system

Abstract

We provide the system. [Solution] In an information processing device that manages a database equipped with means for accumulating and storing configuration information, the means for collecting information and A means of analyzing configuration information using machine learning algorithms to identify elements that contribute to improving individual performance, A means of generating and outputting action suggestions from analysis results using natural language generation technology, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional companies, it is difficult to individually provide appropriate work advice and career guidance for each employee with diverse backgrounds, which has led to problems such as decreased motivation, deteriorated work efficiency, and even increased turnover rates. In such a situation, there is a need for a system that effectively supports the improvement of work styles and career building according to individual needs by integratively utilizing various configuration information such as employees' work histories, health statuses, and communication patterns.

Means for Solving the Problems

[0005] This invention provides an information processing device that manages a database equipped with means for accumulating and storing configuration information. The device collects information and analyzes it using a machine learning algorithm to identify elements that contribute to improving the performance of individual employees. Furthermore, it generates and outputs specific action suggestions using natural language generation technology, thereby providing work style improvement suggestions tailored to the needs of each employee. Finally, by improving the machine learning algorithm based on user feedback and enhancing the accuracy of the suggestions, the system enables increased overall business efficiency and employee satisfaction within the company.

[0006] "Configuration information" refers to data related to improving individual performance, such as an employee's work history, health status, and communication patterns.

[0007] An "information processing device" is a computer system used to manage databases and collect, store, and analyze configuration information.

[0008] A "machine learning algorithm" is a computational method used to extract patterns from data and perform predictions and classifications, and is used for analyzing employee composition information.

[0009] "Natural language generation technology" is a technology that automatically generates human language using computers, and is used to create action suggestions from the analysis results.

[0010] "Action suggestions" refer to specific action plans and advice provided to individual employees based on the analysis results.

[0011] "Feedback" refers to the evaluation, opinions, and response information based on the results of implementation of suggestions received by employees. [Brief explanation of the drawing]

[0012] [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]

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a tagged 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.

[0016] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, a tagged communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system that provides personalized business improvement utilizing employee composition information. Its specific form is described below.

[0034] The server collects data including employees' work history, health status, and various communication patterns. This data is obtained from WiFi connection information, location information systems, and monitoring devices. It also collects data on employees' daily health status through facial expression analysis during their arrival and departure times.

[0035] The server stores this data in a database and analyzes it using machine learning algorithms. This analysis identifies individual employee work patterns and factors influencing their performance. Based on these results, natural language generation technology is used to create specific improvement measures and career development advice.

[0036] The terminal delivers generated reports to the user. Users can refer to the reports and advice provided by the terminal to improve their work and build their careers. This allows users to autonomously review their work methods and improve efficiency.

[0037] As a concrete example, consider a case where an employee's productivity is declining. The server analyzes this employee's past work patterns and health history, suggesting that a decrease in communication frequency or lack of sleep may be contributing factors.

[0038] Based on this analysis, the device will present employees with specific suggestions in a chatbot format regarding ways to improve communication and health management. This will allow users to review their own behavior and take appropriate corrective measures, enabling them to regain high performance.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server collects work history, health status, WiFi and location information, and communication pattern data obtained from monitoring devices, and stores it in a database.

[0042] Step 2:

[0043] The server preprocesses the collected data, imputing missing values ​​and removing noise to prepare an analyzable dataset.

[0044] Step 3:

[0045] The server analyzes pre-processed data using machine learning algorithms to identify the causes of performance degradation and areas for improvement for each employee.

[0046] Step 4:

[0047] Based on the analysis results, the server automatically generates specific action suggestions for individual work improvement and career development using natural language generation technology.

[0048] Step 5:

[0049] The terminal delivers the generated proposal report to the user and presents it to the user through notifications and chatbot functions.

[0050] Step 6:

[0051] The user reviews the suggestions from their device and either takes action on any suggestions they accept or sends feedback to their device.

[0052] Step 7:

[0053] The server collects user feedback and uses it to improve machine learning algorithms, making future suggestions even more accurate.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In recent years, there has been a growing demand for systems that comprehensively manage employee work efficiency and health to improve performance. However, traditional systems have struggled to provide sufficiently personalized improvement measures to individual employees, resulting in a failure to contribute to overall organizational productivity improvement.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server is a data recording device equipped with means for collecting and storing employee activity history, health status, and contact methods, and includes means for collecting information, means for analyzing employee configuration information using machine learning methods to identify factors that contribute to improving individual work efficiency, and means for generating and distributing improvement suggestions based on the analysis results using natural language generation technology. This makes it possible to provide specific and effective work improvement measures tailored to each employee, promote improved employee performance, and improve the productivity of the entire organization.

[0059] "Means of collecting information" refers to data recording devices and related technologies necessary to collect and store information, including employee activity history, health status, and contact methods.

[0060] "Means of analysis using machine learning methods" refers to the process and algorithms of applying machine learning techniques to identify factors that contribute to performance improvement based on collected configuration information.

[0061] "A means of generating and distributing improvement suggestions using natural language generation technology" refers to a method of using natural language generation technology to generate specific business improvement suggestions based on analysis results and provide them to employees in a format suitable for them.

[0062] "Means of transmitting to users via a receiving device" refers to a communication method that uses an electronic receiving device to ensure that the generated improvement suggestions are reliably delivered to users.

[0063] "Means of obtaining responses" refers to the methods and processes for collecting feedback and reactions provided by users and incorporating them into the system.

[0064] "Methods for improving machine learning methods" refers to the process of optimizing machine learning algorithms and models based on collected responses in order to improve the accuracy of analysis and the effectiveness of proposed solutions.

[0065] This invention is a system that provides personalized business improvement utilizing employee configuration information. Its specific form is described below.

[0066] The server collects information using data recording devices that aggregate employee activity history, health status, and contact methods. Specifically, it tracks employees' movement patterns within the office using WiFi connection information and understands relationships between different departments using a location information system. In addition, monitoring devices analyze employees' facial expressions when they arrive at and leave work, and acquire data to evaluate their daily health status.

[0067] The server stores the collected data in a database. The stored data is analyzed by applying machine learning algorithms. Here, existing technologies are used to identify key factors related to improving employee performance. Based on the identified factors, natural language generation technology is used to generate specific work improvement measures and career development advice.

[0068] The terminal sends generated improvement suggestions and reports to the user. The user can review their work by referring to the provided reports and use them to improve their operations. This allows the user to autonomously practice efficient work methods and improve performance.

[0069] As a concrete example, consider a case where an employee's productivity is declining. The server can analyze the employee's past activity patterns and health information, and suggest that work interruptions or decreased communication frequency may be affecting productivity. Based on this analysis, the terminal provides the user with appropriate improvement suggestions as an action plan. Such suggestions may include ways to encourage more frequent communication and advice on health management.

[0070] As an example of a prompt using a generative AI model, the system provides a response when the user inputs "Please provide an analysis of this week's business performance and suggestions for improvement."

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The server collects data on employee activity history, health status, and contact methods. Inputs include WiFi connection information, location data, and data from monitoring devices. This information is output in a format that the server stores in its database. Specifically, the server receives information in real time using a data collection module, organizes it in a specific format, and then stores it long-term.

[0074] Step 2:

[0075] The server uses the collected data to run machine learning algorithms and perform analysis. The database information collected in step 1 is used as input. The output generates a list of factors that affect performance and their weights. Specifically, the server uses anomaly detection algorithms to identify unusual patterns in the data and analyze their causes.

[0076] Step 3:

[0077] The server uses natural language generation technology to generate improvement suggestions based on the analysis results from Step 2. Analysis data is required as input. The output is improvement suggestions in natural language format that can be sent to the user. Specifically, the server utilizes a generation AI model to logically translate the analysis results into text and form suggestions tailored to individual employees.

[0078] Step 4:

[0079] The terminal receives suggestions from the server and distributes them to the user. The input is the improvement suggestions generated in step 3. The output is the improvement suggestion message presented to the user. Specifically, the terminal provides these messages to the user using its notification function and launches an interface where the user can review the content.

[0080] Step 5:

[0081] Users refer to suggestion messages provided from their terminals and create feedback on their own work activities. The input is improvement suggestion messages. The output is information supplied back to the system in the form of feedback. Specifically, users review their actions based on the suggestions and report the effects of those actions to the system sequentially.

[0082] Step 6:

[0083] The server analyzes the feedback provided by the user in step 5 and uses it to improve the machine learning algorithm. The input is the feedback data. The output is the algorithm model for generating updated suggestions. Specifically, the server continuously collects feedback data and tunes the model to improve the accuracy of the algorithm.

[0084] (Application Example 1)

[0085] 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."

[0086] For robots and automated machinery used in factories, effectively performing preventative maintenance while maintaining efficient work performance is crucial from the perspective of reducing operating costs and improving safety. However, conventional methods have made it difficult to accurately assess the operating status of these machines and propose necessary maintenance at the appropriate time.

[0087] 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.

[0088] In this invention, the server is an information processing device that manages a database equipped with means for accumulating and storing configuration information, and includes means for collecting information, means for analyzing the configuration information using a machine learning algorithm to identify elements that contribute to improving the efficiency of individual tasks, means for generating and outputting action suggestions from the analysis results using natural language generation technology, and means for analyzing the operating status using a machine learning algorithm to generate suggestions for efficiency improvements and preventive maintenance. This makes it possible to evaluate the operating status of robots and automated machines in real time and propose the optimal maintenance timing.

[0089] "Configuration information" refers to various attribute data and historical data related to individuals or machines, and is the information that serves as material for analysis based on this data.

[0090] An "information processing device" is a computer device used for collecting, storing, and analyzing information, and has the function of managing databases and performing necessary calculations.

[0091] A "machine learning algorithm" is a set of computational methods that have the ability to learn patterns and rules from data, and in this invention, it is used to identify elements that contribute to individual efficiency improvements.

[0092] "Natural language generation technology" is a technology that enables machines to create text in a natural language that humans can understand, and in this invention, it is used to create action suggestions from analysis results.

[0093] "Operating status" refers to information about the operating conditions of a machine or robot, and includes variables such as operating speed, temperature, and vibration.

[0094] "Preventive maintenance" refers to maintenance work performed regularly to prevent machine and system failures, and is a planned and systematic maintenance activity.

[0095] The system that realizes this invention consists of a server as an information processing device and a terminal used by the user. The server has a database for collecting and managing operational data of robots and automated machinery in the factory in real time, and analyzes this configuration information using machine learning algorithms.

[0096] The server uses Python as its programming language and performs analysis using TENSORFLOW® and other machine learning libraries. Operating status data consists of sensor data such as vibration, temperature, and operating patterns. By inputting this data into machine learning algorithms, factors contributing to efficiency improvements and the need for preventative maintenance are identified.

[0097] Based on the analysis results, the server uses natural language generation technology to generate specific maintenance suggestions, such as "The robot arm needs an oil change in XX minutes." These suggestions are created based on the content input to the AI ​​model as prompts.

[0098] The terminal plays the role of presenting these suggestions to the user. The user receives the suggestions sent from the server via a device such as a smartphone or tablet. For example, the user can input a prompt such as "Please tell me the timing for preventative maintenance based on vibration sensor data" and then check the suggested content.

[0099] In this way, users can improve the efficiency of the machine, perform maintenance at the appropriate time, and contribute to maintaining the machine's long-term performance.

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

[0101] Step 1:

[0102] The server acquires real-time robot operation data from various sensors within the factory. The input data includes sensor data such as vibration, temperature, and motion patterns. This data is stored in a database on the server, preparing it for analysis using machine learning algorithms.

[0103] Step 2:

[0104] The server inputs stored operational data into a machine learning algorithm. This algorithm, implemented using TensorFlow, compares past performance data with current data to extract patterns related to efficiency improvements. As a result, it outputs predicted signs of degradation or anomalies.

[0105] Step 3:

[0106] The server uses natural language generation technology to generate specific maintenance suggestions based on analysis results obtained by machine learning algorithms. Using the analysis results as input data, the generating AI model outputs maintenance recommendations such as "The robot arm needs an oil change in XX minutes."

[0107] Step 4:

[0108] The terminal receives maintenance proposals sent from the server and notifies the user. The user can view the proposals via their smartphone or tablet. The user reviews the proposals on the terminal and makes the necessary decisions to reflect them in the actual maintenance schedule.

