system

Abstract

We provide the system. [Solution] A means of collecting business activity data from business terminals, A means for transferring the aforementioned business activity data to a central server, The central server includes means for analyzing behavioral patterns based on the business activity data, A means for generating suggestions for improving work efficiency based on the results of the analysis of the aforementioned behavioral patterns, A means of notifying users of the aforementioned proposal on a monthly basis, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. <9000010>

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional methods for improving work efficiency perform general improvement measures and overall improvement of individual employees' capabilities at once. Therefore, they are not optimized for individual work styles and have the problem that it is difficult to efficiently train personnel. Also, it takes a great deal of time and effort to grasp employees' work patterns and provide appropriate feedback, which is a great burden on management positions.

Means for Solving the Problems

[0005] This invention provides a system that transfers data collected from business terminals to a central server and analyzes behavioral patterns using a generated AI model, thereby generating individualized business efficiency improvement suggestions. This allows for the automatic generation and monthly provision of customized efficiency improvement suggestions for each employee, reducing the burden on managers while improving overall business efficiency.

[0006] A "business terminal" refers to an electronic device, such as a computer or tablet, used by an employee for work purposes, specifically for collecting work activity data.

[0007] "Business activity data" refers to data generated by employees through their operations on work terminals, and specifically includes information such as application usage, operation history, keystrokes, and mouse movements.

[0008] A "central server" is a computer system that centrally manages and analyzes business activity data transferred from multiple business terminals.

[0009] "Behavioral patterns" are models that show employee time allocation, work procedures, and efficiency trends in their work, extracted based on business activity data.

[0010] A "generative AI model" is a model that uses artificial intelligence technology to analyze business activity data and automatically generate patterns and suggestions for improving business efficiency.

[0011] "Efficiency improvement proposals" refer to improvement measures, methods, and advice on specific behavioral changes for business processes, which are generated based on analysis.

[0012] "Feedback" is the process of providing users with analysis results and suggestions for efficiency improvements, and communicating guidelines for improving business operations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] This invention utilizes a system combining employee terminals and a central server to promote business efficiency. Specifically, employee terminals collect data on employees' work activities and transfer this data to the central server. This data includes information such as application usage, keystrokes, and mouse movements.

[0035] The central server centrally manages work activity data transmitted from numerous work terminals and analyzes this data using a generating AI model. The purpose of the analysis is to reveal the daily work patterns of each employee and derive suggestions for improving individual work efficiency. The generated suggestions include specific work method improvement proposals and advice for skill development.

[0036] For example, if a user spends a lot of time processing emails in the morning, a generative AI model might suggest ways to optimize that time. In this case, the central server would suggest efficiency improvements such as using shortcuts or prioritizing email processing.

[0037] Based on monthly feedback, users can implement business improvement measures proposed by a central server. This feedback includes not only past work patterns but also guidelines for future efficiency improvements, enabling users to clarify their daily work goals and enhance their skills. Thus, the present invention provides a practical method that contributes to business efficiency.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The device activates a work activity tracker and collects data on the employee's work activities. The collected data includes application usage time, keyboard input, and mouse movements.

[0041] Step 2:

[0042] The device periodically collects data and temporarily saves it to local storage. The saved data is later sent to a server for analysis.

[0043] Step 3:

[0044] The terminal transfers locally stored data to a central server at regular intervals. During this process, the data is encrypted using security protocols.

[0045] Step 4:

[0046] The server collects the business activity data it receives and performs preprocessing such as formatting standardization and noise removal.

[0047] Step 5:

[0048] The server generates pre-processed data, which is then input into an AI model to identify individual employee behavior patterns. The AI ​​model uses this data to analyze how to improve work efficiency.

[0049] Step 6:

[0050] Based on the analysis results, the server generates personalized work efficiency suggestions for each employee. These suggestions include prioritizing tasks and optimizing work procedures.

[0051] Step 7:

[0052] Users receive monthly feedback supplied from the server and apply individual suggestions to their work. This feedback includes past performance and new work improvement proposals.

[0053] Step 8:

[0054] The system experimentally implements user-proposed improvements and utilizes the results in the data collection process for the following month. This creates a mechanism that continuously promotes operational efficiency.

[0055] (Example 1)

[0056] 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."

[0057] In today's world, where work is becoming increasingly diverse and efficient work execution is essential, there is a problem in accurately understanding the actions of individual employees and proposing optimal work efficiency improvements on a case-by-case basis. Furthermore, there are limited systems that can efficiently collect and analyze work activity information and provide appropriate feedback to users based on that information.

[0058] 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.

[0059] In this invention, the server includes means for storing work activity information in an integrated database, means for analyzing behavioral patterns based on the work activity information, and means for providing suggestions as reports that users can implement. This enables personalized suggestions for improving work efficiency for each user, and allows for continuous improvement and streamlining of operations.

[0060] "Business equipment" refers to devices and terminals used to collect data related to business activities.

[0061] "Work activity information" refers to data related to various activities performed by employees in the course of their work, including application usage and input device operation status.

[0062] A "central information processing system" refers to a central server that manages and analyzes work activity information collected from multiple business devices.

[0063] An "integrated database" is a data storage system that systematically stores work activity information in various formats and types for use in subsequent analysis and report generation.

[0064] "Analysis of behavioral patterns" refers to the process of analyzing collected work activity information using statistical methods and machine learning models to evaluate employee behavioral tendencies and efficiency in their work.

[0065] A "generative artificial intelligence model" refers to a machine learning algorithm or neural network model used to extract useful patterns and insights from large amounts of data and generate new suggestions or predictions.

[0066] "Providing as a report" means organizing the analysis results and proposed solutions, and presenting them in a documented format that users can easily understand and implement.

[0067] As a form of implementing the invention, this system combines business equipment, a central information processing unit, and a generative AI model to support the efficiency of business operations. First, the business equipment collects work activity information generated when each employee performs their tasks. This work activity information includes detailed data such as application usage, keyboard input, and mouse operations.

[0068] Business equipment securely transfers collected information to a central information processing system. This transfer utilizes encrypted communication protocols such as HTTPS to ensure data security. The central information processing system stores work activity information in an integrated database, enabling efficient management and analysis.

[0069] The server analyzes the collected data using a generative artificial intelligence model. This identifies each employee's behavioral patterns and generates suggestions to help improve inefficient aspects of their work. This generative AI model is developed using machine learning libraries such as TENSORFLOW® and PyTorch.

[0070] Based on the analysis results, the server creates a report with specific suggestions for improving work efficiency and provides it to the user. The user can then incorporate the suggested improvements into their work based on this report. For example, if a user spends a certain amount of time each day focusing on responding to emails, the server will suggest templating and prioritizing email processing to facilitate efficient time management.

[0071] An example of a prompt message would be, "Based on the data collected from business equipment, generate suggestions for optimal work efficiency improvements for each employee." By inputting this prompt into the AI ​​model, it becomes possible to propose specific work efficiency improvement measures.

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

[0073] Step 1:

[0074] The terminal collects various work activity information in real time during work, such as application usage, keyboard input, and mouse operations. Input is user operation data from the terminal, and output is the collected work activity information. This information is temporarily recorded within the terminal and prepared for subsequent processing.

[0075] Step 2:

[0076] The terminal collects work activity information at regular intervals and transfers it to the central information processing unit using an industrial standard encryption protocol, such as HTTPS. The input is work activity information, and the output is encrypted data that arrives at the central information processing unit via a secure communication path.

[0077] Step 3:

[0078] The server stores received work activity information in an integrated database. Input is encrypted work activity information transferred from terminals, and output is data in a unified format stored in the database. This allows for efficient management of large amounts of data and preparation for subsequent analysis.

[0079] Step 4:

[0080] The server runs a generative artificial intelligence model using work activity information stored in an integrated database. This AI model analyzes behavioral patterns and identifies inefficiencies. The input is work activity information stored in the database, and the output is each user's work behavioral patterns and the insights derived from them.

[0081] Step 5:

[0082] The server generates specific business efficiency improvement suggestions based on the insights output by the AI ​​model. The input is the result of behavioral pattern analysis, and the output is a report containing individually optimized business improvement proposals. These suggestions include advice on how to improve business processes and how to make better use of time.

[0083] Step 6:

[0084] The server provides users with a report containing the generated business efficiency suggestions. The input is a report of effective business improvement proposals, and the output is monthly feedback received by the user. Users can then use this feedback to improve their operations.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] There is a need to improve the productivity of the entire organization by streamlining operations and proposing optimal operating patterns for machinery used in factories. However, conventional systems have not been able to fully utilize information on individual work activities and machine operation, making it difficult to generate efficient suggestions.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes means for collecting activity information from business equipment, means for transferring the activity information to a central processing unit, means for analyzing behavioral trends based on the activity information in the central processing unit, and means for analyzing the operation information of factory machinery and proposing optimizations. This enables improved efficiency in business operations and improved operational efficiency of machinery used in factories.

[0090] "Business equipment" refers to electronic devices used to collect data related to business operations.

[0091] "Activity information" refers to data on business usage and performance acquired through business equipment.

[0092] A "central processing unit" is a computer system that manages and analyzes data collected from multiple terminals.

[0093] "Behavioral tendencies" refer to patterns that show the consistency and repetitive characteristics of users' activities in the course of work.

[0094] "Factory machinery" refers to automated equipment used in the manufacturing or production process.

[0095] "Operational information" refers to data related to the operating status of factory machinery, work efficiency, energy consumption, etc.

[0096] "Methods for proposing optimization" refers to the process of deriving improvement proposals to streamline operations or machine behavior based on collected data.

[0097] This invention is a system that transfers activity information collected from business equipment to a central processing unit, performs data analysis using a generated AI model, and generates efficiency improvement suggestions. Specifically, business equipment collects data related to the work at the work site and transmits it to the central processing unit. The central processing unit manages the collected activity information using a database management system, while using a programming language such as Python as a server.

[0098] The server analyzes behavioral trends based on collected data and utilizes generative AI models to derive suggestions for optimizing and improving the efficiency of operations. This not only improves the efficiency of business activities but also optimizes factory machinery. For example, if the server detects that a certain piece of equipment is consuming excessive energy during a specific time period, it can analyze that data and suggest more efficient usage times and methods.

[0099] Users receive suggestions monthly and can use them to implement specific business improvements and machine operation optimizations to increase efficiency. This enables improvements in both individual tasks and overall productivity. An example of a prompt for the generated AI model is, "Analyze the usage patterns of energy-intensive devices and suggest new operating methods to improve efficiency."

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The terminal collects activity information through business equipment. It acquires operation data and input information from users performing their tasks using sensors and logs, and organizes this information as activity data. Inputs include keyboard input, mouse operations, and application usage time, which are provided as data streams. The output is the collected activity information.

[0103] Step 2:

[0104] The terminal transfers the collected activity information to the central processing unit (server). The terminal securely encrypts the obtained data and transmits it over the network. The input is organized activity information. By securely transmitting the data using an encryption protocol, the output is the activity information stored on the server.

[0105] Step 3:

[0106] The server analyzes collected activity information to identify behavioral trends. It queries the data stored on the server using a database management system and detects behavioral characteristics using a generated AI model. The input is accumulated activity information. After data analysis, it outputs characteristic data indicating behavioral trends.

[0107] Step 4:

[0108] The server generates suggestions for efficiency improvements using a generated AI model. It prompts the AI ​​model using prompt messages to request processing and generate optimization suggestions. The input is characteristic data of behavioral tendencies. Through the AI ​​model's dedicated processing, data suggesting work efficiency improvements is output.

[0109] Step 5:

[0110] The server notifies users of monthly efficiency improvement suggestions. The generated suggestions are compiled into a report and delivered to the user's device. The input is efficiency improvement suggestion data. The notification function is used to present the generated suggestions to the user.

[0111] Step 6:

[0112] Users improve their work processes based on suggestions received from the server. They review the efficiency improvements they receive and incorporate them into their daily workflows and machine operation procedures. The input is a suggestion report from the server. Ultimately, the output is improved work efficiency and increased productivity.

