysis to detect data trends.

[0469] Step 4:

[0470] The server uses a generated AI model based on the analysis results to construct specific improvement measures. The input for this step is the analysis results obtained in step 3, and the output is actionable improvement steps. Specifically, the AI ​​model generates suggestions by referring to previous success stories and the knowledge base.

[0471] Step 5:

[0472] The terminal notifies the user of the improvement suggestions sent from the server. The input for this step is the improvement suggestion data from the server, and the output is the improvement suggestion notification received by the user. Specifically, the terminal displays a pop-up notification showing detailed instructions on the screen.

[0473] Step 6:

[0474] The user acts on the notified improvement measures and sends the results as feedback to the server via their device. The input in this step is the result of the user's improvement measures, and the output is the feedback data received by the server. Specifically, the user implements the improvement measures, records the results in an input form, and submits it.

[0475] Step 7:

[0476] The server uses the collected feedback for analysis to improve the accuracy of subsequent suggestions. The input for this step is user feedback data, and the output is a refined analysis model. Specifically, it analyzes the feedback and adjusts the parameters of the machine learning model.

[0477] (Application Example 1)

[0478] 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."

[0479] In factory automation systems, robots are required to operate efficiently. However, delays and inefficient movement patterns can occur, leading to decreased productivity. Therefore, a system is needed that monitors robot movements in real time, identifies inefficient movements, and proposes appropriate improvement measures. Furthermore, it is necessary to improve the accuracy of the system through feedback from operators.

[0480] 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.

[0481] In this invention, the server includes means for recording information related to business operations, means for analyzing the recorded business activity information and identifying inefficient areas, and means for generating specific improvement measures based on the identified inefficient areas. This makes it possible to efficiently manage the robot's movements and provide improvement suggestions in real time.

[0482] "Work information" refers to all information related to the execution of work, including action details and time information acquired in a work environment such as a factory.

[0483] An "inefficient area" is a part of a task or operation where more time or resources are consumed than usual, and which has been identified as an area where there is room for improvement.

[0484] An "operator" is a person who is responsible for monitoring and managing machinery and systems in a work environment such as a factory.

[0485] "Improvement measures" are specific, actionable suggestions or procedures for efficiency improvements generated for identified inefficient areas.

[0486] "Feedback" refers to information returned to the system by operators regarding the results and opinions of improvement measures implemented.

[0487] The system implementing this invention includes the following components: A server is used to collect and analyze operational information. The server is connected to sensors and control devices to receive operational data from the factory. The data is stored in a database on the server and analyzed using a machine learning framework such as TensorFlow. Inefficient areas identified through the analysis are proposed as specific improvement measures in an improvement measure generation module.

[0488] The terminal serves to notify the operator of improvement suggestions sent from the server. An application is installed on the terminal, visually displaying the improvement suggestions through its user interface. The operator can also input feedback via the terminal, which is then sent to the server. This feedback is used as reference information for subsequent data analysis.

[0489] For example, if an abnormal delay is detected when a factory robot arm is stacking pallets, the system performs data analysis and proposes optimized arm movement patterns and paths. This proposal is displayed on a terminal, and the operator can make corrections, thereby improving work efficiency.

[0490] An example of a prompt message for the AI ​​model generated by this system would be: "Analyze the operation logs of the factory robot and suggest inefficient areas and ways to improve them. Specifically, indicate which operations are delayed and how they can be optimized."

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] The server collects operational information from the factory through sensors and control devices. This information includes robot operation logs and time information. The collected data is stored in the server's database and prepared for subsequent analysis. The input is real-time data from factory equipment, and the output is operational information stored in the database.

[0494] Step 2:

[0495] The server retrieves business process information stored in the database and uses TensorFlow to analyze inefficient areas. Data processing includes calculations such as comparing patterns of operation and time. This analysis identifies which specific operations require optimization. The input is business process information retrieved from the database, and the output is a list of the analyzed inefficient areas.

[0496] Step 3:

[0497] The server generates improvement measures for inefficient areas based on the analysis results. These generated improvement measures are then compiled into concrete and actionable proposals using past success stories and machine learning models. A generative AI model is used in this process. The input is a list of inefficient areas, and the output is a proposal for specific improvement measures.

[0498] Step 4:

[0499] The server sends the generated improvement suggestions to the terminal. The terminal visually notifies the operator of the received improvement suggestions. The notification is displayed in an easy-to-understand manner, explaining the content of the improvement and serving as an operational guideline. The input is the improvement suggestion from the server, and the output is the notification content displayed on the terminal screen.

[0500] Step 5:

[0501] The user implements the improvement measures displayed on their device and sends the results and feedback to the server via the device. The feedback includes the effects of the implemented improvements and any additional comments. This feedback is stored in the server's database and used for future analysis and suggestions. The input is the result of implementing the improvement measures, and the output is the feedback information.

[0502] 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.

[0503] This invention is a system that identifies areas for improvement by recording and analyzing data from business activities, and then generates specific improvement measures and notifies users of them. By combining this system with an emotion engine, it recognizes the user's emotional state and realizes sophisticated improvement suggestions that utilize that information.

[0504] Collection and analysis of business data

[0505] The server collects user operation history and related business data from business applications and associated devices. This data includes time information and details of operations, and is stored in a database on the server.

[0506] The collected data is preprocessed by the server and analyzed using machine learning algorithms. This analysis identifies inefficient areas and error patterns in business processes.

[0507] Recognition of user emotions by an emotion engine

[0508] The emotion engine installed on the server analyzes the user's emotions based on sensor data and user interaction data acquired from the user's terminal. This emotion information is updated in real time and used to evaluate workload and stress levels.