[0109] Step 5:

[0110] Users take specific actions based on the suggestions and send feedback to the server via their devices. Based on the feedback as input data, the server updates its machine learning algorithm, performs data calculations to improve the accuracy of future suggestions, and builds a dataset for making more accurate suggestions in the future.

[0111] 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.

[0112] This invention provides a system that incorporates emotional data into configuration information to more effectively support employee work improvement. Its specific form is described below.

[0113] The server collects data on employees' work history, health status, and communication patterns, and uses an emotion engine to extract and collect emotional data from users' facial expressions and voice. This data is stored in a database and used to reflect the changes in users' work patterns and emotions.

[0114] Based on the collected data, the server uses machine learning algorithms to analyze factors and emotional tendencies that contribute to performance improvement. This analysis makes it possible to generate advice that takes into account the impact of emotions on work performance.

[0115] Based on the analysis results obtained, the server uses natural language generation technology to create specific action suggestions for improving work performance and career development for each employee. These suggestions include stress management methods tailored to emotions and communication improvement measures that take into account emotional states.

[0116] The terminal presents the user with action suggestions received from the server. Based on the suggestions received, the user can select and implement actions to improve their own work. The user can also provide feedback on each suggestion, which helps in further refinement of the system.

[0117] As a concrete example, consider a case where an employee has recently been experiencing stress. The server detects a high stress level through facial expression and voice analysis of the employee. Using this emotional data, the system aims to reduce the employee's mental burden by suggesting relaxation methods and the importance of taking regular rest periods.

[0118] The terminal displays these suggestions as notifications during employees' lunch breaks or after work, increasing the likelihood that users will take the suggested actions. In this way, the system, which incorporates emotional data, enables more personalized support, improving work efficiency and promoting overall employee well-being.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The server collects work history, health status, WiFi and location information, and communication data obtained from monitoring devices. It also uses an emotion engine to analyze user facial expressions and voice data, and extracts emotional data. This data is then stored in a database.

[0122] Step 2:

[0123] The server preprocesses the collected data, removing noise and imputing missing values ​​to prepare a dataset suitable for analysis. During this process, it integrates sentiment data with other constituent information.

[0124] Step 3:

[0125] The server uses machine learning algorithms to analyze integrated data and reveal individual user performance and emotional tendencies. This analysis identifies areas for improvement in operations and the impact of emotions on work processes.

[0126] Step 4:

[0127] Based on the analysis results obtained, the server uses natural language generation technology to create personalized action suggestions. These suggestions include stress management and communication improvement measures tailored to the user's emotional state.

[0128] Step 5:

[0129] The device notifies the user of action suggestions received from the server. These notifications are delivered via chatbot functionality or pop-up messages, making them easily accessible to the user.

[0130] Step 6:

[0131] Users can review suggestions from their devices and implement those they accept. They can also provide feedback on the effectiveness of suggestions through their devices.

[0132] Step 7:

[0133] The server collects user feedback and uses it to improve machine learning algorithms and sentiment engines. This allows the system to generate more accurate suggestions in the future.

[0134] (Example 2)

[0135] 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".

[0136] In today's work environment, an individual's work style and health can directly impact their work performance. However, traditional methods have made it difficult to accurately understand the emotional and health states of individual employees and propose concrete improvement measures based on that understanding. As a result, employee health has not been optimally managed, leading to problems such as decreased work efficiency.

[0137] 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.

[0138] In this invention, the server is a data processing system that manages a storage device equipped with elements for accumulating and holding configuration information, and includes means for collecting information relating to work history, health status, communication patterns, and emotional state; means for analyzing the configuration information using an analysis device to identify elements and emotional tendencies that contribute to an individual's work direction; and means for generating and outputting individual action suggestions from the analysis results using a natural language generation device. This enables the automatic generation of appropriate improvement measures tailored to each employee's condition, thereby improving health management and work efficiency.

[0139] "Configuration information" is a general term for information in a data processing system that represents an individual's work history, health status, communication patterns, and emotional state.

[0140] A "storage device" refers to a physical or virtual device used to collect and store information, and is used to manage the collected configuration information.

[0141] A "data processing system" is a system that includes a set of devices or software for collecting and analyzing configuration information and generating information-based actions.

[0142] An "analysis device" is a device that uses configuration information to analyze data through machine learning algorithms and other means to identify factors and emotional tendencies that contribute to an individual's work style.

[0143] A "natural language generation device" is a device or software that automatically generates action suggestions tailored to individual situations based on analysis results, and outputs them as text.

[0144] A "display device" refers to a device that communicates proposals generated by a server to the user and presents them in an actionable format.

[0145] "Evaluation information" refers to information that shows feedback and implementation results regarding suggestions submitted by users, and is data that can be used to improve the system.

[0146] This invention is an information processing system designed to optimize employees' work performance and health. To implement this invention, it is necessary to combine various hardware and software components to collect and analyze various employee data and generate optimal action suggestions.

[0147] The server collects "configuration information" such as work history, health status, communication patterns, and emotional state. This process may utilize wearable devices, integrated sensors, and health management applications. Additionally, software that analyzes emotions from facial expressions and voice is used as an emotion engine. Specific examples include the Facial Emotion Recognition API and Speech Emotion Recognition Software.

[0148] The server stores the collected data in a "storage device," and then an "analytical device" analyzes the data. The analytical device uses machine learning algorithms to extract patterns from the data and identify individual work patterns and emotional tendencies. Libraries such as scikit-learn and TensorFlow are examples of such libraries.

[0149] Based on these results, the server uses a "natural language generation device" to create specific action suggestions. In this process, OpenAI's GPT model and other tools are used to generate action suggestions for each employee. Examples of action suggestions include relaxation methods and recommendations for short breaks for employees experiencing high stress levels.

[0150] The generated action suggestions are delivered to employees as notifications via their devices. These notifications are presented in the form of email or on-screen messages and are delivered appropriately at a time that suits the user's situation. For example, when suggesting a break time during work, the suggestion might be presented before lunchtime.

[0151] A specific example of a prompt message would be, "Use employee work history and emotional data to generate specific suggestions for stress reduction."

[0152] Thus, the system of the present invention can improve work performance and maintain health by making work improvement suggestions that take into account changes in emotions.

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] The server collects configuration information for each employee, including their work history, health status, communication patterns, and emotional state. Input data includes data from wearable devices and health management applications. Based on this, it extracts emotional data from facial expressions and voice using an emotion engine (e.g., Facial Emotion Recognition API). The output is the integrated data stored in a database.

[0156] Step 2:

[0157] The server analyzes data using machine learning algorithms based on collected configuration information. Inputs include work history and sentiment data stored in a database. This data is then analyzed using analytical tools (e.g., TensorFlow) to identify factors and emotional tendencies that contribute to an individual's work style. The output provides insights into performance improvement based on the analysis results.

[0158] Step 3:

[0159] The server generates specific action suggestions using natural language generation technology based on the analysis results. The input includes insights gained from the analysis. A generative AI model (e.g., OpenAI's GPT model) is used to create optimal suggestions for each employee in written form. The output is a personalized suggestion text for each employee.

[0160] Step 4:

[0161] The terminal notifies the user of action suggestions sent from the server. The input is a suggestion text generated by the server. This is presented to the user via a display device, for example, as a pop-up message or email notification. The output allows the user to confirm the suggested action.

[0162] Step 5:

[0163] The user selects and implements actions based on the presented suggestions. The input is the suggestions presented from the terminal. The user considers the content and decides to take action to improve their own work. The output includes the results of the proposed actions and feedback.

[0164] Step 6:

[0165] The server collects user feedback and stores it back in the database. The input includes feedback information provided by users through their devices. This is used to improve the machine learning algorithm and enhance the accuracy of future suggestions. The output is improved accuracy in subsequent analyses and suggestion generation.

[0166] (Application Example 2)

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

[0168] In today's consumer environment, accurately understanding customers' emotional states and providing personalized service accordingly is a challenging task. Especially for large retail stores and companies with diverse customer bases, there is a need for customer service support that utilizes real-time emotion recognition to achieve both improved customer satisfaction and increased sales.

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

[0170] In this invention, the server is an information processing device that manages an aggregation device equipped with means for accumulating and holding configuration information, and includes means for collecting data, means for analyzing the configuration information and emotional data using a machine learning algorithm to identify elements that improve the service efficiency of individuals, and means for generating optimal service proposals based on emotional data and outputting them to the service provider's visual display device. This makes it possible to grasp the customer's emotional state in real time and quickly provide appropriate service proposals.

[0171] "Configuration information" refers to a collection of data or elements, and in particular, the information that constitutes the input data used within a system.

[0172] A "data aggregation device" is a device for comprehensively collecting and managing data, and functions as a database within an information processing device.

[0173] An "information processing device" is a device used for collecting, storing, and analyzing data, and is particularly responsible for managing configuration information.

[0174] "Emotional data" refers to data that indicates an individual's emotional state, and includes information extracted from facial expressions and voice.

[0175] A "machine learning algorithm" is a computational method used to learn patterns from data and perform predictions and classifications, and is used in the analysis of constituent information and sentiment data.

[0176] "Individual service efficiency" refers to the efficiency of performing tasks or services, and serves as an indicator of an individual's performance.

[0177] A "service provider" refers to someone who directly provides goods or services to customers and is responsible for customer service.

[0178] A "visual display device" is a device used to display information visually, and this includes, in particular, the displays of digital terminals and smart glasses.

[0179] To implement this invention, a server acts as the central point for information processing and suggestion generation. The server first uses a network-connected integrator to receive configuration information and emotion data from smart glasses. This integrator collects the customer's facial expressions and voice in real time and transmits them to the server as configuration information.

[0180] The server analyzes this configuration information and sentiment data using machine learning algorithms. This analysis identifies factors that contribute to improving the individual's service efficiency and prepares it to propose the most suitable service to the customer. The algorithms used here utilize TensorFlow and OpenCV for sentiment analysis.

[0181] Next, the server uses natural language generation technology to generate action suggestions based on the identified elements and outputs them to a visual display device. This visual display device is a smart glasses display, and the information is delivered directly to the service provider. In this process, the information is visualized quickly and efficiently, allowing the service provider to respond appropriately to the customer.

[0182] As a concrete example, consider a scenario where a family visits a store. The server, upon receiving emotion analysis indicating that a child has shown interest in a particular product, can notify the store clerk via smart glasses with a description of the product and related discount information.

[0183] This system will enable personalized service tailored to each customer, leading to improved customer satisfaction.

[0184] An example of a prompt message would be, "Identify the product category that the customer in front of me is interested in, and generate a customer service suggestion based on that." Sending this instruction to the server will generate an appropriate suggestion.

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The server receives configuration information and emotion data in real time from the smart glasses. It also receives image and audio data acquired by the camera and microphone as input. This data serves as material for analyzing the customer's facial expressions and voice tone. Based on this, the server uses facial recognition software (e.g., OpenCV) to extract facial expression data and an audio analysis engine (e.g., Google® Speech-to-Text API) to convert the audio data into text.

[0188] Step 2:

[0189] The server uses the received facial expression data and voice text to send emotion analysis prompts to a generative AI model to evaluate the customer's emotional state. The input includes the data obtained along with the prompt text, and the output is a classification result of the emotions the customer is exhibiting and their tendencies. This process is performed using analysis software (e.g., TensorFlow) on hardware running machine learning algorithms.

[0190] Step 3:

[0191] The server uses natural language generation technology to create service suggestions for customers based on the results of sentiment analysis. The input is data on sentiment classification and its fluctuations, and based on this, it infers what kind of service will increase customer interest and satisfaction. As output, specific suggestion sentences are generated. A natural language generation algorithm (e.g., GPT model) is used here.

[0192] Step 4:

[0193] The server sends the generated suggestion text to the smart glasses' visual display. This allows the service provider (store clerk) to instantly see the optimal action for the customer via the display. The input is the generated suggestion text, and the output is specific instructions displayed on the visual display. Based on this, the store clerk can respond quickly.

[0194] Step 5:

[0195] The user (store clerk) observes the results of their suggestions to customers and sends the responses to the server. The input is a record of the customer's response to the presented suggestions, and the output is feedback data stored on the server to improve the accuracy of future suggestions. This data is used to retrain the machine learning algorithm for future improvements.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Second Embodiment]

[0200] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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".