[0113] 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.

[0114] This invention provides a system that combines a business terminal, a central server, and an emotion engine that recognizes user emotions to achieve more advanced and adaptive business efficiency. Specifically, the business terminal measures employees' work activities, including application usage time, input history, and device operation status. In addition, the emotion engine installed in the terminal recognizes the user's emotions and collects emotion data in real time. This data is acquired using facial recognition and voice analysis technologies.

[0115] The collected work activity data and emotional data are securely transferred to a central server at regular intervals. On the server, a generating AI model analyzes the behavioral and emotional patterns of each employee based on this data. By considering both behavioral and emotional aspects, highly accurate suggestions for improving individual work efficiency become possible.

[0116] For example, if a user is experiencing negative emotions (e.g., stress or decreased concentration) while performing a specific task, the server can use this information to generate feedback suggesting a break or reconsidering the order of tasks. These suggestions take into account the alignment between the work situation and the user's emotional state, providing more realistic and effective improvement plans.

[0117] Every month, users receive personalized improvement suggestions based on analysis from the server. The feedback integrates past behaviors and emotional patterns, allowing users to improve their work efficiency and emotional management. As a result, the system contributes not only to increased work efficiency but also to improved employee well-being.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] When the workday begins, the terminal activates the activity tracker and emotion engine, starting to collect employee work activity data and emotion data. Work activity data includes application usage time, keyboard input, and mouse movements, while emotion data is collected through facial recognition and voice analysis.

[0121] Step 2:

[0122] The terminal temporarily stores the business activity data and sentiment data collected in real time in local storage. This allows for the integration of diverse data, which can then be used for later analysis.

[0123] Step 3:

[0124] The terminal periodically transfers encrypted business activity data and emotional data to a central server. Strict security management is in place during this process, and measures are in place to prevent unauthorized access.

[0125] Step 4:

[0126] The server aggregates the received data and performs preprocessing such as formatting standardization and removing outliers. This prepares a dataset suitable for analysis.

[0127] Step 5:

[0128] The server analyzes pre-processed data using a generated AI model to identify each employee's behavioral and emotional patterns. Here, the AI ​​utilizes machine learning algorithms to perform highly accurate analysis.

[0129] Step 6:

[0130] The server generates personalized work efficiency suggestions based on the analysis results. These suggestions take into account the user's emotional state and include advice to adjust workloads and encourage focus on specific tasks.

[0131] Step 7:

[0132] Users receive monthly feedback. The feedback evaluates both behavioral and emotional patterns, identifying areas for improvement and suggesting specific solutions.

[0133] Step 8:

[0134] Based on user-submitted suggestions, practical improvements are implemented, and the resulting new data is fed back into the next analysis cycle. This enhances individual adaptability and achieves sustainable operational efficiency.

[0135] (Example 2)

[0136] 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".

[0137] In conventional business management systems, suggestions to improve user work efficiency were often based solely on activity data related to the actual work. However, since users' emotions and mental state also significantly impact work efficiency, suggestions that do not consider these factors are insufficiently optimized. Therefore, there is a need for a system that can integrate and analyze work-related data and emotional information to provide individually customized suggestions.

[0138] 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.

[0139] In this invention, the server includes means for collecting business-related information from business equipment, means for analyzing behavioral characteristics based on business-related information and user emotional information, and means for generating work efficiency improvement suggestions based on the analysis results of behavioral and emotional characteristics. This makes it possible to generate accurate and personalized work improvement suggestions that take into account each user's individual work activities and emotional state.

[0140] "Business equipment" refers to devices and equipment used to collect data on users' daily work activities.

[0141] "Business-related information" refers to a collection of data collected through business equipment, such as user application usage time, input history, and device operation status.

[0142] A "central computer" refers to a central device or system that receives and stores business-related information and user sentiment information, and processes and analyzes that data.

[0143] "Emotional information" refers to data that represents the user's emotional state and is collected using facial recognition technology and voice analysis technology.

[0144] "Behavioral characteristics" refer to patterns and tendencies in a user's work activities, analyzed based on work-related information.

[0145] "Emotional characteristics" are data that shows the tendencies and patterns of a user's emotions, obtained through the analysis of emotional information.

[0146] A "generative AI model" is an artificial intelligence model used in data analysis and proposal generation. It has the ability to learn from large datasets and generate various outputs.

[0147] A "prompt statement" is an input statement used to give instructions to a generative AI model, and it plays a role in directing the content of the output that will be generated.

[0148] This invention aims to realize a system that improves operational efficiency and manages user emotions. The system has multiple functions and mainly includes operational equipment, a central computer, and emotion recognition technology.

[0149] Business equipment is a device that monitors users' daily work activities. This includes computers, smart devices, and dedicated business support terminals. Business equipment continuously records and collects business-related information such as application usage time, input history, and device operation status.

[0150] Furthermore, the professional equipment is equipped with an emotion engine that includes facial recognition and voice analysis technologies to collect user emotional information. This allows for the analysis of emotions from the user's facial expressions and voice, and the emotional information is converted into data in real time. Specifically, high-resolution cameras and high-sensitivity microphones are used as hardware, and voice recognition algorithms are operated as software.

[0151] The collected data is transferred to a central computer via a secure protocol at regular intervals. The central computer uses a generative AI model to process and analyze work-related information and emotional information in an integrated manner, thereby deriving behavioral and emotional characteristics for each user.

[0152] The server utilizes a generative AI model to provide specific suggestions based on analysis results, supporting the user's work efficiency. These suggestions include reordering tasks and recommending breaks. For example, if the server detects a decline in the user's concentration due to prolonged, continuous data entry, it will recommend taking a short break. These suggestions are generated by providing instructions to the generative AI model using prompt statements.

[0153] As a concrete example, consider the prompt: "How can I analyze user business data and real-time sentiment data to generate useful feedback for improving work efficiency?"

[0154] Based on the feedback provided, users can improve their work attitudes and manage their emotions. This leads to increased work efficiency and improved user well-being.

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

[0156] Step 1:

[0157] The terminal, as a business device, collects user work-related information. Specifically, it records application usage time, input history, and device operation status. Inputs include log data and operation history from various devices, which are saved to the terminal's storage in real time. Output is a dataset of work-related information.

[0158] Step 2:

[0159] The device collects user emotional information using its built-in emotion engine. It analyzes facial expressions and voice in real time using a camera and microphone to determine the user's emotional state. Inputs include video and audio data, which are analyzed to obtain emotional information. The output is a dataset containing emotional information.

[0160] Step 3:

[0161] The terminal transfers work-related and sentiment information to the central computer at regular intervals using a secure protocol. The input is a dataset of collected work-related and sentiment information. This is encrypted before transmission to ensure security. The output is the secure dataset that reaches the central computer.

[0162] Step 4:

[0163] The server operates on a central computer and analyzes received data using a generative AI model. Input consists of datasets of work-related information and emotional information, and the generative AI model extracts behavioral and emotional characteristics. Data processing and calculations reveal patterns specific to each user. The output is the analysis results: behavioral and emotional characteristics.

[0164] Step 5:

[0165] The server uses a generated AI model based on the analysis results to produce specific suggestions for improving work efficiency using prompt messages. The input consists of behavioral and emotional characteristics, which are used to instruct the model via prompt messages. Suggestions include things like changing the order of tasks or recommending breaks. The output is the content of the suggestions.

[0166] Step 6:

[0167] The server notifies the user of the generated suggestions. The user then uses the feedback from the server to improve their work attitude and manage their emotions. The input is the generated suggestions, and the action of notifying the user occurs. The output is the feedback received by the user.

[0168] (Application Example 2)

[0169] 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 device 14 will be referred to as the "terminal."

[0170] In today's industrial environment, improving operational efficiency and workplace safety are crucial challenges. In particular, improving operational efficiency and the work environment while considering the mental health of employees is necessary to increase industrial efficiency and ensure worker safety. However, systems that achieve both simultaneously are limited and currently difficult for many companies and facilities to utilize.

[0171] 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.

[0172] In this invention, the server includes means for collecting work data from a business information processing device, means for transferring the work data and human emotion data to a data collection device, and means for analyzing behavioral patterns and emotion patterns based on the work data and emotion data. This makes it possible to generate suggestions for improving work efficiency and safety and notify users in a timely manner.

[0173] A "business-use information processing device" is a terminal used to collect and process work data and emotional data, and is typically used in factories, commercial facilities, and other similar establishments.

[0174] A "data aggregation device" is a device that stores collected data and performs analysis as needed. It is a data management device that is connected to multiple devices via a network.

[0175] "Work data" refers to data acquired from business information processing equipment, such as work progress, operation history, and usage status.

[0176] "Emotional data" refers to data that represents human emotions, and is information acquired in real time through voice analysis and facial recognition.

[0177] "Behavioral patterns" refer to a series of behavioral histories and characteristics of employees and equipment, analyzed based on work data.

[0178] "Emotional patterns" refer to changes or tendencies in emotions over time or in different situations, as analyzed based on emotional data.

[0179] A "generative AI model" is an artificial intelligence technology used to learn from work data and emotional data, and to generate suggestions for effective business efficiency and improved safety.

[0180] A "proposal" is a set of improvement measures or innovations aimed at increasing work efficiency and safety, based on an analysis of behavioral and emotional patterns.

[0181] A "user" is an individual or organization that has the right to use this system and receive proposals.

[0182] In this embodiment of the invention, first, a business information processing device collects work data such as the progress of each task, the operation history of equipment, and usage status. This information processing device is equipped with emotion recognition technology for analyzing voice and facial expressions in real time, thereby simultaneously acquiring user emotion data.

[0183] The collected work data and emotional data are securely transferred via the network to a data aggregation device. This device functions as a central server, storing and managing the data while maintaining security by appropriately encoding the acquired data.

[0184] The central server uses a generative AI model to analyze behavioral and emotional patterns by linking work data and emotional data. This analysis generates suggestions to improve operational efficiency and safety. For example, it is possible to suppress robot movements around workers experiencing stress, thereby creating a safer environment.

[0185] The generated suggestions are communicated to users in a timely manner, providing them with concrete guidance for improving their work. The hardware of this entire system includes sensor devices, information processing devices, and communication infrastructure for optimizing signals. The software employs emotion recognition algorithms and data analysis engines, enabling advanced data management.

[0186] For example, in a factory setting where a worker is directing multiple operations, if it is determined that the worker's stress level is high, the robot's operating speed can be adjusted to ensure the worker's safety. An example of a prompt to the generative AI model used in this case would be: "Design an AI model that analyzes the emotions of human workers in the factory in real time and adjusts the robot's movements to ensure safety."

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The terminal collects work data and emotional data. Inputs here include the worker's actions, voice, and facial expressions, while outputs are digital data derived from these. The terminal uses sensors and cameras to record the user's operation history, voice tone, and facial changes.

[0190] Step 2:

[0191] The terminal transfers the collected data to the data aggregation device. The input for this step is work data and sentiment data collected by the terminal, and the output is securely encrypted data. Encryption algorithms are applied to enhance security during data transmission.

[0192] Step 3:

[0193] The server stores the received data and prepares it for analysis. The input is the transmitted encrypted data, and the output is the information converted into an analyzable format. The server uses a database to store the data and performs decryption to restore it to an analyzable state.

[0194] Step 4:

[0195] The server uses a generative AI model to analyze behavioral and emotional patterns. This analysis uses stored work and emotional data as input, and the output is specific behavioral and emotional patterns based on the analysis. The generative AI model processes the data and detects trends and anomalies.

[0196] Step 5:

[0197] The server generates suggestions for improving safety and efficiency based on the analysis results. Here, specific behavioral and emotional patterns are taken as input, and concrete work suggestions are output. The generating AI automatically creates suggestions and proposes appropriate measures as the next action step.

[0198] Step 6:

[0199] The system notifies the user of the suggestions. The server generates the suggestions as input, and the output is the result presented in a user-friendly format. Notification methods include terminals, email, and dedicated applications.