[0509] Emotional data is linked to work activity data and considered when suggesting improvement measures. For example, if a user is experiencing high stress, improvement suggestions that reduce their workload will be generated.

[0510] Generation and notification of improvement measures

[0511] The server generates optimized, specific improvement measures based on the analysis results and emotional state. These improvement measures are then adjusted according to the user's work environment and emotional state.

[0512] The generated improvement measures are notified to the user's device. The notification clearly outlines the actionable steps and expected effects, making it easy for the user to implement them.

[0513] Collecting feedback and using it to inform future proposals.

[0514] After a user implements an improvement measure, feedback is sent from their device to the server. This feedback includes changes in the user's feelings and the degree of improvement in their work efficiency.

[0515] The server collects this feedback and uses it to improve the model. This feedback loop allows the system to continuously improve its accuracy and provide more effective improvement suggestions to users.

[0516] As a concrete example, in customer support operations, the server analyzes the operator's work logs to identify frequently occurring problem areas. Simultaneously, if the emotion engine detects that the operator's stress level is high, it proposes process adjustments to mitigate the root cause, enabling workload management. In this way, the present invention functions as a system that balances improved operational efficiency with user-friendliness.

[0517] The following describes the processing flow.

[0518] Step 1:

[0519] The server periodically collects operation data and error logs from business applications. This is done using methods such as data retrieval via APIs and reading log files. The collected data is stored in the server's database.

[0520] Step 2:

[0521] The server receives real-time emotional data from the user through the emotion engine installed in the terminal. This emotional data is analyzed based on factors such as voice tone, text input speed, and facial sensor data to evaluate stress levels and emotional states.

[0522] Step 3:

[0523] The server first preprocesses the collected business data, standardizing it and removing unnecessary data. Then, it applies machine learning algorithms to identify inefficient business processes and potential areas for improvement.

[0524] Step 4:

[0525] The server combines emotional data with the analysis results. Based on these results, it generates specific improvement measures that take into account the user's emotional state. For example, if stress levels are high, this may include suggesting workload redistribution or temporary breaks.

[0526] Step 5:

[0527] The generated improvement suggestions are sent from the server to the user's device. The notification includes recommended actions and explanations of their effects, and is designed to be easy for the user to implement.

[0528] Step 6:

[0529] Users record the results of their attempts at improvement measures on their devices and send them to the server as feedback. This feedback includes changes in their emotions after implementation and the degree of improvement in work efficiency.

[0530] Step 7:

[0531] The server improves its analysis model based on feedback data and incorporates these improvements into future recommendations. This allows the system to continuously improve, increasing the accuracy of recommendations to users.

[0532] (Example 2)

[0533] 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."

[0534] Conventional systems struggled to provide effective business improvement measures simply by recording and analyzing business activity data, and they also lacked the ability to provide individualized support that considered users' emotional states. As a result, users' stress and workload could not be properly managed, limiting efficient work performance.

[0535] 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.

[0536] In this invention, the server includes means for recording information related to business activities, means for analyzing the recorded business activity information and identifying potential areas for improvement, and means for acquiring emotional data from the terminal and analyzing the user's emotions. This enables the generation of advanced improvement measures that comprehensively consider business activity data and user emotional data, and notification to users.

[0537] "Business activity information" refers to all operation history and related data recorded in business processes, including time information and specific details of operations.

[0538] "Potential areas for improvement" refer to parts of business processes identified through the analysis of business activity information where improvements in efficiency or reductions in errors are expected.

[0539] "Specific improvement measures" refer to specific steps and methods for improving a particular business process, generated based on business activity information and user sentiment data.

[0540] "Emotional data" refers to information that indicates the user's emotional state, and is obtained from sensor data and interaction data.

[0541] "Feedback" refers to information about changes in users' feelings and work efficiency in response to implemented improvements, and is collected to improve the accuracy of the system.

[0542] This system aims to improve the efficiency of business operations and is realized by combining multiple technological elements.

[0543] The server collects user operation history and related data from business applications and associated devices. This data is stored in a database and includes time information and details of the operations performed. The server preprocesses this data, removing noise and formatting it to convert it into a format that is easy to analyze.

[0544] Subsequently, the server uses machine learning algorithms to analyze the pre-processed data. The analysis utilizes libraries such as Python's Scikit-learn to identify inefficient areas in business processes. Based on this identified information, the server generates specific improvement measures.

[0545] Simultaneously, the terminal acquires sensor data from the user and sends it to the server's emotion engine. The server uses this data to analyze the user's emotional state. The emotional information is updated in real time, and the user's stress level and workload are evaluated. The server combines this emotional information with work data to inform improvement measures.

[0546] The generated improvement measures are notified from the server to the user's terminal. The notification includes specific steps and an explanation of the effects that the improvements will have. The user can adjust their business processes based on this information.

[0547] After the user implements the improvement measures, they send feedback from their device to the server. The server analyzes this feedback and uses the collected feedback information to update the machine learning model, improving the accuracy of future analyses.

[0548] As a concrete example, in customer support operations, the server analyzes the operator's work logs to identify frequently occurring problem areas. Simultaneously, if the emotion engine detects a high stress level in the operator, it proposes process adjustments to mitigate the root cause. An example of a prompt message would be, "Considering the emotional state of the operators in customer support operations, please propose process improvements to reduce their workload."

[0549] Thus, this system aims to achieve both improved operational efficiency and reduced user stress.

[0550] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0551] Step 1:

[0552] The server collects data from business applications and related devices. Inputs include user operation history and application event logs. The server retrieves this data via APIs and stores it in a database, where time information and operation details are recorded.