[0212] This invention is a system that provides personalized business improvement utilizing employee composition information. Its specific form is described below.

[0213] The server collects data including employees' work history, health status, and various communication patterns. This data is obtained from WiFi connection information, location information systems, and monitoring devices. It also collects data on employees' daily health status through facial expression analysis during their arrival and departure times.

[0214] The server stores this data in a database and analyzes it using machine learning algorithms. This analysis identifies individual employee work patterns and factors influencing their performance. Based on these results, natural language generation technology is used to create specific improvement measures and career development advice.

[0215] The terminal delivers generated reports to the user. Users can refer to the reports and advice provided by the terminal to improve their work and build their careers. This allows users to autonomously review their work methods and improve efficiency.

[0216] As a concrete example, consider a case where an employee's productivity is declining. The server analyzes this employee's past work patterns and health history, suggesting that a decrease in communication frequency or lack of sleep may be contributing factors.

[0217] Based on this analysis, the device will present employees with specific suggestions in a chatbot format regarding ways to improve communication and health management. This will allow users to review their own behavior and take appropriate corrective measures, enabling them to regain high performance.

[0218] The following describes the processing flow.

[0219] Step 1:

[0220] The server collects work history, health status, WiFi and location information, and communication pattern data obtained from monitoring devices, and stores it in a database.

[0221] Step 2:

[0222] The server preprocesses the collected data, imputing missing values ​​and removing noise to prepare an analyzable dataset.

[0223] Step 3:

[0224] The server analyzes pre-processed data using machine learning algorithms to identify the causes of performance degradation and areas for improvement for each employee.

[0225] Step 4:

[0226] Based on the analysis results, the server automatically generates specific action suggestions for individual work improvement and career development using natural language generation technology.

[0227] Step 5:

[0228] The terminal delivers the generated proposal report to the user and presents it to the user through notifications and chatbot functions.

[0229] Step 6:

[0230] The user reviews the suggestions from their device and either takes action on any suggestions they accept or sends feedback to their device.

[0231] Step 7:

[0232] The server collects user feedback and uses it to improve machine learning algorithms, making future suggestions even more accurate.

[0233] (Example 1)

[0234] 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."

[0235] In recent years, there has been a growing demand for systems that comprehensively manage employee work efficiency and health to improve performance. However, traditional systems have struggled to provide sufficiently personalized improvement measures to individual employees, resulting in a failure to contribute to overall organizational productivity improvement.

[0236] 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.

[0237] In this invention, the server is a data recording device equipped with means for collecting and storing employee activity history, health status, and contact methods, and includes means for collecting information, means for analyzing employee configuration information using machine learning methods to identify factors that contribute to improving individual work efficiency, and means for generating and distributing improvement suggestions based on the analysis results using natural language generation technology. This makes it possible to provide specific and effective work improvement measures tailored to each employee, promote improved employee performance, and improve the productivity of the entire organization.

[0238] "Means of collecting information" refers to data recording devices and related technologies necessary to collect and store information, including employee activity history, health status, and contact methods.

[0239] "Means of analysis using machine learning methods" refers to the process and algorithms of applying machine learning techniques to identify factors that contribute to performance improvement based on collected configuration information.

[0240] "A means of generating and distributing improvement suggestions using natural language generation technology" refers to a method of using natural language generation technology to generate specific business improvement suggestions based on analysis results and provide them to employees in a format suitable for them.

[0241] "Means of transmitting to users via a receiving device" refers to a communication method that uses an electronic receiving device to ensure that the generated improvement suggestions are reliably delivered to users.

[0242] "Means of obtaining responses" refers to the methods and processes for collecting feedback and reactions provided by users and incorporating them into the system.

[0243] "Methods for improving machine learning methods" refers to the process of optimizing machine learning algorithms and models based on collected responses in order to improve the accuracy of analysis and the effectiveness of proposed solutions.

[0244] This invention is a system that provides personalized business improvement utilizing employee configuration information. Its specific form is described below.

[0245] The server collects information using data recording devices that aggregate employee activity history, health status, and contact methods. Specifically, it tracks employees' movement patterns within the office using WiFi connection information and understands relationships between different departments using a location information system. In addition, monitoring devices analyze employees' facial expressions when they arrive at and leave work, and acquire data to evaluate their daily health status.

[0246] The server stores the collected data in a database. The stored data is analyzed by applying machine learning algorithms. Here, existing technologies are used to identify key factors related to improving employee performance. Based on the identified factors, natural language generation technology is used to generate specific work improvement measures and career development advice.

[0247] The terminal sends generated improvement suggestions and reports to the user. The user can review their work by referring to the provided reports and use them to improve their operations. This allows the user to autonomously practice efficient work methods and improve performance.

[0248] As a concrete example, consider a case where an employee's productivity is declining. The server can analyze the employee's past activity patterns and health information, and suggest that work interruptions or decreased communication frequency may be affecting productivity. Based on this analysis, the terminal provides the user with appropriate improvement suggestions as an action plan. Such suggestions may include ways to encourage more frequent communication and advice on health management.

[0249] As an example of a prompt using a generative AI model, the system provides a response when the user inputs "Please provide an analysis of this week's business performance and suggestions for improvement."

[0250] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0251] Step 1:

[0252] The server collects data on employee activity history, health status, and contact methods. Inputs include WiFi connection information, location data, and data from monitoring devices. This information is output in a format that the server stores in its database. Specifically, the server receives information in real time using a data collection module, organizes it in a specific format, and then stores it long-term.

[0253] Step 2:

[0254] The server uses the collected data to run machine learning algorithms and perform analysis. The database information collected in step 1 is used as input. The output generates a list of factors that affect performance and their weights. Specifically, the server uses anomaly detection algorithms to identify unusual patterns in the data and analyze their causes.

[0255] Step 3:

[0256] The server uses natural language generation technology to generate improvement suggestions based on the analysis results from Step 2. Analysis data is required as input. The output is improvement suggestions in natural language format that can be sent to the user. Specifically, the server utilizes a generation AI model to logically translate the analysis results into text and form suggestions tailored to individual employees.

[0257] Step 4:

[0258] The terminal receives suggestions from the server and distributes them to the user. The input is the improvement suggestions generated in step 3. The output is the improvement suggestion message presented to the user. Specifically, the terminal provides these messages to the user using its notification function and launches an interface where the user can review the content.

[0259] Step 5:

[0260] Users refer to suggestion messages provided from their terminals and create feedback on their own work activities. The input is improvement suggestion messages. The output is information supplied back to the system in the form of feedback. Specifically, users review their actions based on the suggestions and report the effects of those actions to the system sequentially.

[0261] Step 6:

[0262] The server analyzes the feedback provided by the user in step 5 and uses it to improve the machine learning algorithm. The input is the feedback data. The output is the algorithm model for generating updated suggestions. Specifically, the server continuously collects feedback data and tunes the model to improve the accuracy of the algorithm.

[0263] (Application Example 1)

[0264] 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."

[0265] For robots and automated machinery used in factories, effectively performing preventative maintenance while maintaining efficient work performance is crucial from the perspective of reducing operating costs and improving safety. However, conventional methods have made it difficult to accurately assess the operating status of these machines and propose necessary maintenance at the appropriate time.

[0266] 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.

[0267] In this invention, the server is an information processing device that manages a database equipped with means for accumulating and storing configuration information, and includes means for collecting information, means for analyzing the configuration information using a machine learning algorithm to identify elements that contribute to improving the efficiency of individual tasks, means for generating and outputting action suggestions from the analysis results using natural language generation technology, and means for analyzing the operating status using a machine learning algorithm to generate suggestions for efficiency improvements and preventive maintenance. This makes it possible to evaluate the operating status of robots and automated machines in real time and propose the optimal maintenance timing.

[0268] "Configuration information" refers to various attribute data and historical data related to individuals or machines, and is the information that serves as material for analysis based on this data.

[0269] An "information processing device" is a computer device used for collecting, storing, and analyzing information, and has the function of managing databases and performing necessary calculations.

[0270] A "machine learning algorithm" is a set of computational methods that have the ability to learn patterns and rules from data, and in this invention, it is used to identify elements that contribute to individual efficiency improvements.

[0271] "Natural language generation technology" is a technology that enables machines to create text in a natural language that humans can understand, and in this invention, it is used to create action suggestions from analysis results.

[0272] "Operating status" refers to information about the operating conditions of a machine or robot, and includes variables such as operating speed, temperature, and vibration.

[0273] "Preventive maintenance" refers to maintenance work performed regularly to prevent machine and system failures, and is a planned and systematic maintenance activity.

[0274] The system that realizes this invention consists of a server as an information processing device and a terminal used by the user. The server has a database for collecting and managing operational data of robots and automated machinery in the factory in real time, and analyzes this configuration information using machine learning algorithms.

[0275] The server uses Python as its programming language and performs analysis using TensorFlow and other machine learning libraries. Operating status data consists of sensor data such as vibration, temperature, and operating patterns. By inputting this data into machine learning algorithms, factors contributing to efficiency improvements and the need for proactive maintenance are identified.

[0276] Based on the analysis results, the server uses natural language generation technology to generate specific maintenance suggestions, such as "The robot arm needs an oil change in XX minutes." These suggestions are created based on the content input to the AI ​​model as prompts.

[0277] The terminal plays the role of presenting these suggestions to the user. The user receives the suggestions sent from the server via a device such as a smartphone or tablet. For example, the user can input a prompt such as "Please tell me the timing for preventative maintenance based on vibration sensor data" and then check the suggested content.

[0278] In this way, users can improve the efficiency of the machine, perform maintenance at the appropriate time, and contribute to maintaining the machine's long-term performance.

[0279] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0280] Step 1:

[0281] The server acquires the operation data of the robot in real time from various sensors in the factory. The input data is sensor data such as vibration, temperature, and operation patterns. These data are stored in a database in the server and prepared for analysis by machine learning algorithms.

[0282] Step 2:

[0283] The server inputs the stored operation data into a machine learning algorithm. This algorithm is implemented by TensorFlow, compares past performance data with current data, and extracts patterns related to efficiency improvement. As a result, it outputs predicted signs of deterioration and abnormalities.

[0284] Step 3:

[0285] Based on the analysis results obtained by the machine learning algorithm, the server uses natural language generation technology to generate specific maintenance proposals. Using the analysis results as input data, it outputs maintenance recommendations such as "The oil of the robot arm needs to be changed after [X] minutes" using a generative AI model.

[0286] Step 4:

[0287] The terminal receives the maintenance proposal sent from the server and notifies the user. The user can check the proposal through a smartphone or tablet. The user reviews the proposal content on the terminal and makes the necessary judgments to reflect it in the actual maintenance schedule.

[0288] Step 5:

[0289] Users take specific actions based on the suggestions and send feedback to the server via their devices. Based on the feedback as input data, the server updates its machine learning algorithm, performs data calculations to improve the accuracy of future suggestions, and builds a dataset for making more accurate suggestions in the future.

[0290] 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.

[0291] This invention provides a system that incorporates emotional data into configuration information to more effectively support employee work improvement. Its specific form is described below.

[0292] The server collects data on employees' work history, health status, and communication patterns, and uses an emotion engine to extract and collect emotional data from users' facial expressions and voice. This data is stored in a database and used to reflect the changes in users' work patterns and emotions.

[0293] Based on the collected data, the server uses machine learning algorithms to analyze factors and emotional tendencies that contribute to performance improvement. This analysis makes it possible to generate advice that takes into account the impact of emotions on work performance.

[0294] Based on the analysis results obtained, the server uses natural language generation technology to create specific action suggestions for improving work performance and career development for each employee. These suggestions include stress management methods tailored to emotions and communication improvement measures that take into account emotional states.

[0295] The terminal presents the user with action suggestions received from the server. Based on the suggestions received, the user can select and implement actions to improve their own work. The user can also provide feedback on each suggestion, which helps in further refinement of the system.

[0296] As a concrete example, consider a case where an employee has recently been experiencing stress. The server detects a high stress level through facial expression and voice analysis of the employee. Using this emotional data, the system aims to reduce the employee's mental burden by suggesting relaxation methods and the importance of taking regular rest periods.