[0200] This establishes a complete workflow from data collection, transfer, analysis, and proposal development through terminals and servers, resulting in improved operational efficiency and security.

[0201] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0202] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0204] [Second Embodiment]

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

[0206] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0208] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0210] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0211] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0212] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0213] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0215] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0216] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0217] This invention utilizes a system combining employee terminals and a central server to promote business efficiency. Specifically, employee terminals collect data on employees' work activities and transfer this data to the central server. This data includes information such as application usage, keystrokes, and mouse movements.

[0218] The central server centrally manages work activity data transmitted from numerous work terminals and analyzes this data using a generating AI model. The purpose of the analysis is to reveal the daily work patterns of each employee and derive suggestions for improving individual work efficiency. The generated suggestions include specific work method improvement proposals and advice for skill development.

[0219] For example, if a user spends a lot of time processing emails in the morning, a generative AI model might suggest ways to optimize that time. In this case, the central server would suggest efficiency improvements such as using shortcuts or prioritizing email processing.

[0220] Based on monthly feedback, users can implement business improvement measures proposed by a central server. This feedback includes not only past work patterns but also guidelines for future efficiency improvements, enabling users to clarify their daily work goals and enhance their skills. Thus, the present invention provides a practical method that contributes to business efficiency.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] The device activates a work activity tracker and collects data on the employee's work activities. The collected data includes application usage time, keyboard input, and mouse movements.

[0224] Step 2:

[0225] The device periodically collects data and temporarily saves it to local storage. The saved data is later sent to a server for analysis.

[0226] Step 3:

[0227] The terminal transfers locally stored data to a central server at regular intervals. During this process, the data is encrypted using security protocols.

[0228] Step 4:

[0229] The server collects the business activity data it receives and performs preprocessing such as formatting standardization and noise removal.

[0230] Step 5:

[0231] The server generates pre-processed data, which is then input into an AI model to identify individual employee behavior patterns. The AI ​​model uses this data to analyze how to improve work efficiency.

[0232] Step 6:

[0233] Based on the analysis results, the server generates personalized work efficiency suggestions for each employee. These suggestions include prioritizing tasks and optimizing work procedures.

[0234] Step 7:

[0235] Users receive monthly feedback supplied from the server and apply individual suggestions to their work. This feedback includes past performance and new work improvement proposals.

[0236] Step 8:

[0237] The system experimentally implements user-proposed improvements and utilizes the results in the data collection process for the following month. This creates a mechanism that continuously promotes operational efficiency.

[0238] (Example 1)

[0239] 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."

[0240] In today's world, where work is becoming increasingly diverse and efficient work execution is essential, there is a problem in accurately understanding the actions of individual employees and proposing optimal work efficiency improvements on a case-by-case basis. Furthermore, there are limited systems that can efficiently collect and analyze work activity information and provide appropriate feedback to users based on that information.

[0241] 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.

[0242] In this invention, the server includes means for storing work activity information in an integrated database, means for analyzing behavioral patterns based on the work activity information, and means for providing suggestions as reports that users can implement. This enables personalized suggestions for improving work efficiency for each user, and allows for continuous improvement and streamlining of operations.

[0243] "Business equipment" refers to devices and terminals used to collect data related to business activities.

[0244] "Work activity information" refers to data related to various activities performed by employees in the course of their work, including application usage and input device operation status.

[0245] A "central information processing system" refers to a central server that manages and analyzes work activity information collected from multiple business devices.

[0246] An "integrated database" is a data storage system that systematically stores work activity information in various formats and types for use in subsequent analysis and report generation.

[0247] "Analysis of behavioral patterns" refers to the process of analyzing collected work activity information using statistical methods and machine learning models to evaluate employee behavioral tendencies and efficiency in their work.

[0248] A "generative artificial intelligence model" refers to a machine learning algorithm or neural network model used to extract useful patterns and insights from large amounts of data and generate new suggestions or predictions.

[0249] "Providing as a report" means organizing the analysis results and proposed solutions, and presenting them in a documented format that users can easily understand and implement.

[0250] As a form of implementing the invention, this system combines business equipment, a central information processing unit, and a generative AI model to support the efficiency of business operations. First, the business equipment collects work activity information generated when each employee performs their tasks. This work activity information includes detailed data such as application usage, keyboard input, and mouse operations.

[0251] Business equipment securely transfers collected information to a central information processing system. This transfer utilizes encrypted communication protocols such as HTTPS to ensure data security. The central information processing system stores work activity information in an integrated database, enabling efficient management and analysis.

[0252] The server analyzes the collected data using a generative artificial intelligence model. This identifies each employee's behavioral patterns and generates suggestions to help improve inefficient areas of work. This generative AI model is developed using machine learning libraries such as TensorFlow and PyTorch.

[0253] Based on the analysis results, the server creates a report with specific suggestions for improving work efficiency and provides it to the user. The user can then incorporate the suggested improvements into their work based on this report. For example, if a user spends a certain amount of time each day focusing on responding to emails, the server will suggest templating and prioritizing email processing to facilitate efficient time management.

[0254] An example of a prompt message would be, "Based on the data collected from business equipment, generate suggestions for optimal work efficiency improvements for each employee." By inputting this prompt into the AI ​​model, it becomes possible to propose specific work efficiency improvement measures.

[0255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0256] Step 1:

[0257] The terminal collects various work activity information in real time during work, such as application usage, keyboard input, and mouse operations. Input is user operation data from the terminal, and output is the collected work activity information. This information is temporarily recorded within the terminal and prepared for subsequent processing.

[0258] Step 2:

[0259] The terminal collects work activity information at regular intervals and transfers it to the central information processing unit using an industrial standard encryption protocol, such as HTTPS. The input is work activity information, and the output is encrypted data that arrives at the central information processing unit via a secure communication path.

[0260] Step 3:

[0261] The server stores received work activity information in an integrated database. Input is encrypted work activity information transferred from terminals, and output is data in a unified format stored in the database. This allows for efficient management of large amounts of data and preparation for subsequent analysis.

[0262] Step 4:

[0263] The server runs a generative artificial intelligence model using work activity information stored in an integrated database. This AI model analyzes behavioral patterns and identifies inefficiencies. The input is work activity information stored in the database, and the output is each user's work behavioral patterns and the insights derived from them.

[0264] Step 5:

[0265] The server generates specific business efficiency improvement suggestions based on the insights output by the AI ​​model. The input is the result of behavioral pattern analysis, and the output is a report containing individually optimized business improvement proposals. These suggestions include advice on how to improve business processes and how to make better use of time.

[0266] Step 6:

[0267] The server provides users with a report containing the generated business efficiency suggestions. The input is a report of effective business improvement proposals, and the output is monthly feedback received by the user. Users can then use this feedback to improve their operations.

[0268] (Application Example 1)

[0269] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] There is a need to improve the productivity of the entire organization by streamlining operations and proposing optimal operating patterns for machinery used in factories. However, conventional systems have not been able to fully utilize information on individual work activities and machine operation, making it difficult to generate efficient suggestions.

[0271] 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.

[0272] In this invention, the server includes means for collecting activity information from business equipment, means for transferring the activity information to a central processing unit, means for analyzing behavioral trends based on the activity information in the central processing unit, and means for analyzing the operation information of factory machinery and proposing optimizations. This enables improved efficiency in business operations and improved operational efficiency of machinery used in factories.

[0273] "Business equipment" refers to electronic devices used to collect data related to business operations.

[0274] "Activity information" refers to data on business usage and performance acquired through business equipment.

[0275] A "central processing unit" is a computer system that manages and analyzes data collected from multiple terminals.

[0276] "Behavioral tendencies" refer to patterns that show the consistency and repetitive characteristics of users' activities in the course of work.

[0277] "Factory machinery" refers to automated equipment used in the manufacturing or production process.

[0278] "Operational information" refers to data related to the operating status of factory machinery, work efficiency, energy consumption, etc.

[0279] "Methods for proposing optimization" refers to the process of deriving improvement proposals to streamline operations or machine behavior based on collected data.

[0280] This invention is a system that transfers activity information collected from business equipment to a central processing unit, performs data analysis using a generated AI model, and generates efficiency improvement suggestions. Specifically, business equipment collects data related to the work at the work site and transmits it to the central processing unit. The central processing unit manages the collected activity information using a database management system, while using a programming language such as Python as a server.

[0281] Based on the collected data, the server analyzes the behavior trends and uses the generated AI model to derive proposals for optimizing and enhancing operations. This not only improves the efficiency of business activities but also optimizes factory machinery. For example, if it is detected that a certain business device consumes excessive energy during a specific time period, the server can analyze this data and propose more efficient usage times and methods.

[0282] Users can receive the proposals monthly and based on the content, perform specific business improvements and machine operation improvements to enhance efficiency. This makes it possible to achieve individual business and overall productivity improvements. An example of a prompt sentence for the generated AI model is "Analyze the usage patterns of terminals with high energy consumption and propose new operation methods for efficiency improvement."

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

[0284] Step 1:

[0285] The terminal collects activity information through business devices. The operation data and input information when the user conducts business are obtained using sensors and logs and organized as activity information. The inputs include key inputs, mouse operations, application usage times, etc., which are provided as a data stream. The output is the collected activity information.

[0286] Step 2:

[0287] The terminal transfers the collected activity information to the central processing unit (server). The terminal securely encrypts the obtained data and transmits it via the network. The input is the organized activity information. By securely transmitting the data using an encryption protocol, the output is the activity information stored on the server.

[0288] Step 3:

[0289] The server analyzes collected activity information to identify behavioral trends. It queries the data stored on the server using a database management system and detects behavioral characteristics using a generated AI model. The input is accumulated activity information. After data analysis, it outputs characteristic data indicating behavioral trends.

[0290] Step 4:

[0291] The server generates suggestions for efficiency improvements using a generated AI model. It prompts the AI ​​model using prompt messages to request processing and generate optimization suggestions. The input is characteristic data of behavioral tendencies. Through the AI ​​model's dedicated processing, data suggesting work efficiency improvements is output.

[0292] Step 5:

[0293] The server notifies users of monthly efficiency improvement suggestions. The generated suggestions are compiled into a report and delivered to the user's device. The input is efficiency improvement suggestion data. The notification function is used to present the generated suggestions to the user.

[0294] Step 6:

[0295] Users improve their work processes based on suggestions received from the server. They review the efficiency improvements they receive and incorporate them into their daily workflows and machine operation procedures. The input is a suggestion report from the server. Ultimately, the output is improved work efficiency and increased productivity.

[0296] 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.

[0297] This invention provides a system that combines a business terminal, a central server, and an emotion engine that recognizes user emotions to achieve more advanced and adaptive business efficiency. Specifically, the business terminal measures employees' work activities, including application usage time, input history, and device operation status. In addition, the emotion engine installed in the terminal recognizes the user's emotions and collects emotion data in real time. This data is acquired using facial recognition and voice analysis technologies.

[0298] The collected work activity data and emotional data are securely transferred to a central server at regular intervals. On the server, a generating AI model analyzes the behavioral and emotional patterns of each employee based on this data. By considering both behavioral and emotional aspects, highly accurate suggestions for improving individual work efficiency become possible.

[0299] For example, if a user is experiencing negative emotions (e.g., stress or decreased concentration) while performing a specific task, the server can use this information to generate feedback suggesting a break or reconsidering the order of tasks. These suggestions take into account the alignment between the work situation and the user's emotional state, providing more realistic and effective improvement plans.

[0300] Every month, users receive personalized improvement suggestions based on analysis from the server. The feedback integrates past behaviors and emotional patterns, allowing users to improve their work efficiency and emotional management. As a result, the system contributes not only to increased work efficiency but also to improved employee well-being.

[0301] The following describes the processing flow.

[0302] Step 1:

[0303] At the start of work, the terminal activates the activity tracker and the emotion engine, and begins to collect employees' work activity data and emotion data. The work activity data includes application usage time, keyboard input, and mouse movement, and the emotion data is collected through facial expression recognition and voice analysis.