[0553] Step 2:

[0554] The server preprocesses the collected data. The input is the raw data obtained in step 1. Data processing includes noise reduction and format standardization. The output is data formatted for analysis. Specific operations include the removal of outliers and the conversion of timestamp formats.

[0555] Step 3:

[0556] The server analyzes data using machine learning algorithms. The input data is pre-processed business data. The server uses clustering techniques to identify inefficient areas and error patterns. The output generates information about business areas that need improvement. Specifically, cluster analysis is performed using the Python Scikit-learn library.

[0557] Step 4:

[0558] The device acquires sensor data from the user and sends it to the server. Inputs include the user's biometric information and various sensor data. By sending sensor data, the server can analyze the user's emotional state as output. Specific operations include data capture using the device's camera and microphone.

[0559] Step 5:

[0560] The server analyzes the user's emotions using an emotion engine. Sensor data obtained in the previous step is used as input. An emotion recognition model is used for data processing, and the user's emotion score is obtained as output. This score is updated in real time and used to evaluate workload.

[0561] Step 6:

[0562] The server generates improvement measures based on analysis results and emotional states. Analysis information and emotional scores are taken as inputs simultaneously. The output is a revised business process proposal. Specific examples include suggestions for load reduction and task schedule adjustments. The generated AI model is used to optimize the proposal content.

[0563] Step 7:

[0564] The server notifies the user's terminal of the generated improvement measures. The input is the generated improvement measures. The output is a notification message displayed to the user. Specific actions include pop-up notifications on the terminal and email notifications.

[0565] Step 8:

[0566] After implementing improvements, users generate feedback and send it to the server via their terminal. The input consists of feedback information about the user's work experience and emotional changes. This allows the server to improve the accuracy of future suggestions. As output, the feedback data is used to improve the server's model.

[0567] (Application Example 2)

[0568] 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."

[0569] In daily life, there is a need to improve the quality of life by providing optimal improvement suggestions that take into account the emotional state of the user. However, current systems have struggled to provide effective improvement suggestions by integrating the recognition of the user's emotions with data analysis. To solve this problem, a system is needed that generates real-time improvement measures that reflect the emotional state.

[0570] 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.

[0571] In this invention, the server includes means for providing a method for recording data, means for analyzing the recorded data and identifying potential areas for improvement, means for generating specific improvement measures based on the identified areas for improvement, means for recognizing the user's emotional state, and means for generating optimized improvement suggestions that take the emotional state into consideration. This makes it possible to provide more accurate improvement suggestions based on the user's emotional state and daily data.

[0572] "Data recording methods" refer to technical methods for effectively collecting various types of information related to business activities and daily life, and storing them in a format that can be used for subsequent processing.

[0573] "Means for identifying potential areas for improvement" refers to a processing method that analyzes collected data to identify areas where efficient improvement is expected.

[0574] "Means for generating specific improvement measures" refers to a system for creating specific improvement proposals tailored to the situation, based on identified areas for improvement.

[0575] "Means for recognizing a user's emotional state" refers to technologies that use sensors or devices to measure and analyze a user's emotional state.

[0576] "Means for generating optimized improvement suggestions" refers to a method that considers various data, including the user's emotional state, to provide the most appropriate improvement measures for the given situation.

[0577] This invention aims to build a system that makes the user's daily life more comfortable, centered around a smart home assistant robot installed in the home. The system uses a Raspberry Pi as its main hardware and utilizes software such as TensorFlow and OpenCV to enable emotion recognition and behavioral analysis.

[0578] Specifically, the robot, acting as the terminal, collects information from devices within the home, integrates the data, and sends it to a server. The server analyzes this data to determine daily behavioral patterns and emotional states in real time. During this process, it acquires video data using a camera, performs facial recognition and emotion estimation using OpenCV, and quantifies this as an emotional state using TensorFlow. This generates potential improvement suggestions based on the data analysis, which are then notified to the user at an appropriate time.

[0579] For example, if a user feels tired after dinner and their expression is cloudy, the system might determine this is a sign of stress and suggest changing the lighting to a softer tone and playing relaxing music. Furthermore, since these suggestions are tailored based on a generative AI model, personalized improvements are provided for each user.

[0580] An example of a prompt message utilizing a generative AI model would be, "As a smart home assistant robot, please determine the user's fatigue level and suggest the optimal relaxation plan." In this way, the invention aims to create a comfortable living environment by providing specific suggestions tailored to the user's emotions and lifestyle.

[0581] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0582] Step 1:

[0583] The robot, acting as the terminal, collects data from smart devices and sensors within the home. Inputs include temperature, humidity, illuminance, and camera video data. Outputs include sending this data to a server. The terminal formats the data and transmits it to the server over the network.

[0584] Step 2:

[0585] The server receives data sent from the terminal and stores it in the database. During this process, the data is timestamped and organized based on identifiers. The input is the output data from step 1, and the output is the organized dataset.

[0586] Step 3:

[0587] The server uses a machine learning model to recognize the user's face from video data and perform emotion analysis. Specifically, it extracts facial features using OpenCV and estimates emotions using a TensorFlow model. The input is the video data from step 2, and the output is numerical data of the estimated emotion state.

[0588] Step 4:

[0589] Based on the collected environmental and emotional data, the server analyzes potential areas for improvement. If emotional states or environmental changes exceed a certain threshold, these are recognized as important areas for improvement. The input is the data from steps 2 and 3, and the output is suggestions for improvement.

[0590] Step 5:

[0591] Using a generative AI model, the server generates optimized improvement suggestions. The generated prompt might include something like, "The user is emotionally fatigued; please adjust the lighting temperature and play relaxing music." The input is the suggestion data from step 4, and the output is a specific suggestion for improvement.