[0297] The terminal displays these suggestions as notifications during employees' lunch breaks or after work, increasing the likelihood that users will take the suggested actions. In this way, the system, which incorporates emotional data, enables more personalized support, improving work efficiency and promoting overall employee well-being.

[0298] The following describes the processing flow.

[0299] Step 1:

[0300] The server collects work history, health status, WiFi and location information, and communication data obtained from monitoring devices. It also uses an emotion engine to analyze user facial expressions and voice data, and extracts emotional data. This data is then stored in a database.

[0301] Step 2:

[0302] The server preprocesses the collected data, removing noise and imputing missing values ​​to prepare a dataset suitable for analysis. During this process, it integrates sentiment data with other constituent information.

[0303] Step 3:

[0304] The server analyzes the integrated data using machine learning algorithms to reveal the performance and emotional trends for each user. Through this analysis, identify areas for business improvement and the impact of emotions on the business.

[0305] Step 4:

[0306] Based on the obtained analysis results, the server uses natural language generation technology to create individualized action proposals. The proposals include stress management and communication improvement measures according to the user's emotional state.

[0307] Step 5:

[0308] The terminal notifies the user of the action proposals received from the server. The notification is made through a chatbot function or a pop-up message to make it easily accessible to the user.

[0309] Step 6:

[0310] The user checks the proposals from the terminal and executes the accepted proposals. Also, feedback on the effectiveness of the proposals can be provided through the terminal.

[0311] Step 7:

[0312] The server collects feedback from the user and utilizes it for improving the machine learning algorithms and the emotion engine. This enables the system to generate more accurate proposals in subsequent times.

[0313] (Example 2)

[0314] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0315] In today's work environment, an individual's work style and health can directly impact their work performance. However, traditional methods have made it difficult to accurately understand the emotional and health states of individual employees and propose concrete improvement measures based on that understanding. As a result, employee health has not been optimally managed, leading to problems such as decreased work efficiency.

[0316] 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.

[0317] In this invention, the server is a data processing system that manages a storage device equipped with elements for accumulating and holding configuration information, and includes means for collecting information relating to work history, health status, communication patterns, and emotional state; means for analyzing the configuration information using an analysis device to identify elements and emotional tendencies that contribute to an individual's work direction; and means for generating and outputting individual action suggestions from the analysis results using a natural language generation device. This enables the automatic generation of appropriate improvement measures tailored to each employee's condition, thereby improving health management and work efficiency.

[0318] "Configuration information" is a general term for information in a data processing system that represents an individual's work history, health status, communication patterns, and emotional state.

[0319] A "storage device" refers to a physical or virtual device used to collect and store information, and is used to manage the collected configuration information.

[0320] A "data processing system" is a system that includes a set of devices or software for collecting and analyzing configuration information and generating information-based actions.

[0321] An "analysis device" is a device that uses configuration information to analyze data through machine learning algorithms and other means to identify factors and emotional tendencies that contribute to an individual's work style.

[0322] A "natural language generation device" is a device or software that automatically generates action suggestions tailored to individual situations based on analysis results, and outputs them as text.

[0323] A "display device" refers to a device that communicates proposals generated by a server to the user and presents them in an actionable format.

[0324] "Evaluation information" refers to information that shows feedback and implementation results regarding suggestions submitted by users, and is data that can be used to improve the system.

[0325] This invention is an information processing system designed to optimize employees' work performance and health. To implement this invention, it is necessary to combine various hardware and software components to collect and analyze various employee data and generate optimal action suggestions.

[0326] The server collects "configuration information" such as work history, health status, communication patterns, and emotional state. This process may utilize wearable devices, integrated sensors, and health management applications. Additionally, software that analyzes emotions from facial expressions and voice is used as an emotion engine. Specific examples include the Facial Emotion Recognition API and Speech Emotion Recognition Software.

[0327] The server stores the collected data in a "storage device," and then an "analytical device" analyzes the data. The analytical device uses machine learning algorithms to extract patterns from the data and identify individual work patterns and emotional tendencies. Libraries such as scikit-learn and TensorFlow are examples of such libraries.

[0328] Based on these results, the server uses a "natural language generator" to create specific action suggestions. In this process, OpenAI's GPT model and other tools are used to generate action suggestions for each employee. Examples of action suggestions include relaxation methods and recommendations for short breaks for employees experiencing high stress levels.

[0329] The generated action suggestions are delivered to employees as notifications via their devices. These notifications are presented in the form of email or on-screen messages and are delivered appropriately at a time that suits the user's situation. For example, when suggesting a break time during work, the suggestion might be presented before lunchtime.

[0330] A specific example of a prompt message would be, "Use employee work history and emotional data to generate specific suggestions for stress reduction."

[0331] Thus, the system of the present invention can improve work performance and maintain health by making work improvement suggestions that take into account changes in emotions.

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

[0333] Step 1:

[0334] The server collects configuration information for each employee, including their work history, health status, communication patterns, and emotional state. Input data includes data from wearable devices and health management applications. Based on this, it extracts emotional data from facial expressions and voice using an emotion engine (e.g., Facial Emotion Recognition API). The output is the integrated data stored in a database.

[0335] Step 2:

[0336] The server analyzes data using machine learning algorithms based on collected configuration information. Inputs include work history and sentiment data stored in a database. This data is then analyzed using analytical tools (e.g., TensorFlow) to identify factors and emotional tendencies that contribute to an individual's work style. The output provides insights into performance improvement based on the analysis results.

[0337] Step 3:

[0338] The server generates specific action suggestions using natural language generation technology based on the analysis results. The input includes insights gained from the analysis. A generative AI model (e.g., OpenAI's GPT model) is used to create optimal suggestions for each employee in written form. The output is a personalized suggestion text for each employee.

[0339] Step 4:

[0340] The terminal notifies the user of action suggestions sent from the server. The input is a suggestion text generated by the server. This is presented to the user via a display device, for example, as a pop-up message or email notification. The output allows the user to confirm the suggested action.

[0341] Step 5:

[0342] The user selects and implements actions based on the presented suggestions. The input is the suggestions presented from the terminal. The user considers the content and decides to take action to improve their own work. The output includes the results of the proposed actions and feedback.

[0343] Step 6:

[0344] The server collects user feedback and stores it back in the database. The input includes feedback information provided by users through their devices. This is used to improve the machine learning algorithm and enhance the accuracy of future suggestions. The output is improved accuracy in subsequent analyses and suggestion generation.

[0345] (Application Example 2)

[0346] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0347] In today's consumer environment, accurately understanding customers' emotional states and providing personalized service accordingly is a challenging task. Especially for large retail stores and companies with diverse customer bases, there is a need for customer service support that utilizes real-time emotion recognition to achieve both improved customer satisfaction and increased sales.

[0348] 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.

[0349] In this invention, the server is an information processing device that manages an aggregation device equipped with means for accumulating and holding configuration information, and includes means for collecting data, means for analyzing the configuration information and emotional data using a machine learning algorithm to identify elements that improve the service efficiency of individuals, and means for generating optimal service proposals based on emotional data and outputting them to the service provider's visual display device. This makes it possible to grasp the customer's emotional state in real time and quickly provide appropriate service proposals.

[0350] "Configuration information" refers to a collection of data or elements, and in particular, the information that constitutes the input data used within a system.

[0351] A "data aggregation device" is a device for comprehensively collecting and managing data, and functions as a database within an information processing device.

[0352] An "information processing device" is a device used for collecting, storing, and analyzing data, and is particularly responsible for managing configuration information.

[0353] "Emotional data" refers to data that indicates an individual's emotional state, and includes information extracted from facial expressions and voice.

[0354] A "machine learning algorithm" is a computational method used to learn patterns from data and perform predictions and classifications, and is used in the analysis of constituent information and sentiment data.

[0355] "Individual service efficiency" refers to the efficiency of performing tasks or services, and serves as an indicator of an individual's performance.

[0356] A "service provider" refers to someone who directly provides goods or services to customers and is responsible for customer service.

[0357] A "visual display device" is a device used to display information visually, and this includes, in particular, the displays of digital terminals and smart glasses.

[0358] To implement this invention, a server acts as the central point for information processing and suggestion generation. The server first uses a network-connected integrator to receive configuration information and emotion data from smart glasses. This integrator collects the customer's facial expressions and voice in real time and transmits them to the server as configuration information.

[0359] The server analyzes this configuration information and sentiment data using machine learning algorithms. This analysis identifies factors that contribute to improving the individual's service efficiency and prepares it to propose the most suitable service to the customer. The algorithms used here utilize TensorFlow and OpenCV for sentiment analysis.

[0360] Next, the server uses natural language generation technology to generate action suggestions based on the identified elements and outputs them to a visual display device. This visual display device is a smart glasses display, and the information is delivered directly to the service provider. In this process, the information is visualized quickly and efficiently, allowing the service provider to respond appropriately to the customer.

[0361] As a concrete example, consider a scenario where a family visits a store. The server, upon receiving emotion analysis indicating that a child has shown interest in a particular product, can notify the store clerk via smart glasses with a description of the product and related discount information.

[0362] This system will enable personalized service tailored to each customer, leading to improved customer satisfaction.

[0363] An example of a prompt message would be, "Identify the product category that the customer in front of me is interested in, and generate a customer service suggestion based on that." Sending this instruction to the server will generate an appropriate suggestion.

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] The server receives configuration information and emotion data in real time from the smart glasses. It also receives image and audio data acquired by the camera and microphone as input. This data serves as material for analyzing the customer's facial expressions and voice tone. Based on this, the server uses facial recognition software (e.g., OpenCV) to extract facial expression data and an audio analysis engine (e.g., Google Speech-to-Text API) to convert the audio data into text.

[0367] Step 2:

[0368] The server uses the received facial expression data and voice text to send emotion analysis prompts to a generative AI model to evaluate the customer's emotional state. The input includes the data obtained along with the prompt text, and the output is a classification result of the emotions the customer is exhibiting and their tendencies. This process is performed using analysis software (e.g., TensorFlow) on hardware running machine learning algorithms.

[0369] Step 3:

[0370] The server uses natural language generation technology to create service suggestions for customers based on the results of sentiment analysis. The input is data on sentiment classification and its fluctuations, and based on this, it infers what kind of service will increase customer interest and satisfaction. As output, specific suggestion sentences are generated. A natural language generation algorithm (e.g., GPT model) is used here.

[0371] Step 4:

[0372] The server sends the generated suggestion text to the smart glasses' visual display. This allows the service provider (store clerk) to instantly see the optimal action for the customer via the display. The input is the generated suggestion text, and the output is specific instructions displayed on the visual display. Based on this, the store clerk can respond quickly.

[0373] Step 5:

[0374] The user (store clerk) observes the results of their suggestions to customers and sends the responses to the server. The input is a record of the customer's response to the presented suggestions, and the output is feedback data stored on the server to improve the accuracy of future suggestions. This data is used to retrain the machine learning algorithm for future improvements.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] [Third Embodiment]

[0379] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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".

[0391] This invention is a system that provides personalized business improvement utilizing employee composition information. Its specific form is described below.

[0392] The server collects data including employees' work history, health status, and various communication patterns. This data is obtained from WiFi connection information, location information systems, and monitoring devices. It also collects data on employees' daily health status through facial expression analysis during their arrival and departure times.

[0393] The server stores this data in a database and analyzes it using machine learning algorithms. This analysis identifies individual employee work patterns and factors influencing their performance. Based on these results, natural language generation technology is used to create specific improvement measures and career development advice.

[0394] The terminal delivers generated reports to the user. Users can refer to the reports and advice provided by the terminal to improve their work and build their careers. This allows users to autonomously review their work methods and improve efficiency.

[0395] As a concrete example, consider a case where an employee's productivity is declining. The server analyzes this employee's past work patterns and health history, suggesting that a decrease in communication frequency or lack of sleep may be contributing factors.

[0396] Based on this analysis, the device will present employees with specific suggestions in a chatbot format regarding ways to improve communication and health management. This will allow users to review their own behavior and take appropriate corrective measures, enabling them to regain high performance.

[0397] The following describes the processing flow.

[0398] Step 1:

[0399] The server collects work history, health status, WiFi and location information, and communication pattern data obtained from monitoring devices, and stores it in a database.

[0400] Step 2:

[0401] The server preprocesses the collected data, imputing missing values ​​and removing noise to prepare an analyzable dataset.