[0304] Step 2:

[0305] The terminal temporarily stores the work activity data and emotion data collected in real time in the local storage. This integrates various data and is useful for later analysis.

[0306] Step 3:

[0307] The terminal transfers the encrypted work activity data and emotion data to the central server at regular intervals. Security management in this process is strict, and anti-illegal access measures are implemented.

[0308] Step 4:

[0309] The server aggregates the received data and performs preprocessing such as format unification and outlier removal. This prepares a dataset suitable for analysis.

[0310] Step 5:

[0311] The server analyzes the preprocessed data using the generated AI model to identify the behavior patterns and emotion patterns of each employee. Here, the AI utilizes machine learning algorithms to perform highly accurate analysis.

[0312] Step 6:

[0313] The server generates individual work efficiency improvement proposals based on the analysis results. The proposals take into account the user's emotional state and include advice on adjusting work load and promoting concentration on specific tasks.

[0314] Step 7:

[0315] Users receive monthly feedback. The feedback evaluates both behavioral and emotional patterns, identifying areas for improvement and suggesting specific solutions.

[0316] Step 8:

[0317] Based on user-submitted suggestions, practical improvements are implemented, and the resulting new data is fed back into the next analysis cycle. This enhances individual adaptability and achieves sustainable operational efficiency.

[0318] (Example 2)

[0319] 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".

[0320] In conventional business management systems, suggestions to improve user work efficiency were often based solely on activity data related to the actual work. However, since users' emotions and mental state also significantly impact work efficiency, suggestions that do not consider these factors are insufficiently optimized. Therefore, there is a need for a system that can integrate and analyze work-related data and emotional information to provide individually customized suggestions.

[0321] 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.

[0322] In this invention, the server includes means for collecting business-related information from business equipment, means for analyzing behavioral characteristics based on business-related information and user emotional information, and means for generating work efficiency improvement suggestions based on the analysis results of behavioral and emotional characteristics. This makes it possible to generate accurate and personalized work improvement suggestions that take into account each user's individual work activities and emotional state.

[0323] "Business equipment" refers to devices and equipment used to collect data on users' daily work activities.

[0324] "Business-related information" refers to a collection of data collected through business equipment, such as user application usage time, input history, and device operation status.

[0325] A "central computer" refers to a central device or system that receives and stores business-related information and user sentiment information, and processes and analyzes that data.

[0326] "Emotional information" refers to data that represents the user's emotional state and is collected using facial recognition technology and voice analysis technology.

[0327] "Behavioral characteristics" refer to patterns and tendencies in a user's work activities, analyzed based on work-related information.

[0328] "Emotional characteristics" are data that shows the tendencies and patterns of a user's emotions, obtained through the analysis of emotional information.

[0329] A "generative AI model" is an artificial intelligence model used in data analysis and proposal generation. It has the ability to learn from large datasets and generate various outputs.

[0330] A "prompt statement" is an input statement used to give instructions to a generative AI model, and it plays a role in directing the content of the output that will be generated.

[0331] This invention aims to realize a system that improves operational efficiency and manages user emotions. The system has multiple functions and mainly includes operational equipment, a central computer, and emotion recognition technology.

[0332] Business equipment is a device that monitors users' daily work activities. This includes computers, smart devices, and dedicated business support terminals. Business equipment continuously records and collects business-related information such as application usage time, input history, and device operation status.

[0333] Furthermore, the professional equipment is equipped with an emotion engine that includes facial recognition and voice analysis technologies to collect user emotional information. This allows for the analysis of emotions from the user's facial expressions and voice, and the emotional information is converted into data in real time. Specifically, high-resolution cameras and high-sensitivity microphones are used as hardware, and voice recognition algorithms are operated as software.

[0334] The collected data is transferred to a central computer via a secure protocol at regular intervals. The central computer uses a generative AI model to process and analyze work-related information and emotional information in an integrated manner, thereby deriving behavioral and emotional characteristics for each user.

[0335] The server utilizes a generative AI model to provide specific suggestions based on analysis results, supporting the user's work efficiency. These suggestions include reordering tasks and recommending breaks. For example, if the server detects a decline in the user's concentration due to prolonged, continuous data entry, it will recommend taking a short break. These suggestions are generated by providing instructions to the generative AI model using prompt statements.

[0336] As a concrete example, consider the prompt: "How can I analyze user business data and real-time sentiment data to generate useful feedback for improving work efficiency?"

[0337] Based on the feedback provided, users can improve their work attitudes and manage their emotions. This leads to increased work efficiency and improved user well-being.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The terminal, as a business device, collects user work-related information. Specifically, it records application usage time, input history, and device operation status. Inputs include log data and operation history from various devices, which are saved to the terminal's storage in real time. Output is a dataset of work-related information.

[0341] Step 2:

[0342] The device collects user emotional information using its built-in emotion engine. It analyzes facial expressions and voice in real time using a camera and microphone to determine the user's emotional state. Inputs include video and audio data, which are analyzed to obtain emotional information. The output is a dataset containing emotional information.

[0343] Step 3:

[0344] The terminal transfers work-related and sentiment information to the central computer at regular intervals using a secure protocol. The input is a dataset of collected work-related and sentiment information. This is encrypted before transmission to ensure security. The output is the secure dataset that reaches the central computer.

[0345] Step 4:

[0346] The server operates on a central computer and analyzes received data using a generative AI model. Input consists of datasets of work-related information and emotional information, and the generative AI model extracts behavioral and emotional characteristics. Data processing and calculations reveal patterns specific to each user. The output is the analysis results: behavioral and emotional characteristics.

[0347] Step 5:

[0348] The server uses a generated AI model based on the analysis results to produce specific suggestions for improving work efficiency using prompt messages. The input consists of behavioral and emotional characteristics, which are used to instruct the model via prompt messages. Suggestions include things like changing the order of tasks or recommending breaks. The output is the content of the suggestions.

[0349] Step 6:

[0350] The server notifies the user of the generated suggestions. The user then uses the feedback from the server to improve their work attitude and manage their emotions. The input is the generated suggestions, and the action of notifying the user occurs. The output is the feedback received by the user.

[0351] (Application Example 2)

[0352] 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 as the "terminal".

[0353] In today's industrial environment, improving operational efficiency and workplace safety are crucial challenges. In particular, improving operational efficiency and the work environment while considering the mental health of employees is necessary to increase industrial efficiency and ensure worker safety. However, systems that achieve both simultaneously are limited and currently difficult for many companies and facilities to utilize.

[0354] 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.

[0355] In this invention, the server includes means for collecting work data from a business information processing device, means for transferring the work data and human emotion data to a data collection device, and means for analyzing behavioral patterns and emotion patterns based on the work data and emotion data. This makes it possible to generate suggestions for improving work efficiency and safety and notify users in a timely manner.

[0356] A "business-use information processing device" is a terminal used to collect and process work data and emotional data, and is typically used in factories, commercial facilities, and other similar establishments.

[0357] A "data aggregation device" is a device that stores collected data and performs analysis as needed. It is a data management device that is connected to multiple devices via a network.

[0358] "Work data" refers to data acquired from business information processing equipment, such as work progress, operation history, and usage status.

[0359] "Emotional data" refers to data that represents human emotions, and is information acquired in real time through voice analysis and facial recognition.

[0360] "Behavioral patterns" refer to a series of behavioral histories and characteristics of employees and equipment, analyzed based on work data.

[0361] "Emotional patterns" refer to changes or tendencies in emotions over time or in different situations, as analyzed based on emotional data.

[0362] A "generative AI model" is an artificial intelligence technology used to learn from work data and emotional data, and to generate suggestions for effective business efficiency and improved safety.

[0363] A "proposal" is a set of improvement measures or innovations aimed at increasing work efficiency and safety, based on an analysis of behavioral and emotional patterns.

[0364] A "user" is an individual or organization that has the right to use this system and receive proposals.

[0365] In this embodiment of the invention, first, a business information processing device collects work data such as the progress of each task, the operation history of equipment, and usage status. This information processing device is equipped with emotion recognition technology for analyzing voice and facial expressions in real time, thereby simultaneously acquiring user emotion data.

[0366] The collected work data and emotional data are securely transferred via the network to a data aggregation device. This device functions as a central server, storing and managing the data while maintaining security by appropriately encoding the acquired data.

[0367] The central server uses a generative AI model to analyze behavioral and emotional patterns by linking work data and emotional data. This analysis generates suggestions to improve operational efficiency and safety. For example, it is possible to suppress robot movements around workers experiencing stress, thereby creating a safer environment.

[0368] The generated suggestions are communicated to users in a timely manner, providing them with concrete guidance for improving their work. The hardware of this entire system includes sensor devices, information processing devices, and communication infrastructure for optimizing signals. The software employs emotion recognition algorithms and data analysis engines, enabling advanced data management.

[0369] For example, in a factory setting where a worker is directing multiple operations, if it is determined that the worker's stress level is high, the robot's operating speed can be adjusted to ensure the worker's safety. An example of a prompt to the generative AI model used in this case would be: "Design an AI model that analyzes the emotions of human workers in the factory in real time and adjusts the robot's movements to ensure safety."

[0370] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0371] Step 1:

[0372] The terminal collects work data and emotional data. Inputs here include the worker's actions, voice, and facial expressions, while outputs are digital data derived from these. The terminal uses sensors and cameras to record the user's operation history, voice tone, and facial changes.

[0373] Step 2:

[0374] The terminal transfers the collected data to the data aggregation device. The input for this step is work data and sentiment data collected by the terminal, and the output is securely encrypted data. Encryption algorithms are applied to enhance security during data transmission.

[0375] Step 3:

[0376] The server stores the received data and prepares it for analysis. The input is the transmitted encrypted data, and the output is the information converted into an analyzable format. The server uses a database to store the data and performs decryption to restore it to an analyzable state.

[0377] Step 4:

[0378] The server uses a generative AI model to analyze behavioral and emotional patterns. This analysis uses stored work and emotional data as input, and the output is specific behavioral and emotional patterns based on the analysis. The generative AI model processes the data and detects trends and anomalies.

[0379] Step 5:

[0380] The server generates suggestions for improving safety and efficiency based on the analysis results. Here, specific behavioral and emotional patterns are taken as input, and concrete work suggestions are output. The generating AI automatically creates suggestions and proposes appropriate measures as the next action step.

[0381] Step 6:

[0382] The system notifies the user of the suggestions. The server generates the suggestions as input, and the output is the result presented in a user-friendly format. Notification methods include terminals, email, and dedicated applications.

[0383] This establishes a complete workflow from data collection, transfer, analysis, and proposal development through terminals and servers, resulting in improved operational efficiency and security.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] 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.

[0390] 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).

[0391] 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.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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".

[0400] This invention utilizes a system combining employee terminals and a central server to promote business efficiency. Specifically, employee terminals collect data on employees' work activities and transfer this data to the central server. This data includes information such as application usage, keystrokes, and mouse movements.

[0401] The central server centrally manages work activity data transmitted from numerous work terminals and analyzes this data using a generating AI model. The purpose of the analysis is to reveal the daily work patterns of each employee and derive suggestions for improving individual work efficiency. The generated suggestions include specific work method improvement proposals and advice for skill development.

[0402] For example, if a user spends a lot of time processing emails in the morning, a generative AI model might suggest ways to optimize that time. In this case, the central server would suggest efficiency improvements such as using shortcuts or prioritizing email processing.

[0403] Based on monthly feedback, users can implement business improvement measures proposed by a central server. This feedback includes not only past work patterns but also guidelines for future efficiency improvements, enabling users to clarify their daily work goals and enhance their skills. Thus, the present invention provides a practical method that contributes to business efficiency.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] The device activates a work activity tracker and collects data on the employee's work activities. The collected data includes application usage time, keyboard input, and mouse movements.

[0407] Step 2:

[0408] The device periodically collects data and temporarily saves it to local storage. The saved data is later sent to a server for analysis.

[0409] Step 3:

[0410] The terminal transfers locally stored data to a central server at regular intervals. During this process, the data is encrypted using security protocols.