[0592] Step 6:

[0593] Finally, the server sends the generated improvements to the terminal, which then notifies the user. The terminal displays the suggestions via audio or on screen, prompting the user for confirmation. The input is the suggestions from step 5, and the output is the user's action or feedback.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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".

[0611] This invention is a system that automates the recording, analysis, proposal of improvement measures, and notification of business activity data. This system performs its main functions on a server and provides the user interface through a terminal.

[0612] Data collection

[0613] The server collects log data from business applications and associated devices. This log data includes operation history, error information, timestamps, and user actions. Data collection is performed periodically and stored in the server's database.

[0614] Data Analysis

[0615] The server processes the collected business activity data and applies machine learning algorithms to identify areas for improvement. During the analysis, it detects signs of inefficiency in business processes and analyzes frequently occurring operational errors and time-consuming processes.

[0616] Generation of improvement measures

[0617] The server generates appropriate improvement measures for the identified areas for improvement. This process utilizes past success stories and a database of expert knowledge to generate improvement measures that include specific, actionable steps.

[0618] Notifications and feedback

[0619] Improvement suggestions are sent to the user via their device. These suggestions include steps for improvement and points to change. The user receives the notification and implements the improvements as needed. The user's results and feedback are also sent to the server via the device and collected. This feedback allows the server to improve the accuracy of its analysis and use it to inform future suggestions.

[0620] As a concrete example, in database management, the server collects and analyzes SQL query execution time data. If it identifies queries with excessively long execution times, it suggests improvement measures such as adding indexes or adjusting the query structure. These suggestions are notified to terminals, and administrators can improve performance by following them. In this way, the system enables continuous optimization, leading to improved operational efficiency.

[0621] The following describes the processing flow.

[0622] Step 1:

[0623] The server collects log data from business applications and associated devices. This includes methods such as API access and reading log files. The collected data is stored in a database.

[0624] Step 2:

[0625] The server preprocesses the collected log data to format it into a parseable form. This process includes standardizing timestamps, filtering out unnecessary data, and cleaning the data.

[0626] Step 3:

[0627] The server feeds pre-processed data into a machine learning algorithm to analyze inefficiencies and error patterns in business activities. The analysis results identify areas that require improvement.

[0628] Step 4:

[0629] Based on the analysis results, the server generates specific improvement measures for the areas that need improvement. These improvement measures utilize a pre-established knowledge base and past case data.

[0630] Step 5:

[0631] The server notifies the user's device of the generated improvement measures. The notification is provided in the form of a pop-up or email and includes specific improvement measures and recommended actions.

[0632] Step 6:

[0633] Users receive notifications and implement the suggested improvements in their work. After implementing the improvements, users report feedback to the server via their terminal.

[0634] Step 7:

[0635] The server collects user feedback and stores it as data to improve analysis accuracy. This feedback will be used to inform future analysis processes and suggest improvements.

[0636] (Example 1)

[0637] 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".

[0638] In conventional business processes, manual analysis of business activity data and consideration of improvement measures require considerable time and effort, sometimes leaving inefficient processes unaddressed for extended periods. Furthermore, it is difficult to consistently formulate quick and optimal improvement measures, resulting in decreased business efficiency. In contrast, the present invention aims to optimize business processes and improve efficiency by rapidly and accurately analyzing business-related information and automatically proposing effective improvement measures.

[0639] 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.

[0640] In this invention, the server includes means for collecting business-related information, means for preprocessing the collected business-related information and converting it into an analyzable format, and means for using the converted information to analyze trends using a machine learning algorithm and identify areas for improvement. This makes it possible to quickly detect inefficiencies in business processes and efficiently generate optimal improvement measures.

[0641] "Business-related information" refers to various data and records generated during business activities, including time data and details of operations.

[0642] "Means of collection" refers to technologies and methods for periodically or continuously acquiring necessary information from business applications and devices.

[0643] "Means of preprocessing and converting data into an analyzable format" refers to the process of preparing raw data into a format suitable for analysis, and includes techniques such as data cleaning and format standardization.

[0644] A "machine learning algorithm" refers to a computational method used to analyze business-related information and perform pattern recognition and anomaly detection, building an optimal model from training data.

[0645] "Means for identifying areas for improvement" refers to techniques that identify inefficient parts or areas with room for improvement in business processes based on analysis results.

[0646] "Methods for constructing improvement measures using generative AI models" refers to technologies that utilize AI technology to propose specific procedures and methods based on historical data and analysis.

[0647] "Means of notifying users via a device" refers to hardware and software interfaces that deliver improvement measures to users in a way that is easy to see and understand.

[0648] "Means for collecting and analyzing responses from users" refers to technologies that collect feedback provided by users and use that feedback to improve the accuracy of future analyses and improvement measures.

[0649] This invention is a system for improving business efficiency and mainly consists of three elements: a server, a terminal, and a user. The entire system automates a series of processes, including the collection, analysis, generation of improvement measures, and notification of these measures, based on business activity data.

[0650] The server performs primary processing and collects business-related information from business applications and associated devices. The hardware used is a standard server computer, and the software includes data collection APIs and log analysis programs. The collected data is formatted into an analyzable format by preprocessing algorithms, and duplicates and outliers are removed.

[0651] For data analysis, machine learning algorithms are utilized, employing libraries such as Python's Scikit-learn and TensorFlow. The server uses these algorithms to identify areas for improvement in business processes and then uses generative AI models to construct specific improvement measures. This allows for rapid identification of inefficiencies in operations and the proposal of optimal improvement solutions to users.

[0652] The terminal serves to notify the user of improvement measures, and a PC or smart device is used as the user interface. The notification is displayed in a format that the user can easily understand and provides details of the improvement measures.