[0402] Step 3:

[0403] The server analyzes pre-processed data using machine learning algorithms to identify the causes of performance degradation and areas for improvement for each employee.

[0404] Step 4:

[0405] Based on the analysis results, the server automatically generates specific action suggestions for individual work improvement and career development using natural language generation technology.

[0406] Step 5:

[0407] The terminal delivers the generated proposal report to the user and presents it to the user through notifications and chatbot functions.

[0408] Step 6:

[0409] The user reviews the suggestions from their device and either takes action on any suggestions they accept or sends feedback to their device.

[0410] Step 7:

[0411] The server collects user feedback and uses it to improve machine learning algorithms, making future suggestions even more accurate.

[0412] (Example 1)

[0413] 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."

[0414] In recent years, there has been a growing demand for systems that comprehensively manage employee work efficiency and health to improve performance. However, traditional systems have struggled to provide sufficiently personalized improvement measures to individual employees, resulting in a failure to contribute to overall organizational productivity improvement.

[0415] 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.

[0416] In this invention, the server is a data recording device equipped with means for collecting and storing employee activity history, health status, and contact methods, and includes means for collecting information, means for analyzing employee configuration information using machine learning methods to identify factors that contribute to improving individual work efficiency, and means for generating and distributing improvement suggestions based on the analysis results using natural language generation technology. This makes it possible to provide specific and effective work improvement measures tailored to each employee, promote improved employee performance, and improve the productivity of the entire organization.

[0417] "Means of collecting information" refers to data recording devices and related technologies necessary to collect and store information, including employee activity history, health status, and contact methods.

[0418] "Means of analysis using machine learning methods" refers to the process and algorithms of applying machine learning techniques to identify factors that contribute to performance improvement based on collected configuration information.

[0419] "A means of generating and distributing improvement suggestions using natural language generation technology" refers to a method of using natural language generation technology to generate specific business improvement suggestions based on analysis results and provide them to employees in a format suitable for them.

[0420] "Means of transmitting to users via a receiving device" refers to a communication method that uses an electronic receiving device to ensure that the generated improvement suggestions are reliably delivered to users.

[0421] "Means of obtaining responses" refers to the methods and processes for collecting feedback and reactions provided by users and incorporating them into the system.

[0422] "Methods for improving machine learning methods" refers to the process of optimizing machine learning algorithms and models based on collected responses in order to improve the accuracy of analysis and the effectiveness of proposed solutions.

[0423] This invention is a system that provides personalized business improvement utilizing employee configuration information. Its specific form is described below.

[0424] The server collects information using data recording devices that aggregate employee activity history, health status, and contact methods. Specifically, it tracks employees' movement patterns within the office using WiFi connection information and understands relationships between different departments using a location information system. In addition, monitoring devices analyze employees' facial expressions when they arrive at and leave work, and acquire data to evaluate their daily health status.

[0425] The server stores the collected data in a database. The stored data is analyzed by applying machine learning algorithms. Here, existing technologies are used to identify key factors related to improving employee performance. Based on the identified factors, natural language generation technology is used to generate specific work improvement measures and career development advice.

[0426] The terminal sends generated improvement suggestions and reports to the user. The user can review their work by referring to the provided reports and use them to improve their operations. This allows the user to autonomously practice efficient work methods and improve performance.

[0427] As a concrete example, consider a case where an employee's productivity is declining. The server can analyze the employee's past activity patterns and health information, and suggest that work interruptions or decreased communication frequency may be affecting productivity. Based on this analysis, the terminal provides the user with appropriate improvement suggestions as an action plan. Such suggestions may include ways to encourage more frequent communication and advice on health management.

[0428] As an example of a prompt using a generative AI model, the system provides a response when the user inputs "Please provide an analysis of this week's business performance and suggestions for improvement."

[0429] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0430] Step 1:

[0431] The server collects data on employee activity history, health status, and contact methods. Inputs include WiFi connection information, location data, and data from monitoring devices. This information is output in a format that the server stores in its database. Specifically, the server receives information in real time using a data collection module, organizes it in a specific format, and then stores it long-term.

[0432] Step 2:

[0433] The server uses the collected data to run machine learning algorithms and perform analysis. The database information collected in step 1 is used as input. The output generates a list of factors that affect performance and their weights. Specifically, the server uses anomaly detection algorithms to identify unusual patterns in the data and analyze their causes.

[0434] Step 3:

[0435] The server uses natural language generation technology to generate improvement suggestions based on the analysis results from Step 2. Analysis data is required as input. The output is improvement suggestions in natural language format that can be sent to the user. Specifically, the server utilizes a generation AI model to logically translate the analysis results into text and form suggestions tailored to individual employees.

[0436] Step 4:

[0437] The terminal receives suggestions from the server and distributes them to the user. The input is the improvement suggestions generated in step 3. The output is the improvement suggestion message presented to the user. Specifically, the terminal provides these messages to the user using its notification function and launches an interface where the user can review the content.

[0438] Step 5:

[0439] Users refer to suggestion messages provided from their terminals and create feedback on their own work activities. The input is improvement suggestion messages. The output is information supplied back to the system in the form of feedback. Specifically, users review their actions based on the suggestions and report the effects of those actions to the system sequentially.

[0440] Step 6:

[0441] The server analyzes the feedback provided by the user in step 5 and uses it to improve the machine learning algorithm. The input is the feedback data. The output is the algorithm model for generating updated suggestions. Specifically, the server continuously collects feedback data and tunes the model to improve the accuracy of the algorithm.

[0442] (Application Example 1)

[0443] 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."

[0444] For robots and automated machinery used in factories, effectively performing preventative maintenance while maintaining efficient work performance is crucial from the perspective of reducing operating costs and improving safety. However, conventional methods have made it difficult to accurately assess the operating status of these machines and propose necessary maintenance at the appropriate time.

[0445] 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.

[0446] In this invention, the server is an information processing device that manages a database equipped with means for accumulating and storing configuration information, and includes means for collecting information, means for analyzing the configuration information using a machine learning algorithm to identify elements that contribute to improving the efficiency of individual tasks, means for generating and outputting action suggestions from the analysis results using natural language generation technology, and means for analyzing the operating status using a machine learning algorithm to generate suggestions for efficiency improvements and preventive maintenance. This makes it possible to evaluate the operating status of robots and automated machines in real time and propose the optimal maintenance timing.

[0447] "Configuration information" refers to various attribute data and historical data related to individuals or machines, and is the information that serves as material for analysis based on this data.

[0448] An "information processing device" is a computer device used for collecting, storing, and analyzing information, and has the function of managing databases and performing necessary calculations.

[0449] A "machine learning algorithm" is a set of computational methods that have the ability to learn patterns and rules from data, and in this invention, it is used to identify elements that contribute to individual efficiency improvements.

[0450] "Natural language generation technology" is a technology that enables machines to create text in a natural language that humans can understand, and in this invention, it is used to create action suggestions from analysis results.

[0451] "Operating status" refers to information about the operating conditions of a machine or robot, and includes variables such as operating speed, temperature, and vibration.

[0452] "Preventive maintenance" refers to maintenance work performed regularly to prevent machine and system failures, and is a planned and systematic maintenance activity.

[0453] The system that realizes this invention consists of a server as an information processing device and a terminal used by the user. The server has a database for collecting and managing operational data of robots and automated machinery in the factory in real time, and analyzes this configuration information using machine learning algorithms.

[0454] The server uses Python as its programming language and performs analysis using TensorFlow and other machine learning libraries. Operating status data consists of sensor data such as vibration, temperature, and operating patterns. By inputting this data into machine learning algorithms, factors contributing to efficiency improvements and the need for proactive maintenance are identified.

[0455] Based on the analysis results, the server uses natural language generation technology to generate specific maintenance suggestions, such as "The robot arm needs an oil change in XX minutes." These suggestions are created based on the content input to the AI ​​model as prompts.

[0456] The terminal plays the role of presenting these suggestions to the user. The user receives the suggestions sent from the server via a device such as a smartphone or tablet. For example, the user can input a prompt such as "Please tell me the timing for preventative maintenance based on vibration sensor data" and then check the suggested content.

[0457] In this way, users can improve the efficiency of the machine, perform maintenance at the appropriate time, and contribute to maintaining the machine's long-term performance.

[0458] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0459] Step 1:

[0460] The server acquires real-time robot operation data from various sensors within the factory. The input data includes sensor data such as vibration, temperature, and motion patterns. This data is stored in a database on the server, preparing it for analysis using machine learning algorithms.

[0461] Step 2:

[0462] The server inputs stored operational data into a machine learning algorithm. This algorithm, implemented using TensorFlow, compares past performance data with current data to extract patterns related to efficiency improvements. As a result, it outputs predicted signs of degradation or anomalies.

[0463] Step 3:

[0464] The server uses natural language generation technology to generate specific maintenance suggestions based on analysis results obtained by machine learning algorithms. Using the analysis results as input data, the generating AI model outputs maintenance recommendations such as "The robot arm needs an oil change in XX minutes."

[0465] Step 4:

[0466] The terminal receives maintenance proposals sent from the server and notifies the user. The user can view the proposals via their smartphone or tablet. The user reviews the proposals on the terminal and makes the necessary decisions to reflect them in the actual maintenance schedule.

[0467] Step 5:

[0468] Users take specific actions based on the suggestions and send feedback to the server via their devices. Based on the feedback as input data, the server updates its machine learning algorithm, performs data calculations to improve the accuracy of future suggestions, and builds a dataset for making more accurate suggestions in the future.

[0469] 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.

[0470] This invention provides a system that incorporates emotional data into configuration information to more effectively support employee work improvement. Its specific form is described below.

[0471] The server collects data on employees' work history, health status, and communication patterns, and uses an emotion engine to extract and collect emotional data from users' facial expressions and voice. This data is stored in a database and used to reflect the changes in users' work patterns and emotions.

[0472] Based on the collected data, the server uses machine learning algorithms to analyze factors and emotional tendencies that contribute to performance improvement. This analysis makes it possible to generate advice that takes into account the impact of emotions on work performance.

[0473] Based on the analysis results obtained, the server uses natural language generation technology to create specific action suggestions for improving work performance and career development for each employee. These suggestions include stress management methods tailored to emotions and communication improvement measures that take into account emotional states.

[0474] The terminal presents the user with action suggestions received from the server. Based on the suggestions received, the user can select and implement actions to improve their own work. The user can also provide feedback on each suggestion, which helps in further refinement of the system.

[0475] As a concrete example, consider a case where an employee has recently been experiencing stress. The server detects a high stress level through facial expression and voice analysis of the employee. Using this emotional data, the system aims to reduce the employee's mental burden by suggesting relaxation methods and the importance of taking regular rest periods.

[0476] The terminal displays these suggestions as notifications during employees' lunch breaks or after work, increasing the likelihood that users will take the suggested actions. In this way, the system, which incorporates emotional data, enables more personalized support, improving work efficiency and promoting overall employee well-being.

[0477] The following describes the processing flow.

[0478] Step 1:

[0479] The server collects work history, health status, WiFi and location information, and communication data obtained from monitoring devices. It also uses an emotion engine to analyze user facial expressions and voice data, and extracts emotional data. This data is then stored in a database.

[0480] Step 2:

[0481] The server preprocesses the collected data, removing noise and imputing missing values ​​to prepare a dataset suitable for analysis. During this process, it integrates sentiment data with other constituent information.

[0482] Step 3:

[0483] The server uses machine learning algorithms to analyze integrated data and reveal individual user performance and emotional tendencies. This analysis identifies areas for improvement in operations and the impact of emotions on work processes.

[0484] Step 4:

[0485] Based on the analysis results obtained, the server uses natural language generation technology to create personalized action suggestions. These suggestions include stress management and communication improvement measures tailored to the user's emotional state.

[0486] Step 5:

[0487] The device notifies the user of action suggestions received from the server. These notifications are delivered via chatbot functionality or pop-up messages, making them easily accessible to the user.

[0488] Step 6:

[0489] Users can review suggestions from their devices and implement those they accept. They can also provide feedback on the effectiveness of suggestions through their devices.

[0490] Step 7:

[0491] The server collects user feedback and uses it to improve machine learning algorithms and sentiment engines. This allows the system to generate more accurate suggestions in the future.