[0411] Step 4:

[0412] The server collects the business activity data it receives and performs preprocessing such as formatting standardization and noise removal.

[0413] Step 5:

[0414] The server generates pre-processed data, which is then input into an AI model to identify individual employee behavior patterns. The AI ​​model uses this data to analyze how to improve work efficiency.

[0415] Step 6:

[0416] Based on the analysis results, the server generates personalized work efficiency suggestions for each employee. These suggestions include prioritizing tasks and optimizing work procedures.

[0417] Step 7:

[0418] Users receive monthly feedback supplied from the server and apply individual suggestions to their work. This feedback includes past performance and new work improvement proposals.

[0419] Step 8:

[0420] The system experimentally implements user-proposed improvements and utilizes the results in the data collection process for the following month. This creates a mechanism that continuously promotes operational efficiency.

[0421] (Example 1)

[0422] 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."

[0423] In today's world, where work is becoming increasingly diverse and efficient work execution is essential, there is a problem in accurately understanding the actions of individual employees and proposing optimal work efficiency improvements on a case-by-case basis. Furthermore, there are limited systems that can efficiently collect and analyze work activity information and provide appropriate feedback to users based on that information.

[0424] 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.

[0425] In this invention, the server includes means for storing work activity information in an integrated database, means for analyzing behavioral patterns based on the work activity information, and means for providing suggestions as reports that users can implement. This enables personalized suggestions for improving work efficiency for each user, and allows for continuous improvement and streamlining of operations.

[0426] "Business equipment" refers to devices and terminals used to collect data related to business activities.

[0427] "Work activity information" refers to data related to various activities performed by employees in the course of their work, including application usage and input device operation status.

[0428] A "central information processing system" refers to a central server that manages and analyzes work activity information collected from multiple business devices.

[0429] An "integrated database" is a data storage system that systematically stores work activity information in various formats and types for use in subsequent analysis and report generation.

[0430] "Analysis of behavioral patterns" refers to the process of analyzing collected work activity information using statistical methods and machine learning models to evaluate employee behavioral tendencies and efficiency in their work.

[0431] A "generative artificial intelligence model" refers to a machine learning algorithm or neural network model used to extract useful patterns and insights from large amounts of data and generate new suggestions or predictions.

[0432] "Providing as a report" means organizing the analysis results and proposed solutions, and presenting them in a documented format that users can easily understand and implement.

[0433] As a form of implementing the invention, this system combines business equipment, a central information processing unit, and a generative AI model to support the efficiency of business operations. First, the business equipment collects work activity information generated when each employee performs their tasks. This work activity information includes detailed data such as application usage, keyboard input, and mouse operations.

[0434] Business equipment securely transfers collected information to a central information processing system. This transfer utilizes encrypted communication protocols such as HTTPS to ensure data security. The central information processing system stores work activity information in an integrated database, enabling efficient management and analysis.

[0435] The server analyzes the collected data using a generative artificial intelligence model. This identifies each employee's behavioral patterns and generates suggestions to help improve inefficient areas of work. This generative AI model is developed using machine learning libraries such as TensorFlow and PyTorch.

[0436] Based on the analysis results, the server creates a report with specific suggestions for improving work efficiency and provides it to the user. The user can then incorporate the suggested improvements into their work based on this report. For example, if a user spends a certain amount of time each day focusing on responding to emails, the server will suggest templating and prioritizing email processing to facilitate efficient time management.

[0437] An example of a prompt message would be, "Based on the data collected from business equipment, generate suggestions for optimal work efficiency improvements for each employee." By inputting this prompt into the AI ​​model, it becomes possible to propose specific work efficiency improvement measures.

[0438] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0439] Step 1:

[0440] The terminal collects various work activity information in real time during work, such as application usage, keyboard input, and mouse operations. Input is user operation data from the terminal, and output is the collected work activity information. This information is temporarily recorded within the terminal and prepared for subsequent processing.

[0441] Step 2:

[0442] The terminal collects work activity information at regular intervals and transfers it to the central information processing unit using an industrial standard encryption protocol, such as HTTPS. The input is work activity information, and the output is encrypted data that arrives at the central information processing unit via a secure communication path.

[0443] Step 3:

[0444] The server stores received work activity information in an integrated database. Input is encrypted work activity information transferred from terminals, and output is data in a unified format stored in the database. This allows for efficient management of large amounts of data and preparation for subsequent analysis.

[0445] Step 4:

[0446] The server runs a generative artificial intelligence model using work activity information stored in an integrated database. This AI model analyzes behavioral patterns and identifies inefficiencies. The input is work activity information stored in the database, and the output is each user's work behavioral patterns and the insights derived from them.

[0447] Step 5:

[0448] The server generates specific business efficiency improvement suggestions based on the insights output by the AI ​​model. The input is the result of behavioral pattern analysis, and the output is a report containing individually optimized business improvement proposals. These suggestions include advice on how to improve business processes and how to make better use of time.

[0449] Step 6:

[0450] The server provides users with a report containing the generated business efficiency suggestions. The input is a report of effective business improvement proposals, and the output is monthly feedback received by the user. Users can then use this feedback to improve their operations.

[0451] (Application Example 1)

[0452] 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."

[0453] There is a need to improve the productivity of the entire organization by streamlining operations and proposing optimal operating patterns for machinery used in factories. However, conventional systems have not been able to fully utilize information on individual work activities and machine operation, making it difficult to generate efficient suggestions.

[0454] 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.

[0455] In this invention, the server includes means for collecting activity information from business equipment, means for transferring the activity information to a central processing unit, means for analyzing behavioral trends based on the activity information in the central processing unit, and means for analyzing the operation information of factory machinery and proposing optimizations. This enables improved efficiency in business operations and improved operational efficiency of machinery used in factories.

[0456] "Business equipment" refers to electronic devices used to collect data related to business operations.

[0457] "Activity information" refers to data on business usage and performance acquired through business equipment.

[0458] A "central processing unit" is a computer system that manages and analyzes data collected from multiple terminals.

[0459] "Behavioral tendencies" refer to patterns that show the consistency and repetitive characteristics of users' activities in the course of work.

[0460] "Factory machinery" refers to automated equipment used in the manufacturing or production process.

[0461] "Operational information" refers to data related to the operating status of factory machinery, work efficiency, energy consumption, etc.

[0462] "Methods for proposing optimization" refers to the process of deriving improvement proposals to streamline operations or machine behavior based on collected data.

[0463] This invention is a system that transfers activity information collected from business equipment to a central processing unit, performs data analysis using a generated AI model, and generates efficiency improvement suggestions. Specifically, business equipment collects data related to the work at the work site and transmits it to the central processing unit. The central processing unit manages the collected activity information using a database management system, while using a programming language such as Python as a server.

[0464] The server analyzes behavioral trends based on collected data and utilizes generative AI models to derive suggestions for optimizing and improving the efficiency of operations. This not only improves the efficiency of business activities but also optimizes factory machinery. For example, if the server detects that a certain piece of equipment is consuming excessive energy during a specific time period, it can analyze that data and suggest more efficient usage times and methods.

[0465] Users receive suggestions monthly and can use them to implement specific business improvements and machine operation optimizations to increase efficiency. This enables improvements in both individual tasks and overall productivity. An example of a prompt for the generated AI model is, "Analyze the usage patterns of energy-intensive devices and suggest new operating methods to improve efficiency."

[0466] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0467] Step 1:

[0468] The terminal collects activity information through business equipment. It acquires operation data and input information from users performing their tasks using sensors and logs, and organizes this information as activity data. Inputs include keyboard input, mouse operations, and application usage time, which are provided as data streams. The output is the collected activity information.

[0469] Step 2:

[0470] The terminal transfers the collected activity information to the central processing unit (server). The terminal securely encrypts the obtained data and transmits it over the network. The input is organized activity information. By securely transmitting the data using an encryption protocol, the output is the activity information stored on the server.

[0471] Step 3:

[0472] The server analyzes collected activity information to identify behavioral trends. It queries the data stored on the server using a database management system and detects behavioral characteristics using a generated AI model. The input is accumulated activity information. After data analysis, it outputs characteristic data indicating behavioral trends.

[0473] Step 4:

[0474] The server generates suggestions for efficiency improvements using a generated AI model. It prompts the AI ​​model using prompt messages to request processing and generate optimization suggestions. The input is characteristic data of behavioral tendencies. Through the AI ​​model's dedicated processing, data suggesting work efficiency improvements is output.

[0475] Step 5:

[0476] The server notifies users of monthly efficiency improvement suggestions. The generated suggestions are compiled into a report and delivered to the user's device. The input is efficiency improvement suggestion data. The notification function is used to present the generated suggestions to the user.

[0477] Step 6:

[0478] Users improve their work processes based on suggestions received from the server. They review the efficiency improvements they receive and incorporate them into their daily workflows and machine operation procedures. The input is a suggestion report from the server. Ultimately, the output is improved work efficiency and increased productivity.

[0479] 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.

[0480] This invention provides a system that combines a business terminal, a central server, and an emotion engine that recognizes user emotions to achieve more advanced and adaptive business efficiency. Specifically, the business terminal measures employees' work activities, including application usage time, input history, and device operation status. In addition, the emotion engine installed in the terminal recognizes the user's emotions and collects emotion data in real time. This data is acquired using facial recognition and voice analysis technologies.

[0481] The collected work activity data and emotional data are securely transferred to a central server at regular intervals. On the server, a generating AI model analyzes the behavioral and emotional patterns of each employee based on this data. By considering both behavioral and emotional aspects, highly accurate suggestions for improving individual work efficiency become possible.

[0482] For example, if a user is experiencing negative emotions (e.g., stress or decreased concentration) while performing a specific task, the server can use this information to generate feedback suggesting a break or reconsidering the order of tasks. These suggestions take into account the alignment between the work situation and the user's emotional state, providing more realistic and effective improvement plans.

[0483] Every month, users receive personalized improvement suggestions based on analysis from the server. The feedback integrates past behaviors and emotional patterns, allowing users to improve their work efficiency and emotional management. As a result, the system contributes not only to increased work efficiency but also to improved employee well-being.

[0484] The following describes the processing flow.

[0485] Step 1:

[0486] When the workday begins, the terminal activates the activity tracker and emotion engine, starting to collect employee work activity data and emotion data. Work activity data includes application usage time, keyboard input, and mouse movements, while emotion data is collected through facial recognition and voice analysis.

[0487] Step 2:

[0488] The terminal temporarily stores the business activity data and sentiment data collected in real time in local storage. This allows for the integration of diverse data, which can then be used for later analysis.

[0489] Step 3:

[0490] The terminal periodically transfers encrypted business activity data and emotional data to a central server. Strict security management is in place during this process, and measures are in place to prevent unauthorized access.

[0491] Step 4:

[0492] The server aggregates the received data and performs preprocessing such as formatting standardization and removing outliers. This prepares a dataset suitable for analysis.

[0493] Step 5:

[0494] The server analyzes pre-processed data using a generated AI model to identify each employee's behavioral and emotional patterns. Here, the AI ​​utilizes machine learning algorithms to perform highly accurate analysis.

[0495] Step 6:

[0496] The server generates personalized work efficiency suggestions based on the analysis results. These suggestions take into account the user's emotional state and include advice to adjust workloads and encourage focus on specific tasks.

[0497] Step 7:

[0498] Users receive monthly feedback. The feedback evaluates both behavioral and emotional patterns, identifying areas for improvement and suggesting specific solutions.

[0499] Step 8:

[0500] Based on user-submitted suggestions, practical improvements are implemented, and the resulting new data is fed back into the next analysis cycle. This enhances individual adaptability and achieves sustainable operational efficiency.

[0501] (Example 2)

[0502] 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."

[0503] In conventional business management systems, suggestions to improve user work efficiency were often based solely on activity data related to the actual work. However, since users' emotions and mental state also significantly impact work efficiency, suggestions that do not consider these factors are insufficiently optimized. Therefore, there is a need for a system that can integrate and analyze work-related data and emotional information to provide individually customized suggestions.