[0653] As a concrete example, consider an improvement process in database management. The server collects execution time data for SQL queries, and if it identifies queries that are taking a long time, it proposes improvements to optimize those queries. These improvements are then notified to the terminal, and by implementing them, users can expect improved performance.

[0654] Examples of prompts generated using AI models include, "Analyze recent business data, identify areas needing improvement, and generate specific suggestions," and "Provide suggestions for improving the execution time of SQL queries when it is too long." These prompts serve as a starting point for data analysis.

[0655] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0656] Step 1:

[0657] The server collects business-related data from business applications and devices. The input for this step is log data, including operation history and error information, obtained via APIs. The server periodically collects this data and records it in a database. Specifically, it performs data queries and scans log files to extract necessary information.

[0658] Step 2:

[0659] The server preprocesses the collected log data and converts it into a parseable format. The input for this step is the raw data collected in step 1, and the output is clean data that has been formatted by removing outliers and duplicates. Specifically, it applies a data cleaning algorithm and performs format conversion.

[0660] Step 3:

[0661] The server applies machine learning algorithms to clean data to analyze areas for business improvement. The input for this step is pre-processed data, and the output is identified inefficient processes and error patterns. Specifically, it performs clustering and regression analysis to detect data trends.

[0662] Step 4:

[0663] The server uses a generated AI model based on the analysis results to construct specific improvement measures. The input for this step is the analysis results obtained in step 3, and the output is actionable improvement steps. Specifically, the AI ​​model generates suggestions by referring to previous success stories and the knowledge base.

[0664] Step 5:

[0665] The terminal notifies the user of the improvement suggestions sent from the server. The input for this step is the improvement suggestion data from the server, and the output is the improvement suggestion notification received by the user. Specifically, the terminal displays a pop-up notification showing detailed instructions on the screen.

[0666] Step 6:

[0667] The user acts on the notified improvement measures and sends the results as feedback to the server via their device. The input in this step is the result of the user's improvement measures, and the output is the feedback data received by the server. Specifically, the user implements the improvement measures, records the results in an input form, and submits it.

[0668] Step 7:

[0669] The server uses the collected feedback for analysis to improve the accuracy of subsequent suggestions. The input for this step is user feedback data, and the output is a refined analysis model. Specifically, it analyzes the feedback and adjusts the parameters of the machine learning model.

[0670] (Application Example 1)

[0671] 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".

[0672] In factory automation systems, robots are required to operate efficiently. However, delays and inefficient movement patterns can occur, leading to decreased productivity. Therefore, a system is needed that monitors robot movements in real time, identifies inefficient movements, and proposes appropriate improvement measures. Furthermore, it is necessary to improve the accuracy of the system through feedback from operators.

[0673] 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.

[0674] In this invention, the server includes means for recording information related to business operations, means for analyzing the recorded business activity information and identifying inefficient areas, and means for generating specific improvement measures based on the identified inefficient areas. This makes it possible to efficiently manage the robot's movements and provide improvement suggestions in real time.

[0675] "Work information" refers to all information related to the execution of work, including action details and time information acquired in a work environment such as a factory.

[0676] An "inefficient area" is a part of a task or operation where more time or resources are consumed than usual, and which has been identified as an area where there is room for improvement.

[0677] An "operator" is a person who is responsible for monitoring and managing machinery and systems in a work environment such as a factory.

[0678] "Improvement measures" are specific, actionable suggestions or procedures for efficiency improvements generated for identified inefficient areas.

[0679] "Feedback" refers to information returned to the system by operators regarding the results and opinions of improvement measures implemented.

[0680] The system implementing this invention includes the following components: A server is used to collect and analyze operational information. The server is connected to sensors and control devices to receive operational data from the factory. The data is stored in a database on the server and analyzed using a machine learning framework such as TensorFlow. Inefficient areas identified through the analysis are proposed as specific improvement measures in an improvement measure generation module.

[0681] The terminal serves to notify the operator of improvement suggestions sent from the server. An application is installed on the terminal, visually displaying the improvement suggestions through its user interface. The operator can also input feedback via the terminal, which is then sent to the server. This feedback is used as reference information for subsequent data analysis.

[0682] For example, if an abnormal delay is detected when a factory robot arm is stacking pallets, the system performs data analysis and proposes optimized arm movement patterns and paths. This proposal is displayed on a terminal, and the operator can make corrections, thereby improving work efficiency.

[0683] An example of a prompt message for the AI ​​model generated by this system would be: "Analyze the operation logs of the factory robot and suggest inefficient areas and ways to improve them. Specifically, indicate which operations are delayed and how they can be optimized."

[0684] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0685] Step 1:

[0686] The server collects operational information from the factory through sensors and control devices. This information includes robot operation logs and time information. The collected data is stored in the server's database and prepared for subsequent analysis. The input is real-time data from factory equipment, and the output is operational information stored in the database.

[0687] Step 2:

[0688] The server retrieves business process information stored in the database and uses TensorFlow to analyze inefficient areas. Data processing includes calculations such as comparing patterns of operation and time. This analysis identifies which specific operations require optimization. The input is business process information retrieved from the database, and the output is a list of the analyzed inefficient areas.

[0689] Step 3:

[0690] The server generates improvement measures for inefficient areas based on the analysis results. These generated improvement measures are then compiled into concrete and actionable proposals using past success stories and machine learning models. A generative AI model is used in this process. The input is a list of inefficient areas, and the output is a proposal for specific improvement measures.