[0492] (Example 2)

[0493] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0494] In today's work environment, an individual's work style and health can directly impact their work performance. However, traditional methods have made it difficult to accurately understand the emotional and health states of individual employees and propose concrete improvement measures based on that understanding. As a result, employee health has not been optimally managed, leading to problems such as decreased work efficiency.

[0495] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0496] In this invention, the server is a data processing system that manages a storage device equipped with elements for accumulating and holding configuration information, and includes means for collecting information relating to work history, health status, communication patterns, and emotional state; means for analyzing the configuration information using an analysis device to identify elements and emotional tendencies that contribute to an individual's work direction; and means for generating and outputting individual action suggestions from the analysis results using a natural language generation device. This enables the automatic generation of appropriate improvement measures tailored to each employee's condition, thereby improving health management and work efficiency.

[0497] "Configuration information" is a general term for information in a data processing system that represents an individual's work history, health status, communication patterns, and emotional state.

[0498] A "storage device" refers to a physical or virtual device used to collect and store information, and is used to manage the collected configuration information.

[0499] A "data processing system" is a system that includes a set of devices or software for collecting and analyzing configuration information and generating information-based actions.

[0500] An "analysis device" is a device that uses configuration information to analyze data through machine learning algorithms and other means to identify factors and emotional tendencies that contribute to an individual's work style.

[0501] A "natural language generation device" is a device or software that automatically generates action suggestions tailored to individual situations based on analysis results, and outputs them as text.

[0502] A "display device" refers to a device that communicates proposals generated by a server to the user and presents them in an actionable format.

[0503] "Evaluation information" refers to information that shows feedback and implementation results regarding suggestions submitted by users, and is data that can be used to improve the system.

[0504] This invention is an information processing system designed to optimize employees' work performance and health. To implement this invention, it is necessary to combine various hardware and software components to collect and analyze various employee data and generate optimal action suggestions.

[0505] The server collects "configuration information" such as work history, health status, communication patterns, and emotional state. This process may utilize wearable devices, integrated sensors, and health management applications. Additionally, software that analyzes emotions from facial expressions and voice is used as an emotion engine. Specific examples include the Facial Emotion Recognition API and Speech Emotion Recognition Software.

[0506] The server stores the collected data in a "storage device," and then an "analytical device" analyzes the data. The analytical device uses machine learning algorithms to extract patterns from the data and identify individual work patterns and emotional tendencies. Libraries such as scikit-learn and TensorFlow are examples of such libraries.

[0507] Based on these results, the server uses a "natural language generator" to create specific action suggestions. In this process, OpenAI's GPT model and other tools are used to generate action suggestions for each employee. Examples of action suggestions include relaxation methods and recommendations for short breaks for employees experiencing high stress levels.

[0508] The generated action suggestions are delivered to employees as notifications via their devices. These notifications are presented in the form of email or on-screen messages and are delivered appropriately at a time that suits the user's situation. For example, when suggesting a break time during work, the suggestion might be presented before lunchtime.

[0509] A specific example of a prompt message would be, "Use employee work history and emotional data to generate specific suggestions for stress reduction."

[0510] Thus, the system of the present invention can improve work performance and maintain health by making work improvement suggestions that take into account changes in emotions.

[0511] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0512] Step 1:

[0513] The server collects configuration information for each employee, including their work history, health status, communication patterns, and emotional state. Input data includes data from wearable devices and health management applications. Based on this, it extracts emotional data from facial expressions and voice using an emotion engine (e.g., Facial Emotion Recognition API). The output is the integrated data stored in a database.

[0514] Step 2:

[0515] The server analyzes data using machine learning algorithms based on collected configuration information. Inputs include work history and sentiment data stored in a database. This data is then analyzed using analytical tools (e.g., TensorFlow) to identify factors and emotional tendencies that contribute to an individual's work style. The output provides insights into performance improvement based on the analysis results.

[0516] Step 3:

[0517] The server generates specific action suggestions using natural language generation technology based on the analysis results. The input includes insights gained from the analysis. A generative AI model (e.g., OpenAI's GPT model) is used to create optimal suggestions for each employee in written form. The output is a personalized suggestion text for each employee.

[0518] Step 4:

[0519] The terminal notifies the user of action suggestions sent from the server. The input is a suggestion text generated by the server. This is presented to the user via a display device, for example, as a pop-up message or email notification. The output allows the user to confirm the suggested action.

[0520] Step 5:

[0521] The user selects and implements actions based on the presented suggestions. The input is the suggestions presented from the terminal. The user considers the content and decides to take action to improve their own work. The output includes the results of the proposed actions and feedback.

[0522] Step 6:

[0523] The server collects user feedback and stores it back in the database. The input includes feedback information provided by users through their devices. This is used to improve the machine learning algorithm and enhance the accuracy of future suggestions. The output is improved accuracy in subsequent analyses and suggestion generation.

[0524] (Application Example 2)

[0525] 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."

[0526] In today's consumer environment, accurately understanding customers' emotional states and providing personalized service accordingly is a challenging task. Especially for large retail stores and companies with diverse customer bases, there is a need for customer service support that utilizes real-time emotion recognition to achieve both improved customer satisfaction and increased sales.

[0527] 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.

[0528] In this invention, the server is an information processing device that manages an aggregation device equipped with means for accumulating and holding configuration information, and includes means for collecting data, means for analyzing the configuration information and emotional data using a machine learning algorithm to identify elements that improve the service efficiency of individuals, and means for generating optimal service proposals based on emotional data and outputting them to the service provider's visual display device. This makes it possible to grasp the customer's emotional state in real time and quickly provide appropriate service proposals.

[0529] "Configuration information" refers to a collection of data or elements, and in particular, the information that constitutes the input data used within a system.

[0530] A "data aggregation device" is a device for comprehensively collecting and managing data, and functions as a database within an information processing device.

[0531] An "information processing device" is a device used for collecting, storing, and analyzing data, and is particularly responsible for managing configuration information.

[0532] "Emotional data" refers to data that indicates an individual's emotional state, and includes information extracted from facial expressions and voice.

[0533] A "machine learning algorithm" is a computational method used to learn patterns from data and perform predictions and classifications, and is used in the analysis of constituent information and sentiment data.

[0534] "Individual service efficiency" refers to the efficiency of performing tasks or services, and serves as an indicator of an individual's performance.

[0535] A "service provider" refers to someone who directly provides goods or services to customers and is responsible for customer service.

[0536] A "visual display device" is a device used to display information visually, and this includes, in particular, the displays of digital terminals and smart glasses.

[0537] To implement this invention, a server acts as the central point for information processing and suggestion generation. The server first uses a network-connected integrator to receive configuration information and emotion data from smart glasses. This integrator collects the customer's facial expressions and voice in real time and transmits them to the server as configuration information.

[0538] The server analyzes this configuration information and sentiment data using machine learning algorithms. This analysis identifies factors that contribute to improving the individual's service efficiency and prepares it to propose the most suitable service to the customer. The algorithms used here utilize TensorFlow and OpenCV for sentiment analysis.

[0539] Next, the server uses natural language generation technology to generate action suggestions based on the identified elements and outputs them to a visual display device. This visual display device is a smart glasses display, and the information is delivered directly to the service provider. In this process, the information is visualized quickly and efficiently, allowing the service provider to respond appropriately to the customer.

[0540] As a concrete example, consider a scenario where a family visits a store. The server, upon receiving emotion analysis indicating that a child has shown interest in a particular product, can notify the store clerk via smart glasses with a description of the product and related discount information.

[0541] This system will enable personalized service tailored to each customer, leading to improved customer satisfaction.

[0542] An example of a prompt message would be, "Identify the product category that the customer in front of me is interested in, and generate a customer service suggestion based on that." Sending this instruction to the server will generate an appropriate suggestion.

[0543] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0544] Step 1:

[0545] The server receives configuration information and emotion data in real time from the smart glasses. It also receives image and audio data acquired by the camera and microphone as input. This data serves as material for analyzing the customer's facial expressions and voice tone. Based on this, the server uses facial recognition software (e.g., OpenCV) to extract facial expression data and an audio analysis engine (e.g., Google Speech-to-Text API) to convert the audio data into text.

[0546] Step 2:

[0547] The server uses the received facial expression data and voice text to send emotion analysis prompts to a generative AI model to evaluate the customer's emotional state. The input includes the data obtained along with the prompt text, and the output is a classification result of the emotions the customer is exhibiting and their tendencies. This process is performed using analysis software (e.g., TensorFlow) on hardware running machine learning algorithms.

[0548] Step 3:

[0549] The server uses natural language generation technology to create service suggestions for customers based on the results of sentiment analysis. The input is data on sentiment classification and its fluctuations, and based on this, it infers what kind of service will increase customer interest and satisfaction. As output, specific suggestion sentences are generated. A natural language generation algorithm (e.g., GPT model) is used here.

[0550] Step 4:

[0551] The server sends the generated suggestion text to the smart glasses' visual display. This allows the service provider (store clerk) to instantly see the optimal action for the customer via the display. The input is the generated suggestion text, and the output is specific instructions displayed on the visual display. Based on this, the store clerk can respond quickly.

[0552] Step 5:

[0553] The user (store clerk) observes the results of their suggestions to customers and sends the responses to the server. The input is a record of the customer's response to the presented suggestions, and the output is feedback data stored on the server to improve the accuracy of future suggestions. This data is used to retrain the machine learning algorithm for future improvements.

[0554] 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.

[0555] 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.

[0556] 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.

[0557] [Fourth Embodiment]

[0558] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0559] 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.

[0560] 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).

[0561] 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.

[0562] 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.

[0563] 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).

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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".

[0571] This invention is a system that provides personalized business improvement utilizing employee composition information. Its specific form is described below.

[0572] The server collects data including employees' work history, health status, and various communication patterns. This data is obtained from WiFi connection information, location information systems, and monitoring devices. It also collects data on employees' daily health status through facial expression analysis during their arrival and departure times.

[0573] The server stores this data in a database and analyzes it using machine learning algorithms. This analysis identifies individual employee work patterns and factors influencing their performance. Based on these results, natural language generation technology is used to create specific improvement measures and career development advice.

[0574] The terminal delivers generated reports to the user. Users can refer to the reports and advice provided by the terminal to improve their work and build their careers. This allows users to autonomously review their work methods and improve efficiency.

[0575] As a concrete example, consider a case where an employee's productivity is declining. The server analyzes this employee's past work patterns and health history, suggesting that a decrease in communication frequency or lack of sleep may be contributing factors.

[0576] Based on this analysis, the device will present employees with specific suggestions in a chatbot format regarding ways to improve communication and health management. This will allow users to review their own behavior and take appropriate corrective measures, enabling them to regain high performance.

[0577] The following describes the processing flow.

[0578] Step 1:

[0579] The server collects work history, health status, WiFi and location information, and communication pattern data obtained from monitoring devices, and stores it in a database.

[0580] Step 2:

[0581] The server preprocesses the collected data, imputing missing values ​​and removing noise to prepare an analyzable dataset.

[0582] Step 3:

[0583] The server analyzes pre-processed data using machine learning algorithms to identify the causes of performance degradation and areas for improvement for each employee.

[0584] Step 4:

[0585] Based on the analysis results, the server automatically generates specific action suggestions for individual work improvement and career development using natural language generation technology.

[0586] Step 5:

[0587] The terminal delivers the generated proposal report to the user and presents it to the user through notifications and chatbot functions.

[0588] Step 6:

[0589] The user reviews the suggestions from their device and either takes action on any suggestions they accept or sends feedback to their device.

[0590] Step 7:

[0591] The server collects user feedback and uses it to improve machine learning algorithms, making future suggestions even more accurate.

[0592] (Example 1)

[0593] 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".

[0594] In recent years, there has been a growing demand for systems that comprehensively manage employee work efficiency and health to improve performance. However, traditional systems have struggled to provide sufficiently personalized improvement measures to individual employees, resulting in a failure to contribute to overall organizational productivity improvement.

[0595] 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.

[0596] In this invention, the server is a data recording device equipped with means for collecting and storing employee activity history, health status, and contact methods, and includes means for collecting information, means for analyzing employee configuration information using machine learning methods to identify factors that contribute to improving individual work efficiency, and means for generating and distributing improvement suggestions based on the analysis results using natural language generation technology. This makes it possible to provide specific and effective work improvement measures tailored to each employee, promote improved employee performance, and improve the productivity of the entire organization.