[0504] 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.

[0505] In this invention, the server includes means for collecting business-related information from business equipment, means for analyzing behavioral characteristics based on business-related information and user emotional information, and means for generating work efficiency improvement suggestions based on the analysis results of behavioral and emotional characteristics. This makes it possible to generate accurate and personalized work improvement suggestions that take into account each user's individual work activities and emotional state.

[0506] "Business equipment" refers to devices and equipment used to collect data on users' daily work activities.

[0507] "Business-related information" refers to a collection of data collected through business equipment, such as user application usage time, input history, and device operation status.

[0508] A "central computer" refers to a central device or system that receives and stores business-related information and user sentiment information, and processes and analyzes that data.

[0509] "Emotional information" refers to data that represents the user's emotional state and is collected using facial recognition technology and voice analysis technology.

[0510] "Behavioral characteristics" refer to patterns and tendencies in a user's work activities, analyzed based on work-related information.

[0511] "Emotional characteristics" are data that shows the tendencies and patterns of a user's emotions, obtained through the analysis of emotional information.

[0512] A "generative AI model" is an artificial intelligence model used in data analysis and proposal generation. It has the ability to learn from large datasets and generate various outputs.

[0513] A "prompt statement" is an input statement used to give instructions to a generative AI model, and it plays a role in directing the content of the output that will be generated.

[0514] This invention aims to realize a system that improves operational efficiency and manages user emotions. The system has multiple functions and mainly includes operational equipment, a central computer, and emotion recognition technology.

[0515] Business equipment is a device that monitors users' daily work activities. This includes computers, smart devices, and dedicated business support terminals. Business equipment continuously records and collects business-related information such as application usage time, input history, and device operation status.

[0516] Furthermore, the professional equipment is equipped with an emotion engine that includes facial recognition and voice analysis technologies to collect user emotional information. This allows for the analysis of emotions from the user's facial expressions and voice, and the emotional information is converted into data in real time. Specifically, high-resolution cameras and high-sensitivity microphones are used as hardware, and voice recognition algorithms are operated as software.

[0517] The collected data is transferred to a central computer via a secure protocol at regular intervals. The central computer uses a generative AI model to process and analyze work-related information and emotional information in an integrated manner, thereby deriving behavioral and emotional characteristics for each user.

[0518] The server utilizes a generative AI model to provide specific suggestions based on analysis results, supporting the user's work efficiency. These suggestions include reordering tasks and recommending breaks. For example, if the server detects a decline in the user's concentration due to prolonged, continuous data entry, it will recommend taking a short break. These suggestions are generated by providing instructions to the generative AI model using prompt statements.

[0519] As a concrete example, consider the prompt: "How can I analyze user business data and real-time sentiment data to generate useful feedback for improving work efficiency?"

[0520] Based on the feedback provided, users can improve their work attitudes and manage their emotions. This leads to increased work efficiency and improved user well-being.

[0521] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0522] Step 1:

[0523] The terminal, as a business device, collects user work-related information. Specifically, it records application usage time, input history, and device operation status. Inputs include log data and operation history from various devices, which are saved to the terminal's storage in real time. Output is a dataset of work-related information.

[0524] Step 2:

[0525] The device collects user emotional information using its built-in emotion engine. It analyzes facial expressions and voice in real time using a camera and microphone to determine the user's emotional state. Inputs include video and audio data, which are analyzed to obtain emotional information. The output is a dataset containing emotional information.

[0526] Step 3:

[0527] The terminal transfers work-related and sentiment information to the central computer at regular intervals using a secure protocol. The input is a dataset of collected work-related and sentiment information. This is encrypted before transmission to ensure security. The output is the secure dataset that reaches the central computer.

[0528] Step 4:

[0529] The server operates on a central computer and analyzes received data using a generative AI model. Input consists of datasets of work-related information and emotional information, and the generative AI model extracts behavioral and emotional characteristics. Data processing and calculations reveal patterns specific to each user. The output is the analysis results: behavioral and emotional characteristics.

[0530] Step 5:

[0531] The server uses a generated AI model based on the analysis results to produce specific suggestions for improving work efficiency using prompt messages. The input consists of behavioral and emotional characteristics, which are used to instruct the model via prompt messages. Suggestions include things like changing the order of tasks or recommending breaks. The output is the content of the suggestions.

[0532] Step 6:

[0533] The server notifies the user of the generated suggestions. The user then uses the feedback from the server to improve their work attitude and manage their emotions. The input is the generated suggestions, and the action of notifying the user occurs. The output is the feedback received by the user.

[0534] (Application Example 2)

[0535] 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."

[0536] In today's industrial environment, improving operational efficiency and workplace safety are crucial challenges. In particular, improving operational efficiency and the work environment while considering the mental health of employees is necessary to increase industrial efficiency and ensure worker safety. However, systems that achieve both simultaneously are limited and currently difficult for many companies and facilities to utilize.

[0537] 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.

[0538] In this invention, the server includes means for collecting work data from a business information processing device, means for transferring the work data and human emotion data to a data collection device, and means for analyzing behavioral patterns and emotion patterns based on the work data and emotion data. This makes it possible to generate suggestions for improving work efficiency and safety and notify users in a timely manner.

[0539] A "business-use information processing device" is a terminal used to collect and process work data and emotional data, and is typically used in factories, commercial facilities, and other similar establishments.

[0540] A "data aggregation device" is a device that stores collected data and performs analysis as needed. It is a data management device that is connected to multiple devices via a network.

[0541] "Work data" refers to data acquired from business information processing equipment, such as work progress, operation history, and usage status.

[0542] "Emotional data" refers to data that represents human emotions, and is information acquired in real time through voice analysis and facial recognition.

[0543] "Behavioral patterns" refer to a series of behavioral histories and characteristics of employees and equipment, analyzed based on work data.

[0544] "Emotional patterns" refer to changes or tendencies in emotions over time or in different situations, as analyzed based on emotional data.

[0545] A "generative AI model" is an artificial intelligence technology used to learn from work data and emotional data, and to generate suggestions for effective business efficiency and improved safety.

[0546] A "proposal" is a set of improvement measures or innovations aimed at increasing work efficiency and safety, based on an analysis of behavioral and emotional patterns.

[0547] A "user" is an individual or organization that has the right to use this system and receive proposals.

[0548] In this embodiment of the invention, first, a business information processing device collects work data such as the progress of each task, the operation history of equipment, and usage status. This information processing device is equipped with emotion recognition technology for analyzing voice and facial expressions in real time, thereby simultaneously acquiring user emotion data.

[0549] The collected work data and emotional data are securely transferred via the network to a data aggregation device. This device functions as a central server, storing and managing the data while maintaining security by appropriately encoding the acquired data.

[0550] The central server uses a generative AI model to analyze behavioral and emotional patterns by linking work data and emotional data. This analysis generates suggestions to improve operational efficiency and safety. For example, it is possible to suppress robot movements around workers experiencing stress, thereby creating a safer environment.

[0551] The generated suggestions are communicated to users in a timely manner, providing them with concrete guidance for improving their work. The hardware of this entire system includes sensor devices, information processing devices, and communication infrastructure for optimizing signals. The software employs emotion recognition algorithms and data analysis engines, enabling advanced data management.

[0552] For example, in a factory setting where a worker is directing multiple operations, if it is determined that the worker's stress level is high, the robot's operating speed can be adjusted to ensure the worker's safety. An example of a prompt to the generative AI model used in this case would be: "Design an AI model that analyzes the emotions of human workers in the factory in real time and adjusts the robot's movements to ensure safety."

[0553] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0554] Step 1:

[0555] The terminal collects work data and emotional data. Inputs here include the worker's actions, voice, and facial expressions, while outputs are digital data derived from these. The terminal uses sensors and cameras to record the user's operation history, voice tone, and facial changes.

[0556] Step 2:

[0557] The terminal transfers the collected data to the data aggregation device. The input for this step is work data and sentiment data collected by the terminal, and the output is securely encrypted data. Encryption algorithms are applied to enhance security during data transmission.

[0558] Step 3:

[0559] The server stores the received data and prepares it for analysis. The input is the transmitted encrypted data, and the output is the information converted into an analyzable format. The server uses a database to store the data and performs decryption to restore it to an analyzable state.

[0560] Step 4:

[0561] The server uses a generative AI model to analyze behavioral and emotional patterns. This analysis uses stored work and emotional data as input, and the output is specific behavioral and emotional patterns based on the analysis. The generative AI model processes the data and detects trends and anomalies.

[0562] Step 5:

[0563] The server generates suggestions for improving safety and efficiency based on the analysis results. Here, specific behavioral and emotional patterns are taken as input, and concrete work suggestions are output. The generating AI automatically creates suggestions and proposes appropriate measures as the next action step.

[0564] Step 6:

[0565] The system notifies the user of the suggestions. The server generates the suggestions as input, and the output is the result presented in a user-friendly format. Notification methods include terminals, email, and dedicated applications.

[0566] This establishes a complete workflow from data collection, transfer, analysis, and proposal development through terminals and servers, resulting in improved operational efficiency and security.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] [Fourth Embodiment]

[0571] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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".

[0584] This invention utilizes a system combining employee terminals and a central server to promote business efficiency. Specifically, employee terminals collect data on employees' work activities and transfer this data to the central server. This data includes information such as application usage, keystrokes, and mouse movements.

[0585] The central server centrally manages work activity data transmitted from numerous work terminals and analyzes this data using a generating AI model. The purpose of the analysis is to reveal the daily work patterns of each employee and derive suggestions for improving individual work efficiency. The generated suggestions include specific work method improvement proposals and advice for skill development.

[0586] For example, if a user spends a lot of time processing emails in the morning, a generative AI model might suggest ways to optimize that time. In this case, the central server would suggest efficiency improvements such as using shortcuts or prioritizing email processing.

[0587] Based on monthly feedback, users can implement business improvement measures proposed by a central server. This feedback includes not only past work patterns but also guidelines for future efficiency improvements, enabling users to clarify their daily work goals and enhance their skills. Thus, the present invention provides a practical method that contributes to business efficiency.

[0588] The following describes the processing flow.

[0589] Step 1:

[0590] The device activates a work activity tracker and collects data on the employee's work activities. The collected data includes application usage time, keyboard input, and mouse movements.

[0591] Step 2:

[0592] The device periodically collects data and temporarily saves it to local storage. The saved data is later sent to a server for analysis.

[0593] Step 3:

[0594] The terminal transfers locally stored data to a central server at regular intervals. During this process, the data is encrypted using security protocols.

[0595] Step 4:

[0596] The server collects the business activity data it receives and performs preprocessing such as formatting standardization and noise removal.

[0597] Step 5:

[0598] The server generates pre-processed data, which is then input into an AI model to identify individual employee behavior patterns. The AI ​​model uses this data to analyze how to improve work efficiency.

[0599] Step 6:

[0600] Based on the analysis results, the server generates personalized work efficiency suggestions for each employee. These suggestions include prioritizing tasks and optimizing work procedures.

[0601] Step 7:

[0602] Users receive monthly feedback supplied from the server and apply individual suggestions to their work. This feedback includes past performance and new work improvement proposals.

[0603] Step 8:

[0604] The system experimentally implements user-proposed improvements and utilizes the results in the data collection process for the following month. This creates a mechanism that continuously promotes operational efficiency.

[0605] (Example 1)

[0606] 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".

[0607] In today's world, where work is becoming increasingly diverse and efficient work execution is essential, there is a problem in accurately understanding the actions of individual employees and proposing optimal work efficiency improvements on a case-by-case basis. Furthermore, there are limited systems that can efficiently collect and analyze work activity information and provide appropriate feedback to users based on that information.

[0608] 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.

[0609] In this invention, the server includes means for storing work activity information in an integrated database, means for analyzing behavioral patterns based on the work activity information, and means for providing suggestions as reports that users can implement. This enables personalized suggestions for improving work efficiency for each user, and allows for continuous improvement and streamlining of operations.