[0691] Step 4:

[0692] The server sends the generated improvement suggestions to the terminal. The terminal visually notifies the operator of the received improvement suggestions. The notification is displayed in an easy-to-understand manner, explaining the content of the improvement and serving as an operational guideline. The input is the improvement suggestion from the server, and the output is the notification content displayed on the terminal screen.

[0693] Step 5:

[0694] The user implements the improvement measures displayed on their device and sends the results and feedback to the server via the device. The feedback includes the effects of the implemented improvements and any additional comments. This feedback is stored in the server's database and used for future analysis and suggestions. The input is the result of implementing the improvement measures, and the output is the feedback information.

[0695] 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.

[0696] This invention is a system that identifies areas for improvement by recording and analyzing data from business activities, and then generates specific improvement measures and notifies users of them. By combining this system with an emotion engine, it recognizes the user's emotional state and realizes sophisticated improvement suggestions that utilize that information.

[0697] Collection and analysis of business data

[0698] The server collects user operation history and related business data from business applications and associated devices. This data includes time information and details of operations, and is stored in a database on the server.

[0699] The collected data is preprocessed by the server and analyzed using machine learning algorithms. This analysis identifies inefficient areas and error patterns in business processes.

[0700] Recognition of user emotions by an emotion engine

[0701] The emotion engine installed on the server analyzes the user's emotions based on sensor data and user interaction data acquired from the user's terminal. This emotion information is updated in real time and used to evaluate workload and stress levels.

[0702] Emotional data is linked to work activity data and considered when suggesting improvement measures. For example, if a user is experiencing high stress, improvement suggestions that reduce their workload will be generated.

[0703] Generation and notification of improvement measures

[0704] The server generates optimized, specific improvement measures based on the analysis results and emotional state. These improvement measures are then adjusted according to the user's work environment and emotional state.

[0705] The generated improvement measures are notified to the user's device. The notification clearly outlines the actionable steps and expected effects, making it easy for the user to implement them.

[0706] Collecting feedback and using it to inform future proposals.

[0707] After a user implements an improvement measure, feedback is sent from their device to the server. This feedback includes changes in the user's feelings and the degree of improvement in their work efficiency.

[0708] The server collects this feedback and uses it to improve the model. This feedback loop allows the system to continuously improve its accuracy and provide more effective improvement suggestions to users.

[0709] As a concrete example, in customer support operations, the server analyzes the operator's work logs to identify frequently occurring problem areas. Simultaneously, if the emotion engine detects that the operator's stress level is high, it proposes process adjustments to mitigate the root cause, enabling workload management. In this way, the present invention functions as a system that balances improved operational efficiency with user-friendliness.

[0710] The following describes the processing flow.

[0711] Step 1:

[0712] The server periodically collects operation data and error logs from business applications. This is done using methods such as data retrieval via APIs and reading log files. The collected data is stored in the server's database.

[0713] Step 2:

[0714] The server receives real-time emotional data from the user through the emotion engine installed in the terminal. This emotional data is analyzed based on factors such as voice tone, text input speed, and facial sensor data to evaluate stress levels and emotional states.

[0715] Step 3:

[0716] The server first preprocesses the collected business data, standardizing it and removing unnecessary data. Then, it applies machine learning algorithms to identify inefficient business processes and potential areas for improvement.

[0717] Step 4:

[0718] The server combines emotional data with the analysis results. Based on these results, it generates specific improvement measures that take into account the user's emotional state. For example, if stress levels are high, this may include suggesting workload redistribution or temporary breaks.

[0719] Step 5:

[0720] The generated improvement suggestions are sent from the server to the user's device. The notification includes recommended actions and explanations of their effects, and is designed to be easy for the user to implement.

[0721] Step 6:

[0722] Users record the results of their attempts at improvement measures on their devices and send them to the server as feedback. This feedback includes changes in their emotions after implementation and the degree of improvement in work efficiency.

[0723] Step 7:

[0724] The server improves its analysis model based on feedback data and incorporates these improvements into future recommendations. This allows the system to continuously improve, increasing the accuracy of recommendations to users.

[0725] (Example 2)

[0726] 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".

[0727] Conventional systems struggled to provide effective business improvement measures simply by recording and analyzing business activity data, and they also lacked the ability to provide individualized support that considered users' emotional states. As a result, users' stress and workload could not be properly managed, limiting efficient work performance.

[0728] 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.

[0729] In this invention, the server includes means for recording information related to business activities, means for analyzing the recorded business activity information and identifying potential areas for improvement, and means for acquiring emotional data from the terminal and analyzing the user's emotions. This enables the generation of advanced improvement measures that comprehensively consider business activity data and user emotional data, and notification to users.

[0730] "Business activity information" refers to all operation history and related data recorded in business processes, including time information and specific details of operations.

[0731] "Potential areas for improvement" refer to parts of business processes identified through the analysis of business activity information where improvements in efficiency or reductions in errors are expected.

[0732] "Specific improvement measures" refer to specific steps and methods for improving a particular business process, generated based on business activity information and user sentiment data.

[0733] "Emotional data" refers to information that indicates the user's emotional state, and is obtained from sensor data and interaction data.

[0734] "Feedback" refers to information about changes in users' feelings and work efficiency in response to implemented improvements, and is collected to improve the accuracy of the system.

[0735] This system aims to improve the efficiency of business operations and is realized by combining multiple technological elements.

[0736] The server collects user operation history and related data from business applications and associated devices. This data is stored in a database and includes time information and details of the operations performed. The server preprocesses this data, removing noise and formatting it to convert it into a format that is easy to analyze.

[0737] Subsequently, the server uses machine learning algorithms to analyze the pre-processed data. The analysis utilizes libraries such as Python's Scikit-learn to identify inefficient areas in business processes. Based on this identified information, the server generates specific improvement measures.