[0597] "Means of collecting information" refers to data recording devices and related technologies necessary to collect and store information, including employee activity history, health status, and contact methods.

[0598] "Means of analysis using machine learning methods" refers to the process and algorithms of applying machine learning techniques to identify factors that contribute to performance improvement based on collected configuration information.

[0599] "A means of generating and distributing improvement suggestions using natural language generation technology" refers to a method of using natural language generation technology to generate specific business improvement suggestions based on analysis results and provide them to employees in a format suitable for them.

[0600] "Means of transmitting to users via a receiving device" refers to a communication method that uses an electronic receiving device to ensure that the generated improvement suggestions are reliably delivered to users.

[0601] "Means of obtaining responses" refers to the methods and processes for collecting feedback and reactions provided by users and incorporating them into the system.

[0602] "Methods for improving machine learning methods" refers to the process of optimizing machine learning algorithms and models based on collected responses in order to improve the accuracy of analysis and the effectiveness of proposed solutions.

[0603] This invention is a system that provides personalized business improvement utilizing employee configuration information. Its specific form is described below.

[0604] The server collects information using data recording devices that aggregate employee activity history, health status, and contact methods. Specifically, it tracks employees' movement patterns within the office using WiFi connection information and understands relationships between different departments using a location information system. In addition, monitoring devices analyze employees' facial expressions when they arrive at and leave work, and acquire data to evaluate their daily health status.

[0605] The server stores the collected data in a database. The stored data is analyzed by applying machine learning algorithms. Here, existing technologies are used to identify key factors related to improving employee performance. Based on the identified factors, natural language generation technology is used to generate specific work improvement measures and career development advice.

[0606] The terminal sends generated improvement suggestions and reports to the user. The user can review their work by referring to the provided reports and use them to improve their operations. This allows the user to autonomously practice efficient work methods and improve performance.

[0607] As a concrete example, consider a case where an employee's productivity is declining. The server can analyze the employee's past activity patterns and health information, and suggest that work interruptions or decreased communication frequency may be affecting productivity. Based on this analysis, the terminal provides the user with appropriate improvement suggestions as an action plan. Such suggestions may include ways to encourage more frequent communication and advice on health management.

[0608] As an example of a prompt using a generative AI model, the system provides a response when the user inputs "Please provide an analysis of this week's business performance and suggestions for improvement."

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

[0610] Step 1:

[0611] The server collects data on employee activity history, health status, and contact methods. Inputs include WiFi connection information, location data, and data from monitoring devices. This information is output in a format that the server stores in its database. Specifically, the server receives information in real time using a data collection module, organizes it in a specific format, and then stores it long-term.

[0612] Step 2:

[0613] The server uses the collected data to run machine learning algorithms and perform analysis. The database information collected in step 1 is used as input. The output generates a list of factors that affect performance and their weights. Specifically, the server uses anomaly detection algorithms to identify unusual patterns in the data and analyze their causes.

[0614] Step 3:

[0615] The server uses natural language generation technology to generate improvement suggestions based on the analysis results from Step 2. Analysis data is required as input. The output is improvement suggestions in natural language format that can be sent to the user. Specifically, the server utilizes a generation AI model to logically translate the analysis results into text and form suggestions tailored to individual employees.

[0616] Step 4:

[0617] The terminal receives suggestions from the server and distributes them to the user. The input is the improvement suggestions generated in step 3. The output is the improvement suggestion message presented to the user. Specifically, the terminal provides these messages to the user using its notification function and launches an interface where the user can review the content.

[0618] Step 5:

[0619] Users refer to suggestion messages provided from their terminals and create feedback on their own work activities. The input is improvement suggestion messages. The output is information supplied back to the system in the form of feedback. Specifically, users review their actions based on the suggestions and report the effects of those actions to the system sequentially.

[0620] Step 6:

[0621] The server analyzes the feedback provided by the user in step 5 and uses it to improve the machine learning algorithm. The input is the feedback data. The output is the algorithm model for generating updated suggestions. Specifically, the server continuously collects feedback data and tunes the model to improve the accuracy of the algorithm.

[0622] (Application Example 1)

[0623] 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".

[0624] For robots and automated machinery used in factories, effectively performing preventative maintenance while maintaining efficient work performance is crucial from the perspective of reducing operating costs and improving safety. However, conventional methods have made it difficult to accurately assess the operating status of these machines and propose necessary maintenance at the appropriate time.

[0625] 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.

[0626] In this invention, the server is an information processing device that manages a database equipped with means for accumulating and storing configuration information, and includes means for collecting information, means for analyzing the configuration information using a machine learning algorithm to identify elements that contribute to improving the efficiency of individual tasks, means for generating and outputting action suggestions from the analysis results using natural language generation technology, and means for analyzing the operating status using a machine learning algorithm to generate suggestions for efficiency improvements and preventive maintenance. This makes it possible to evaluate the operating status of robots and automated machines in real time and propose the optimal maintenance timing.

[0627] "Configuration information" refers to various attribute data and historical data related to individuals or machines, and is the information that serves as material for analysis based on this data.

[0628] An "information processing device" is a computer device used for collecting, storing, and analyzing information, and has the function of managing databases and performing necessary calculations.

[0629] A "machine learning algorithm" is a set of computational methods that have the ability to learn patterns and rules from data, and in this invention, it is used to identify elements that contribute to individual efficiency improvements.

[0630] "Natural language generation technology" is a technology that enables machines to create text in a natural language that humans can understand, and in this invention, it is used to create action suggestions from analysis results.

[0631] "Operating status" refers to information about the operating conditions of a machine or robot, and includes variables such as operating speed, temperature, and vibration.

[0632] "Preventive maintenance" refers to maintenance work performed regularly to prevent machine and system failures, and is a planned and systematic maintenance activity.

[0633] The system that realizes this invention consists of a server as an information processing device and a terminal used by the user. The server has a database for collecting and managing operational data of robots and automated machinery in the factory in real time, and analyzes this configuration information using machine learning algorithms.

[0634] The server uses Python as its programming language and performs analysis using TensorFlow and other machine learning libraries. Operating status data consists of sensor data such as vibration, temperature, and operating patterns. By inputting this data into machine learning algorithms, factors contributing to efficiency improvements and the need for proactive maintenance are identified.

[0635] Based on the analysis results, the server uses natural language generation technology to generate specific maintenance suggestions, such as "The robot arm needs an oil change in XX minutes." These suggestions are created based on the content input to the AI ​​model as prompts.

[0636] The terminal plays the role of presenting these suggestions to the user. The user receives the suggestions sent from the server via a device such as a smartphone or tablet. For example, the user can input a prompt such as "Please tell me the timing for preventative maintenance based on vibration sensor data" and then check the suggested content.

[0637] In this way, users can improve the efficiency of the machine, perform maintenance at the appropriate time, and contribute to maintaining the machine's long-term performance.

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

[0639] Step 1:

[0640] The server acquires real-time robot operation data from various sensors within the factory. The input data includes sensor data such as vibration, temperature, and motion patterns. This data is stored in a database on the server, preparing it for analysis using machine learning algorithms.

[0641] Step 2:

[0642] The server inputs stored operational data into a machine learning algorithm. This algorithm, implemented using TensorFlow, compares past performance data with current data to extract patterns related to efficiency improvements. As a result, it outputs predicted signs of degradation or anomalies.

[0643] Step 3:

[0644] The server uses natural language generation technology to generate specific maintenance suggestions based on analysis results obtained by machine learning algorithms. Using the analysis results as input data, the generating AI model outputs maintenance recommendations such as "The robot arm needs an oil change in XX minutes."

[0645] Step 4:

[0646] The terminal receives maintenance proposals sent from the server and notifies the user. The user can view the proposals via their smartphone or tablet. The user reviews the proposals on the terminal and makes the necessary decisions to reflect them in the actual maintenance schedule.

[0647] Step 5:

[0648] Users take specific actions based on the suggestions and send feedback to the server via their devices. Based on the feedback as input data, the server updates its machine learning algorithm, performs data calculations to improve the accuracy of future suggestions, and builds a dataset for making more accurate suggestions in the future.

[0649] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0650] This invention provides a system that incorporates emotional data into configuration information to more effectively support employee work improvement. Its specific form is described below.

[0651] The server collects data on employees' work history, health status, and communication patterns, and uses an emotion engine to extract and collect emotional data from users' facial expressions and voice. This data is stored in a database and used to reflect the changes in users' work patterns and emotions.

[0652] Based on the collected data, the server uses machine learning algorithms to analyze factors and emotional tendencies that contribute to performance improvement. This analysis makes it possible to generate advice that takes into account the impact of emotions on work performance.

[0653] Based on the analysis results obtained, the server uses natural language generation technology to create specific action suggestions for improving work performance and career development for each employee. These suggestions include stress management methods tailored to emotions and communication improvement measures that take into account emotional states.

[0654] The terminal presents the user with action suggestions received from the server. Based on the suggestions received, the user can select and implement actions to improve their own work. The user can also provide feedback on each suggestion, which helps in further refinement of the system.

[0655] As a concrete example, consider a case where an employee has recently been experiencing stress. The server detects a high stress level through facial expression and voice analysis of the employee. Using this emotional data, the system aims to reduce the employee's mental burden by suggesting relaxation methods and the importance of taking regular rest periods.

[0656] The terminal displays these suggestions as notifications during employees' lunch breaks or after work, increasing the likelihood that users will take the suggested actions. In this way, the system, which incorporates emotional data, enables more personalized support, improving work efficiency and promoting overall employee well-being.

[0657] The following describes the processing flow.

[0658] Step 1:

[0659] The server collects work history, health status, WiFi and location information, and communication data obtained from monitoring devices. It also uses an emotion engine to analyze user facial expressions and voice data, and extracts emotional data. This data is then stored in a database.

[0660] Step 2:

[0661] The server preprocesses the collected data, removing noise and imputing missing values ​​to prepare a dataset suitable for analysis. During this process, it integrates sentiment data with other constituent information.

[0662] Step 3:

[0663] The server uses machine learning algorithms to analyze integrated data and reveal individual user performance and emotional tendencies. This analysis identifies areas for improvement in operations and the impact of emotions on work processes.

[0664] Step 4:

[0665] Based on the analysis results obtained, the server uses natural language generation technology to create personalized action suggestions. These suggestions include stress management and communication improvement measures tailored to the user's emotional state.

[0666] Step 5:

[0667] The device notifies the user of action suggestions received from the server. These notifications are delivered via chatbot functionality or pop-up messages, making them easily accessible to the user.

[0668] Step 6:

[0669] Users can review suggestions from their devices and implement those they accept. They can also provide feedback on the effectiveness of suggestions through their devices.

[0670] Step 7:

[0671] The server collects user feedback and uses it to improve machine learning algorithms and sentiment engines. This allows the system to generate more accurate suggestions in the future.

[0672] (Example 2)

[0673] 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".

[0674] In today's work environment, an individual's work style and health can directly impact their work performance. However, traditional methods have made it difficult to accurately understand the emotional and health states of individual employees and propose concrete improvement measures based on that understanding. As a result, employee health has not been optimally managed, leading to problems such as decreased work efficiency.

[0675] 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.

[0676] In this invention, the server is a data processing system that manages a storage device equipped with elements for accumulating and holding configuration information, and includes means for collecting information relating to work history, health status, communication patterns, and emotional state; means for analyzing the configuration information using an analysis device to identify elements and emotional tendencies that contribute to an individual's work direction; and means for generating and outputting individual action suggestions from the analysis results using a natural language generation device. This enables the automatic generation of appropriate improvement measures tailored to each employee's condition, thereby improving health management and work efficiency.

[0677] "Configuration information" is a general term for information in a data processing system that represents an individual's work history, health status, communication patterns, and emotional state.

[0678] A "storage device" refers to a physical or virtual device used to collect and store information, and is used to manage the collected configuration information.

[0679] A "data processing system" is a system that includes a set of devices or software for collecting and analyzing configuration information and generating information-based actions.

[0680] An "analysis device" is a device that uses configuration information to analyze data through machine learning algorithms and other means to identify factors and emotional tendencies that contribute to an individual's work style.

[0681] A "natural language generation device" is a device or software that automatically generates action suggestions tailored to individual situations based on analysis results, and outputs them as text.