[0610] "Business equipment" refers to devices and terminals used to collect data related to business activities.

[0611] "Work activity information" refers to data related to various activities performed by employees in the course of their work, including application usage and input device operation status.

[0612] A "central information processing system" refers to a central server that manages and analyzes work activity information collected from multiple business devices.

[0613] An "integrated database" is a data storage system that systematically stores work activity information in various formats and types for use in subsequent analysis and report generation.

[0614] "Analysis of behavioral patterns" refers to the process of analyzing collected work activity information using statistical methods and machine learning models to evaluate employee behavioral tendencies and efficiency in their work.

[0615] A "generative artificial intelligence model" refers to a machine learning algorithm or neural network model used to extract useful patterns and insights from large amounts of data and generate new suggestions or predictions.

[0616] "Providing as a report" means organizing the analysis results and proposed solutions, and presenting them in a documented format that users can easily understand and implement.

[0617] As a form of implementing the invention, this system combines business equipment, a central information processing unit, and a generative AI model to support the efficiency of business operations. First, the business equipment collects work activity information generated when each employee performs their tasks. This work activity information includes detailed data such as application usage, keyboard input, and mouse operations.

[0618] Business equipment securely transfers collected information to a central information processing system. This transfer utilizes encrypted communication protocols such as HTTPS to ensure data security. The central information processing system stores work activity information in an integrated database, enabling efficient management and analysis.

[0619] The server analyzes the collected data using a generative artificial intelligence model. This identifies each employee's behavioral patterns and generates suggestions to help improve inefficient areas of work. This generative AI model is developed using machine learning libraries such as TensorFlow and PyTorch.

[0620] Based on the analysis results, the server creates a report with specific suggestions for improving work efficiency and provides it to the user. The user can then incorporate the suggested improvements into their work based on this report. For example, if a user spends a certain amount of time each day focusing on responding to emails, the server will suggest templating and prioritizing email processing to facilitate efficient time management.

[0621] An example of a prompt message would be, "Based on the data collected from business equipment, generate suggestions for optimal work efficiency improvements for each employee." By inputting this prompt into the AI ​​model, it becomes possible to propose specific work efficiency improvement measures.

[0622] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0623] Step 1:

[0624] The terminal collects various work activity information in real time during work, such as application usage, keyboard input, and mouse operations. Input is user operation data from the terminal, and output is the collected work activity information. This information is temporarily recorded within the terminal and prepared for subsequent processing.

[0625] Step 2:

[0626] The terminal collects work activity information at regular intervals and transfers it to the central information processing unit using an industrial standard encryption protocol, such as HTTPS. The input is work activity information, and the output is encrypted data that arrives at the central information processing unit via a secure communication path.

[0627] Step 3:

[0628] The server stores received work activity information in an integrated database. Input is encrypted work activity information transferred from terminals, and output is data in a unified format stored in the database. This allows for efficient management of large amounts of data and preparation for subsequent analysis.

[0629] Step 4:

[0630] The server runs a generative artificial intelligence model using work activity information stored in an integrated database. This AI model analyzes behavioral patterns and identifies inefficiencies. The input is work activity information stored in the database, and the output is each user's work behavioral patterns and the insights derived from them.

[0631] Step 5:

[0632] The server generates specific business efficiency improvement suggestions based on the insights output by the AI ​​model. The input is the result of behavioral pattern analysis, and the output is a report containing individually optimized business improvement proposals. These suggestions include advice on how to improve business processes and how to make better use of time.

[0633] Step 6:

[0634] The server provides users with a report containing the generated business efficiency suggestions. The input is a report of effective business improvement proposals, and the output is monthly feedback received by the user. Users can then use this feedback to improve their operations.

[0635] (Application Example 1)

[0636] 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".

[0637] There is a need to improve the productivity of the entire organization by streamlining operations and proposing optimal operating patterns for machinery used in factories. However, conventional systems have not been able to fully utilize information on individual work activities and machine operation, making it difficult to generate efficient suggestions.

[0638] 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.

[0639] In this invention, the server includes means for collecting activity information from business equipment, means for transferring the activity information to a central processing unit, means for analyzing behavioral trends based on the activity information in the central processing unit, and means for analyzing the operation information of factory machinery and proposing optimizations. This enables improved efficiency in business operations and improved operational efficiency of machinery used in factories.

[0640] "Business equipment" refers to electronic devices used to collect data related to business operations.

[0641] "Activity information" refers to data on business usage and performance acquired through business equipment.

[0642] A "central processing unit" is a computer system that manages and analyzes data collected from multiple terminals.

[0643] "Behavioral tendencies" refer to patterns that show the consistency and repetitive characteristics of users' activities in the course of work.

[0644] "Factory machinery" refers to automated equipment used in the manufacturing or production process.

[0645] "Operational information" refers to data related to the operating status of factory machinery, work efficiency, energy consumption, etc.

[0646] "Methods for proposing optimization" refers to the process of deriving improvement proposals to streamline operations or machine behavior based on collected data.

[0647] This invention is a system that transfers activity information collected from business equipment to a central processing unit, performs data analysis using a generated AI model, and generates efficiency improvement suggestions. Specifically, business equipment collects data related to the work at the work site and transmits it to the central processing unit. The central processing unit manages the collected activity information using a database management system, while using a programming language such as Python as a server.

[0648] The server analyzes behavioral trends based on collected data and utilizes generative AI models to derive suggestions for optimizing and improving the efficiency of operations. This not only improves the efficiency of business activities but also optimizes factory machinery. For example, if the server detects that a certain piece of equipment is consuming excessive energy during a specific time period, it can analyze that data and suggest more efficient usage times and methods.

[0649] Users receive suggestions monthly and can use them to implement specific business improvements and machine operation optimizations to increase efficiency. This enables improvements in both individual tasks and overall productivity. An example of a prompt for the generated AI model is, "Analyze the usage patterns of energy-intensive devices and suggest new operating methods to improve efficiency."

[0650] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0651] Step 1:

[0652] The terminal collects activity information through business equipment. It acquires operation data and input information from users performing their tasks using sensors and logs, and organizes this information as activity data. Inputs include keyboard input, mouse operations, and application usage time, which are provided as data streams. The output is the collected activity information.

[0653] Step 2:

[0654] The terminal transfers the collected activity information to the central processing unit (server). The terminal securely encrypts the obtained data and transmits it over the network. The input is organized activity information. By securely transmitting the data using an encryption protocol, the output is the activity information stored on the server.

[0655] Step 3:

[0656] The server analyzes collected activity information to identify behavioral trends. It queries the data stored on the server using a database management system and detects behavioral characteristics using a generated AI model. The input is accumulated activity information. After data analysis, it outputs characteristic data indicating behavioral trends.

[0657] Step 4:

[0658] The server generates suggestions for efficiency improvements using a generated AI model. It prompts the AI ​​model using prompt messages to request processing and generate optimization suggestions. The input is characteristic data of behavioral tendencies. Through the AI ​​model's dedicated processing, data suggesting work efficiency improvements is output.

[0659] Step 5:

[0660] The server notifies users of monthly efficiency improvement suggestions. The generated suggestions are compiled into a report and delivered to the user's device. The input is efficiency improvement suggestion data. The notification function is used to present the generated suggestions to the user.

[0661] Step 6:

[0662] Users improve their work processes based on suggestions received from the server. They review the efficiency improvements they receive and incorporate them into their daily workflows and machine operation procedures. The input is a suggestion report from the server. Ultimately, the output is improved work efficiency and increased productivity.

[0663] 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.

[0664] This invention provides a system that combines a business terminal, a central server, and an emotion engine that recognizes user emotions to achieve more advanced and adaptive business efficiency. Specifically, the business terminal measures employees' work activities, including application usage time, input history, and device operation status. In addition, the emotion engine installed in the terminal recognizes the user's emotions and collects emotion data in real time. This data is acquired using facial recognition and voice analysis technologies.

[0665] The collected work activity data and emotional data are securely transferred to a central server at regular intervals. On the server, a generating AI model analyzes the behavioral and emotional patterns of each employee based on this data. By considering both behavioral and emotional aspects, highly accurate suggestions for improving individual work efficiency become possible.

[0666] For example, if a user is experiencing negative emotions (e.g., stress or decreased concentration) while performing a specific task, the server can use this information to generate feedback suggesting a break or reconsidering the order of tasks. These suggestions take into account the alignment between the work situation and the user's emotional state, providing more realistic and effective improvement plans.

[0667] Every month, users receive personalized improvement suggestions based on analysis from the server. The feedback integrates past behaviors and emotional patterns, allowing users to improve their work efficiency and emotional management. As a result, the system contributes not only to increased work efficiency but also to improved employee well-being.

[0668] The following describes the processing flow.

[0669] Step 1:

[0670] When the workday begins, the terminal activates the activity tracker and emotion engine, starting to collect employee work activity data and emotion data. Work activity data includes application usage time, keyboard input, and mouse movements, while emotion data is collected through facial recognition and voice analysis.

[0671] Step 2:

[0672] The terminal temporarily stores the business activity data and sentiment data collected in real time in local storage. This allows for the integration of diverse data, which can then be used for later analysis.

[0673] Step 3:

[0674] The terminal periodically transfers encrypted business activity data and emotional data to a central server. Strict security management is in place during this process, and measures are in place to prevent unauthorized access.

[0675] Step 4:

[0676] The server aggregates the received data and performs preprocessing such as formatting standardization and removing outliers. This prepares a dataset suitable for analysis.

[0677] Step 5:

[0678] The server analyzes pre-processed data using a generated AI model to identify each employee's behavioral and emotional patterns. Here, the AI ​​utilizes machine learning algorithms to perform highly accurate analysis.

[0679] Step 6:

[0680] The server generates personalized work efficiency suggestions based on the analysis results. These suggestions take into account the user's emotional state and include advice to adjust workloads and encourage focus on specific tasks.

[0681] Step 7:

[0682] Users receive monthly feedback. The feedback evaluates both behavioral and emotional patterns, identifying areas for improvement and suggesting specific solutions.

[0683] Step 8:

[0684] Based on user-submitted suggestions, practical improvements are implemented, and the resulting new data is fed back into the next analysis cycle. This enhances individual adaptability and achieves sustainable operational efficiency.

[0685] (Example 2)

[0686] 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".

[0687] In conventional business management systems, suggestions to improve user work efficiency were often based solely on activity data related to the actual work. However, since users' emotions and mental state also significantly impact work efficiency, suggestions that do not consider these factors are insufficiently optimized. Therefore, there is a need for a system that can integrate and analyze work-related data and emotional information to provide individually customized suggestions.

[0688] 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.

[0689] In this invention, the server includes means for collecting business-related information from business equipment, means for analyzing behavioral characteristics based on business-related information and user emotional information, and means for generating work efficiency improvement suggestions based on the analysis results of behavioral and emotional characteristics. This makes it possible to generate accurate and personalized work improvement suggestions that take into account each user's individual work activities and emotional state.

[0690] "Business equipment" refers to devices and equipment used to collect data on users' daily work activities.

[0691] "Business-related information" refers to a collection of data collected through business equipment, such as user application usage time, input history, and device operation status.

[0692] A "central computer" refers to a central device or system that receives and stores business-related information and user sentiment information, and processes and analyzes that data.

[0693] "Emotional information" refers to data that represents the user's emotional state and is collected using facial recognition technology and voice analysis technology.

[0694] "Behavioral characteristics" refer to patterns and tendencies in a user's work activities, analyzed based on work-related information.

[0695] "Emotional characteristics" are data that shows the tendencies and patterns of a user's emotions, obtained through the analysis of emotional information.

[0696] A "generative AI model" is an artificial intelligence model used in data analysis and proposal generation. It has the ability to learn from large datasets and generate various outputs.

[0697] A "prompt statement" is an input statement used to give instructions to a generative AI model, and it plays a role in directing the content of the output that will be generated.

[0698] This invention aims to realize a system that improves operational efficiency and manages user emotions. The system has multiple functions and mainly includes operational equipment, a central computer, and emotion recognition technology.