[0738] Simultaneously, the terminal acquires sensor data from the user and sends it to the server's emotion engine. The server uses this data to analyze the user's emotional state. The emotional information is updated in real time, and the user's stress level and workload are evaluated. The server combines this emotional information with work data to inform improvement measures.

[0739] The generated improvement measures are notified from the server to the user's terminal. The notification includes specific steps and an explanation of the effects that the improvements will have. The user can adjust their business processes based on this information.

[0740] After the user implements the improvement measures, they send feedback from their device to the server. The server analyzes this feedback and uses the collected feedback information to update the machine learning model, improving the accuracy of future analyses.

[0741] As a concrete example, in customer support operations, the server analyzes the operator's work logs to identify frequently occurring problem areas. Simultaneously, if the emotion engine detects a high stress level in the operator, it proposes process adjustments to mitigate the root cause. An example of a prompt message would be, "Considering the emotional state of the operators in customer support operations, please propose process improvements to reduce their workload."

[0742] Thus, this system aims to achieve both improved operational efficiency and reduced user stress.

[0743] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0744] Step 1:

[0745] The server collects data from business applications and related devices. Inputs include user operation history and application event logs. The server retrieves this data via APIs and stores it in a database, where time information and operation details are recorded.

[0746] Step 2:

[0747] The server preprocesses the collected data. The input is the raw data obtained in step 1. Data processing includes noise reduction and format standardization. The output is data formatted for analysis. Specific operations include the removal of outliers and the conversion of timestamp formats.

[0748] Step 3:

[0749] The server analyzes data using machine learning algorithms. The input data is pre-processed business data. The server uses clustering techniques to identify inefficient areas and error patterns. The output generates information about business areas that need improvement. Specifically, cluster analysis is performed using the Python Scikit-learn library.

[0750] Step 4:

[0751] The device acquires sensor data from the user and sends it to the server. Inputs include the user's biometric information and various sensor data. By sending sensor data, the server can analyze the user's emotional state as output. Specific operations include data capture using the device's camera and microphone.

[0752] Step 5:

[0753] The server analyzes the user's emotions using an emotion engine. Sensor data obtained in the previous step is used as input. An emotion recognition model is used for data processing, and the user's emotion score is obtained as output. This score is updated in real time and used to evaluate workload.

[0754] Step 6:

[0755] The server generates improvement measures based on analysis results and emotional states. Analysis information and emotional scores are taken as inputs simultaneously. The output is a revised business process proposal. Specific examples include suggestions for load reduction and task schedule adjustments. The generated AI model is used to optimize the proposal content.

[0756] Step 7:

[0757] The server notifies the user's terminal of the generated improvement measures. The input is the generated improvement measures. The output is a notification message displayed to the user. Specific actions include pop-up notifications on the terminal and email notifications.

[0758] Step 8:

[0759] After implementing improvements, users generate feedback and send it to the server via their terminal. The input consists of feedback information about the user's work experience and emotional changes. This allows the server to improve the accuracy of future suggestions. As output, the feedback data is used to improve the server's model.

[0760] (Application Example 2)

[0761] 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".

[0762] In daily life, there is a need to improve the quality of life by providing optimal improvement suggestions that take into account the emotional state of the user. However, current systems have struggled to provide effective improvement suggestions by integrating the recognition of the user's emotions with data analysis. To solve this problem, a system is needed that generates real-time improvement measures that reflect the emotional state.

[0763] 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.

[0764] In this invention, the server includes means for providing a method for recording data, means for analyzing the recorded data and identifying potential areas for improvement, means for generating specific improvement measures based on the identified areas for improvement, means for recognizing the user's emotional state, and means for generating optimized improvement suggestions that take the emotional state into consideration. This makes it possible to provide more accurate improvement suggestions based on the user's emotional state and daily data.

[0765] "Data recording methods" refer to technical methods for effectively collecting various types of information related to business activities and daily life, and storing them in a format that can be used for subsequent processing.

[0766] "Means for identifying potential areas for improvement" refers to a processing method that analyzes collected data to identify areas where efficient improvement is expected.

[0767] "Means for generating specific improvement measures" refers to a system for creating specific improvement proposals tailored to the situation, based on identified areas for improvement.

[0768] "Means for recognizing a user's emotional state" refers to technologies that use sensors or devices to measure and analyze a user's emotional state.

[0769] "Means for generating optimized improvement suggestions" refers to a method that considers various data, including the user's emotional state, to provide the most appropriate improvement measures for the given situation.

[0770] This invention aims to build a system that makes the user's daily life more comfortable, centered around a smart home assistant robot installed in the home. The system uses a Raspberry Pi as its main hardware and utilizes software such as TensorFlow and OpenCV to enable emotion recognition and behavioral analysis.

[0771] Specifically, the robot, acting as the terminal, collects information from devices within the home, integrates the data, and sends it to a server. The server analyzes this data to determine daily behavioral patterns and emotional states in real time. During this process, it acquires video data using a camera, performs facial recognition and emotion estimation using OpenCV, and quantifies this as an emotional state using TensorFlow. This generates potential improvement suggestions based on the data analysis, which are then notified to the user at an appropriate time.

[0772] For example, if a user feels tired after dinner and their expression is cloudy, the system might determine this is a sign of stress and suggest changing the lighting to a softer tone and playing relaxing music. Furthermore, since these suggestions are tailored based on a generative AI model, personalized improvements are provided for each user.

[0773] An example of a prompt message utilizing a generative AI model would be, "As a smart home assistant robot, please determine the user's fatigue level and suggest the optimal relaxation plan." In this way, the invention aims to create a comfortable living environment by providing specific suggestions tailored to the user's emotions and lifestyle.