[0682] A "display device" refers to a device that communicates proposals generated by a server to the user and presents them in an actionable format.

[0683] "Evaluation information" refers to information that shows feedback and implementation results regarding suggestions submitted by users, and is data that can be used to improve the system.

[0684] This invention is an information processing system designed to optimize employees' work performance and health. To implement this invention, it is necessary to combine various hardware and software components to collect and analyze various employee data and generate optimal action suggestions.

[0685] The server collects "configuration information" such as work history, health status, communication patterns, and emotional state. This process may utilize wearable devices, integrated sensors, and health management applications. Additionally, software that analyzes emotions from facial expressions and voice is used as an emotion engine. Specific examples include the Facial Emotion Recognition API and Speech Emotion Recognition Software.

[0686] The server stores the collected data in a "storage device," and then an "analytical device" analyzes the data. The analytical device uses machine learning algorithms to extract patterns from the data and identify individual work patterns and emotional tendencies. Libraries such as scikit-learn and TensorFlow are examples of such libraries.

[0687] Based on these results, the server uses a "natural language generator" to create specific action suggestions. In this process, OpenAI's GPT model and other tools are used to generate action suggestions for each employee. Examples of action suggestions include relaxation methods and recommendations for short breaks for employees experiencing high stress levels.

[0688] The generated action suggestions are delivered to employees as notifications via their devices. These notifications are presented in the form of email or on-screen messages and are delivered appropriately at a time that suits the user's situation. For example, when suggesting a break time during work, the suggestion might be presented before lunchtime.

[0689] A specific example of a prompt message would be, "Use employee work history and emotional data to generate specific suggestions for stress reduction."

[0690] Thus, the system of the present invention can improve work performance and maintain health by making work improvement suggestions that take into account changes in emotions.

[0691] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0692] Step 1:

[0693] The server collects configuration information for each employee, including their work history, health status, communication patterns, and emotional state. Input data includes data from wearable devices and health management applications. Based on this, it extracts emotional data from facial expressions and voice using an emotion engine (e.g., Facial Emotion Recognition API). The output is the integrated data stored in a database.

[0694] Step 2:

[0695] The server analyzes data using machine learning algorithms based on collected configuration information. Inputs include work history and sentiment data stored in a database. This data is then analyzed using analytical tools (e.g., TensorFlow) to identify factors and emotional tendencies that contribute to an individual's work style. The output provides insights into performance improvement based on the analysis results.

[0696] Step 3:

[0697] The server generates specific action suggestions using natural language generation technology based on the analysis results. The input includes insights gained from the analysis. A generative AI model (e.g., OpenAI's GPT model) is used to create optimal suggestions for each employee in written form. The output is a personalized suggestion text for each employee.

[0698] Step 4:

[0699] The terminal notifies the user of action suggestions sent from the server. The input is a suggestion text generated by the server. This is presented to the user via a display device, for example, as a pop-up message or email notification. The output allows the user to confirm the suggested action.

[0700] Step 5:

[0701] The user selects and implements actions based on the presented suggestions. The input is the suggestions presented from the terminal. The user considers the content and decides to take action to improve their own work. The output includes the results of the proposed actions and feedback.

[0702] Step 6:

[0703] The server collects user feedback and stores it back in the database. The input includes feedback information provided by users through their devices. This is used to improve the machine learning algorithm and enhance the accuracy of future suggestions. The output is improved accuracy in subsequent analyses and suggestion generation.

[0704] (Application Example 2)

[0705] 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".

[0706] In today's consumer environment, accurately understanding customers' emotional states and providing personalized service accordingly is a challenging task. Especially for large retail stores and companies with diverse customer bases, there is a need for customer service support that utilizes real-time emotion recognition to achieve both improved customer satisfaction and increased sales.

[0707] 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.

[0708] In this invention, the server is an information processing device that manages an aggregation device equipped with means for accumulating and holding configuration information, and includes means for collecting data, means for analyzing the configuration information and emotional data using a machine learning algorithm to identify elements that improve the service efficiency of individuals, and means for generating optimal service proposals based on emotional data and outputting them to the service provider's visual display device. This makes it possible to grasp the customer's emotional state in real time and quickly provide appropriate service proposals.

[0709] "Configuration information" refers to a collection of data or elements, and in particular, the information that constitutes the input data used within a system.

[0710] A "data aggregation device" is a device for comprehensively collecting and managing data, and functions as a database within an information processing device.

[0711] An "information processing device" is a device used for collecting, storing, and analyzing data, and is particularly responsible for managing configuration information.

[0712] "Emotional data" refers to data that indicates an individual's emotional state, and includes information extracted from facial expressions and voice.

[0713] A "machine learning algorithm" is a computational method used to learn patterns from data and perform predictions and classifications, and is used in the analysis of constituent information and sentiment data.

[0714] "Individual service efficiency" refers to the efficiency of performing tasks or services, and serves as an indicator of an individual's performance.

[0715] A "service provider" refers to someone who directly provides goods or services to customers and is responsible for customer service.

[0716] A "visual display device" is a device used to display information visually, and this includes, in particular, the displays of digital terminals and smart glasses.

[0717] To implement this invention, a server acts as the central point for information processing and suggestion generation. The server first uses a network-connected integrator to receive configuration information and emotion data from smart glasses. This integrator collects the customer's facial expressions and voice in real time and transmits them to the server as configuration information.

[0718] The server analyzes this configuration information and sentiment data using machine learning algorithms. This analysis identifies factors that contribute to improving the individual's service efficiency and prepares it to propose the most suitable service to the customer. The algorithms used here utilize TensorFlow and OpenCV for sentiment analysis.

[0719] Next, the server uses natural language generation technology to generate action suggestions based on the identified elements and outputs them to a visual display device. This visual display device is a smart glasses display, and the information is delivered directly to the service provider. In this process, the information is visualized quickly and efficiently, allowing the service provider to respond appropriately to the customer.

[0720] As a concrete example, consider a scenario where a family visits a store. The server, upon receiving emotion analysis indicating that a child has shown interest in a particular product, can notify the store clerk via smart glasses with a description of the product and related discount information.

[0721] This system will enable personalized service tailored to each customer, leading to improved customer satisfaction.

[0722] An example of a prompt message would be, "Identify the product category that the customer in front of me is interested in, and generate a customer service suggestion based on that." Sending this instruction to the server will generate an appropriate suggestion.

[0723] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0724] Step 1:

[0725] The server receives configuration information and emotion data in real time from the smart glasses. It also receives image and audio data acquired by the camera and microphone as input. This data serves as material for analyzing the customer's facial expressions and voice tone. Based on this, the server uses facial recognition software (e.g., OpenCV) to extract facial expression data and an audio analysis engine (e.g., Google Speech-to-Text API) to convert the audio data into text.

[0726] Step 2:

[0727] The server uses the received facial expression data and voice text to send emotion analysis prompts to a generative AI model to evaluate the customer's emotional state. The input includes the data obtained along with the prompt text, and the output is a classification result of the emotions the customer is exhibiting and their tendencies. This process is performed using analysis software (e.g., TensorFlow) on hardware running machine learning algorithms.

[0728] Step 3:

[0729] The server uses natural language generation technology to create service suggestions for customers based on the results of sentiment analysis. The input is data on sentiment classification and its fluctuations, and based on this, it infers what kind of service will increase customer interest and satisfaction. As output, specific suggestion sentences are generated. A natural language generation algorithm (e.g., GPT model) is used here.

[0730] Step 4:

[0731] The server sends the generated suggestion text to the smart glasses' visual display. This allows the service provider (store clerk) to instantly see the optimal action for the customer via the display. The input is the generated suggestion text, and the output is specific instructions displayed on the visual display. Based on this, the store clerk can respond quickly.

[0732] Step 5:

[0733] The user (store clerk) observes the results of their suggestions to customers and sends the responses to the server. The input is a record of the customer's response to the presented suggestions, and the output is feedback data stored on the server to improve the accuracy of future suggestions. This data is used to retrain the machine learning algorithm for future improvements.

[0734] 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.

[0735] 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.

[0736] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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."

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0755] The following is further disclosed regarding the embodiments described above.

[0756] (Claim 1)

[0757] In an information processing device that manages a database equipped with means for accumulating and storing configuration information, the means for collecting information and

[0758] A means of analyzing configuration information using machine learning algorithms to identify elements that contribute to improving individual performance,

[0759] A means of generating and outputting action suggestions from analysis results using natural language generation technology,

[0760] A system that includes this.

[0761] (Claim 2)

[0762] The system according to claim 1, further comprising means for transmitting a proposed action to a user via a receiving device and obtaining feedback.

[0763] (Claim 3)

[0764] The system according to claim 1, further comprising means for improving the machine learning algorithm based on collected feedback and improving the accuracy of the proposed content.

[0765] "Example 1"

[0766] (Claim 1)

[0767] A data recording device equipped with means for collecting and storing employee activity history, health status, and contact methods, comprising means for collecting information,

[0768] A means of analyzing employee composition information using machine learning methods to identify factors that contribute to improving the efficiency of individual tasks,

[0769] A means for generating and distributing improvement suggestions based on analysis results using natural language generation technology,

[0770] A means for transmitting the proposed action plan to the user via a display device,

[0771] A means of analyzing location information and changes in contact frequency obtained from user activities, and updating suggestions for continuously improving efficiency,

[0772] A system that includes this.

[0773] (Claim 2)

[0774] The system according to claim 1, further comprising means for transmitting a proposed action to a user via a receiving device and obtaining a response.

[0775] (Claim 3)

[0776] The system according to claim 1, further comprising means for improving the machine learning method based on collected responses and increasing the accuracy of the proposed content.

[0777] "Application Example 1"

[0778] (Claim 1)

[0779] In an information processing device that manages a database equipped with means for accumulating and storing configuration information, the means for collecting information and

[0780] A means of analyzing configuration information using machine learning algorithms to identify elements that contribute to improving the efficiency of individual tasks,

[0781] A means of generating and outputting action suggestions from analysis results using natural language generation technology,

[0782] A means for analyzing operating conditions using machine learning algorithms and generating suggestions for efficiency improvements and preventative maintenance,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, further comprising means for transmitting a proposed action to a user via a receiving device and obtaining a response.

[0786] (Claim 3)

[0787] The system according to claim 1, further comprising means for improving the accuracy of the proposed content by improving the machine learning algorithm based on the collected responses.

[0788] "Example 2 of combining an emotion engine"

[0789] (Claim 1)

[0790] In a data processing system that manages a storage device equipped with elements for accumulating and holding configuration information, means for collecting information relating to work history, health status, communication patterns and emotional state,

[0791] A means for analyzing the aforementioned configuration information using an analysis device to identify elements and emotional tendencies that contribute to an individual's work direction,

[0792] A means for generating and outputting individual action suggestions from analysis results using a natural language generation device,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, further comprising means for transmitting a proposed action to a person via a display device and obtaining evaluation information.

[0796] (Claim 3)

[0797] The system according to claim 1, further comprising means for improving the accuracy of the proposed content by improving the analytical device based on the collected evaluation information.

[0798] "Application example 2 when combining with an emotional engine"

[0799] (Claim 1)

[0800] An information processing device for managing an integrating device equipped with means for accumulating and holding configuration information, comprising means for collecting data,

[0801] A means of analyzing configuration information and emotional data using machine learning algorithms to identify elements that improve the service efficiency of individuals,

[0802] A means for generating optimal service suggestions based on emotional data and outputting them to the service provider's visual display device,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, further comprising means for transmitting proposed actions to a user via a receiving terminal and obtaining opinion information.

[0806] (Claim 3)

[0807] The system according to claim 1, further comprising means for improving the accuracy of the proposed content by improving the machine learning algorithm based on the collected opinion information. [Explanation of Symbols]

[0808] 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. In an information processing device that manages a database equipped with means for accumulating and storing configuration information, the means for collecting information and A means of analyzing configuration information using machine learning algorithms to identify elements that contribute to improving individual performance, A means of generating and outputting action suggestions from analysis results using natural language generation technology, A system that includes this.

2. The system according to claim 1, further comprising means for transmitting a proposed action to a user via a receiving device and obtaining feedback.

3. The system according to claim 1, further comprising means for improving the machine learning algorithm based on collected feedback and improving the accuracy of the proposed content.

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