[0699] Business equipment is a device that monitors users' daily work activities. This includes computers, smart devices, and dedicated business support terminals. Business equipment continuously records and collects business-related information such as application usage time, input history, and device operation status.

[0700] Furthermore, the professional equipment is equipped with an emotion engine that includes facial recognition and voice analysis technologies to collect user emotional information. This allows for the analysis of emotions from the user's facial expressions and voice, and the emotional information is converted into data in real time. Specifically, high-resolution cameras and high-sensitivity microphones are used as hardware, and voice recognition algorithms are operated as software.

[0701] The collected data is transferred to a central computer via a secure protocol at regular intervals. The central computer uses a generative AI model to process and analyze work-related information and emotional information in an integrated manner, thereby deriving behavioral and emotional characteristics for each user.

[0702] The server utilizes a generative AI model to provide specific suggestions based on analysis results, supporting the user's work efficiency. These suggestions include reordering tasks and recommending breaks. For example, if the server detects a decline in the user's concentration due to prolonged, continuous data entry, it will recommend taking a short break. These suggestions are generated by providing instructions to the generative AI model using prompt statements.

[0703] As a concrete example, consider the prompt: "How can I analyze user business data and real-time sentiment data to generate useful feedback for improving work efficiency?"

[0704] Based on the feedback provided, users can improve their work attitudes and manage their emotions. This leads to increased work efficiency and improved user well-being.

[0705] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0706] Step 1:

[0707] The terminal, as a business device, collects user work-related information. Specifically, it records application usage time, input history, and device operation status. Inputs include log data and operation history from various devices, which are saved to the terminal's storage in real time. Output is a dataset of work-related information.

[0708] Step 2:

[0709] The device collects user emotional information using its built-in emotion engine. It analyzes facial expressions and voice in real time using a camera and microphone to determine the user's emotional state. Inputs include video and audio data, which are analyzed to obtain emotional information. The output is a dataset containing emotional information.

[0710] Step 3:

[0711] The terminal transfers work-related and sentiment information to the central computer at regular intervals using a secure protocol. The input is a dataset of collected work-related and sentiment information. This is encrypted before transmission to ensure security. The output is the secure dataset that reaches the central computer.

[0712] Step 4:

[0713] The server operates on a central computer and analyzes received data using a generative AI model. Input consists of datasets of work-related information and emotional information, and the generative AI model extracts behavioral and emotional characteristics. Data processing and calculations reveal patterns specific to each user. The output is the analysis results: behavioral and emotional characteristics.

[0714] Step 5:

[0715] The server uses a generated AI model based on the analysis results to produce specific suggestions for improving work efficiency using prompt messages. The input consists of behavioral and emotional characteristics, which are used to instruct the model via prompt messages. Suggestions include things like changing the order of tasks or recommending breaks. The output is the content of the suggestions.

[0716] Step 6:

[0717] The server notifies the user of the generated suggestions. The user then uses the feedback from the server to improve their work attitude and manage their emotions. The input is the generated suggestions, and the action of notifying the user occurs. The output is the feedback received by the user.

[0718] (Application Example 2)

[0719] 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".

[0720] In today's industrial environment, improving operational efficiency and workplace safety are crucial challenges. In particular, improving operational efficiency and the work environment while considering the mental health of employees is necessary to increase industrial efficiency and ensure worker safety. However, systems that achieve both simultaneously are limited and currently difficult for many companies and facilities to utilize.

[0721] 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.

[0722] In this invention, the server includes means for collecting work data from a business information processing device, means for transferring the work data and human emotion data to a data collection device, and means for analyzing behavioral patterns and emotion patterns based on the work data and emotion data. This makes it possible to generate suggestions for improving work efficiency and safety and notify users in a timely manner.

[0723] A "business-use information processing device" is a terminal used to collect and process work data and emotional data, and is typically used in factories, commercial facilities, and other similar establishments.

[0724] A "data aggregation device" is a device that stores collected data and performs analysis as needed. It is a data management device that is connected to multiple devices via a network.

[0725] "Work data" refers to data acquired from business information processing equipment, such as work progress, operation history, and usage status.

[0726] "Emotional data" refers to data that represents human emotions, and is information acquired in real time through voice analysis and facial recognition.

[0727] "Behavioral patterns" refer to a series of behavioral histories and characteristics of employees and equipment, analyzed based on work data.

[0728] "Emotional patterns" refer to changes or tendencies in emotions over time or in different situations, as analyzed based on emotional data.

[0729] A "generative AI model" is an artificial intelligence technology used to learn from work data and emotional data, and to generate suggestions for effective business efficiency and improved safety.

[0730] A "proposal" is a set of improvement measures or innovations aimed at increasing work efficiency and safety, based on an analysis of behavioral and emotional patterns.

[0731] A "user" is an individual or organization that has the right to use this system and receive proposals.

[0732] In this embodiment of the invention, first, a business information processing device collects work data such as the progress of each task, the operation history of equipment, and usage status. This information processing device is equipped with emotion recognition technology for analyzing voice and facial expressions in real time, thereby simultaneously acquiring user emotion data.

[0733] The collected work data and emotional data are securely transferred via the network to a data aggregation device. This device functions as a central server, storing and managing the data while maintaining security by appropriately encoding the acquired data.

[0734] The central server uses a generative AI model to analyze behavioral and emotional patterns by linking work data and emotional data. This analysis generates suggestions to improve operational efficiency and safety. For example, it is possible to suppress robot movements around workers experiencing stress, thereby creating a safer environment.

[0735] The generated suggestions are communicated to users in a timely manner, providing them with concrete guidance for improving their work. The hardware of this entire system includes sensor devices, information processing devices, and communication infrastructure for optimizing signals. The software employs emotion recognition algorithms and data analysis engines, enabling advanced data management.

[0736] For example, in a factory setting where a worker is directing multiple operations, if it is determined that the worker's stress level is high, the robot's operating speed can be adjusted to ensure the worker's safety. An example of a prompt to the generative AI model used in this case would be: "Design an AI model that analyzes the emotions of human workers in the factory in real time and adjusts the robot's movements to ensure safety."

[0737] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0738] Step 1:

[0739] The terminal collects work data and emotional data. Inputs here include the worker's actions, voice, and facial expressions, while outputs are digital data derived from these. The terminal uses sensors and cameras to record the user's operation history, voice tone, and facial changes.

[0740] Step 2:

[0741] The terminal transfers the collected data to the data aggregation device. The input for this step is work data and sentiment data collected by the terminal, and the output is securely encrypted data. Encryption algorithms are applied to enhance security during data transmission.

[0742] Step 3:

[0743] The server stores the received data and prepares it for analysis. The input is the transmitted encrypted data, and the output is the information converted into an analyzable format. The server uses a database to store the data and performs decryption to restore it to an analyzable state.

[0744] Step 4:

[0745] The server uses a generative AI model to analyze behavioral and emotional patterns. This analysis uses stored work and emotional data as input, and the output is specific behavioral and emotional patterns based on the analysis. The generative AI model processes the data and detects trends and anomalies.

[0746] Step 5:

[0747] The server generates suggestions for improving safety and efficiency based on the analysis results. Here, specific behavioral and emotional patterns are taken as input, and concrete work suggestions are output. The generating AI automatically creates suggestions and proposes appropriate measures as the next action step.

[0748] Step 6:

[0749] The system notifies the user of the suggestions. The server generates the suggestions as input, and the output is the result presented in a user-friendly format. Notification methods include terminals, email, and dedicated applications.

[0750] This establishes a complete workflow from data collection, transfer, analysis, and proposal development through terminals and servers, resulting in improved operational efficiency and security.

[0751] 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.

[0752] 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.

[0753] 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 robot 414.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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."

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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 as being incorporated by reference.

[0772] The following is further disclosed regarding the embodiments described above.

[0773] (Claim 1)

[0774] A means of collecting business activity data from business terminals,

[0775] A means for transferring the aforementioned business activity data to a central server,

[0776] The central server includes means for analyzing behavioral patterns based on the business activity data,

[0777] A means for generating suggestions for improving work efficiency based on the results of the analysis of the aforementioned behavioral patterns,

[0778] A means of notifying users of the aforementioned proposal on a monthly basis,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, further comprising means for encrypting and transferring the aforementioned business activity data.

[0782] (Claim 3)

[0783] The system according to claim 1, further comprising means for using a generative AI model to analyze the aforementioned behavioral patterns.

[0784] "Example 1"

[0785] (Claim 1)

[0786] Means for collecting work activity information from commercial equipment,

[0787] Means for transferring the aforementioned work activity information to a central information processing device,

[0788] The central information processing device includes means for analyzing behavioral patterns based on the work activity information,

[0789] A means for generating suggestions for improving work efficiency based on the analysis results of the aforementioned behavioral patterns,

[0790] A means of notifying users of the aforementioned proposal within a normal period,

[0791] Means for storing the aforementioned work activity information in an integrated database,

[0792] A means of providing the aforementioned proposal as a report so that users can implement it,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, further comprising means for encrypting and transferring the aforementioned work activity information.

[0796] (Claim 3)

[0797] The system according to claim 1, further comprising means for using a generative artificial intelligence model to analyze the aforementioned behavioral patterns.

[0798] "Application Example 1"

[0799] (Claim 1)

[0800] Means for collecting activity information from business equipment,

[0801] Means for transferring the aforementioned activity information to the central processing unit,

[0802] The central processing unit includes means for analyzing behavioral trends based on the activity information,

[0803] A means for generating suggestions for improving work efficiency based on the results of the analysis of the aforementioned behavioral trends,

[0804] A means of notifying users of the aforementioned proposal on a monthly basis,

[0805] A means of analyzing the operational information of factory machinery and proposing optimizations,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, further comprising means for encrypting and transmitting the aforementioned activity information.

[0809] (Claim 3)

[0810] The system according to claim 1, further comprising means for using a generative AI model to analyze the aforementioned behavioral tendencies and operational information of factory machinery.

[0811] "Example 2 of combining an emotion engine"

[0812] (Claim 1)

[0813] Means for collecting business-related information from business equipment,

[0814] A means for transferring the aforementioned business-related information to a central computer,

[0815] The central computer includes means for analyzing behavioral characteristics based on the business-related information and user sentiment information,

[0816] A means for generating suggestions for improving work efficiency based on the analysis results of the aforementioned behavioral and emotional characteristics,

[0817] A means of periodically notifying the user of the aforementioned proposal,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, further comprising means for securely transferring the aforementioned business-related information and emotional information.

[0821] (Claim 3)

[0822] The system according to claim 1, comprising means for using a generative AI model to analyze the aforementioned behavioral and emotional characteristics, and generating suggestions using prompt sentences.

[0823] "Application example 2 of combining emotional engines"

[0824] (Claim 1)

[0825] A means of collecting work data from a business information processing device,

[0826] Means for transferring the aforementioned work data and human emotion data to a data collection device,

[0827] The aforementioned data collection device includes means for analyzing behavioral patterns and emotional patterns based on the work data and emotional data,

[0828] A means for generating proposals for improving work efficiency and safety based on the analysis results of the aforementioned behavioral patterns and emotional patterns,

[0829] Means for notifying users of the aforementioned proposal,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, further comprising means for encoding and transmitting the aforementioned work data and emotional data.

[0833] (Claim 3)

[0834] The system according to claim 1, further comprising means for using a generative AI model to analyze the aforementioned behavioral patterns and emotional patterns. [Explanation of Symbols]

[0835] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting business activity data from business terminals, A means for transferring the aforementioned business activity data to a central server, The central server includes means for analyzing behavioral patterns based on the business activity data, A means for generating suggestions for improving work efficiency based on the results of the analysis of the aforementioned behavioral patterns, A means of notifying users of the aforementioned proposal on a monthly basis, A system that includes this.

2. The system according to claim 1, further comprising means for encrypting and transferring the aforementioned business activity data.

3. The system according to claim 1, further comprising means for using a generative AI model to analyze the aforementioned behavioral patterns.

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