[0774] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0775] Step 1:

[0776] The robot, acting as the terminal, collects data from smart devices and sensors within the home. Inputs include temperature, humidity, illuminance, and camera video data. Outputs include sending this data to a server. The terminal formats the data and transmits it to the server over the network.

[0777] Step 2:

[0778] The server receives data sent from the terminal and stores it in the database. During this process, the data is timestamped and organized based on identifiers. The input is the output data from step 1, and the output is the organized dataset.

[0779] Step 3:

[0780] The server uses a machine learning model to recognize the user's face from video data and perform emotion analysis. Specifically, it extracts facial features using OpenCV and estimates emotions using a TensorFlow model. The input is the video data from step 2, and the output is numerical data of the estimated emotion state.

[0781] Step 4:

[0782] Based on the collected environmental and emotional data, the server analyzes potential areas for improvement. If emotional states or environmental changes exceed a certain threshold, these are recognized as important areas for improvement. The input is the data from steps 2 and 3, and the output is suggestions for improvement.

[0783] Step 5:

[0784] Using a generative AI model, the server generates optimized improvement suggestions. The generated prompt might include something like, "The user is emotionally fatigued; please adjust the lighting temperature and play relaxing music." The input is the suggestion data from step 4, and the output is a specific suggestion for improvement.

[0785] Step 6:

[0786] Finally, the server sends the generated improvements to the terminal, which then notifies the user. The terminal displays the suggestions via audio or on screen, prompting the user for confirmation. The input is the suggestions from step 5, and the output is the user's action or feedback.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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."

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] The following is further disclosed regarding the embodiments described above.

[0809] (Claim 1)

[0810] A means of providing a method for recording data in business activities,

[0811] A means for analyzing the recorded business activity data and identifying potential areas for improvement,

[0812] Means for generating specific improvement measures based on the identified improvement areas,

[0813] A system including means for notifying users of the aforementioned improvement measures.

[0814] (Claim 2)

[0815] The system according to claim 1, wherein the aforementioned business activity data includes time information and operation details.

[0816] (Claim 3)

[0817] The system according to claim 1, wherein the notification means further includes means for collecting feedback on the implemented improvement measures.

[0818] "Example 1"

[0819] (Claim 1)

[0820] Means of collecting business-related information,

[0821] The means for preprocessing the collected business-related information and converting it into an analyzable format,

[0822] A means for analyzing trends using the converted information and identifying areas for improvement using a machine learning algorithm,

[0823] A means for constructing improvement measures using a generative AI model based on the identified improvement areas,

[0824] Means for notifying the user of the aforementioned improvement measures via the device,

[0825] A means for collecting and analyzing user responses to the aforementioned improvement measures,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the aforementioned business-related information includes time data and operation details.

[0829] (Claim 3)

[0830] The system according to claim 1, wherein the user's response includes evaluation information for the implemented improvement measures.

[0831] "Application Example 1"

[0832] (Claim 1)

[0833] Means for recording information related to work tasks,

[0834] A means for analyzing the recorded business activity information and identifying inefficient areas,

[0835] Means for generating specific improvement measures based on the identified inefficient areas,

[0836] A means for notifying the operator of the aforementioned improvement measures,

[0837] A system including means for collecting feedback from the aforementioned operator.

[0838] (Claim 2)

[0839] The system according to claim 1, wherein the aforementioned business operation information includes time information and operation details.

[0840] (Claim 3)

[0841] The system according to claim 1, wherein the analysis means further includes means for suggesting improvements to the operation using real-time information obtained from a data acquisition device.

[0842] "Example 2 of combining an emotion engine"

[0843] (Claim 1)

[0844] Means of recording information in business activities,

[0845] A means for analyzing the recorded business activity information and identifying potential areas for improvement,

[0846] Means for generating specific improvement measures based on the identified improvement areas and the user's emotional state,

[0847] A means for notifying the user's terminal of the aforementioned improvement measures,

[0848] A means for acquiring emotional data from the aforementioned terminal and analyzing the user's emotions,

[0849] A means of linking emotional information to business activity information and considering it in generating improvement measures,

[0850] A means of collecting feedback and improving analysis accuracy,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, wherein the aforementioned business activity information includes time information and operation details.

[0854] (Claim 3)

[0855] The system according to claim 1, wherein the notification means further includes means for collecting feedback on the implemented improvement measures and changes in the user's feelings.

[0856] "Application example 2 when combining with an emotional engine"

[0857] (Claim 1)

[0858] A means of providing a method for recording data,

[0859] Means for analyzing the recorded data and identifying potential areas for improvement,

[0860] Means for generating specific improvement measures based on the identified improvement areas,

[0861] A means of notifying users of the aforementioned improvement measures,

[0862] Means for recognizing the emotional state of the user,

[0863] A system including means for generating optimized improvement suggestions that take the aforementioned emotional state into consideration.

[0864] (Claim 2)

[0865] The system according to claim 1, wherein the data includes time information and operation details.

[0866] (Claim 3)

[0867] The system according to claim 1, wherein the notification means further includes means for collecting feedback on the implemented improvement measures and analyzing the relationship between emotional states and business data based on the feedback. [Explanation of Symbols]

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

Claims

1. A means of providing a method for recording data in business activities, A means for analyzing the recorded business activity data and identifying potential areas for improvement, Means for generating specific improvement measures based on the identified improvement areas, A system including means for notifying users of the aforementioned improvement measures.

2. The system according to claim 1, wherein the aforementioned business activity data includes time information and operation details.

3. The system according to claim 1, wherein the notification means further includes means for collecting feedback on the implemented improvement measures.

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