system

Abstract

Provide a system. 【Solution means】 Means for collecting various information data regarding individuals within an organization, Means for integrating the information data and analyzing it based on machine learning algorithms, Means for proposing an individual placement strategy and cultivation strategy based on the analysis results, Means for transmitting the proposal to an information terminal for display, Means for recollecting the execution results of the proposal and feeding back to the next analysis, Means for proposing a device for improving the efficiency of the manufacturing process and an operator placement strategy, Means for collecting and analyzing the working conditions in the manufacturing process in real time, A system including.

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, the method including 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 as a 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] Modern organizations are facing issues such as a declining birthrate and aging population, as well as the complexity of business operations, thus effective human resource management is required. In particular, it is important to utilize diverse information to assign the most suitable roles to individual persons, but this requires a lot of resources and time. Also, it is difficult to accurately grasp an individual's skills and psychological state and make strategic decisions based on that. Furthermore, it is not easy to obtain feedback in real time and reflect it in the next strategy.

Means for Solving the Problems

[0005] This invention provides a system that utilizes machine learning algorithms to collect, integrate, and analyze diverse data about individuals within an organization. Based on the analysis results, this system proposes optimal placement and training strategies for each individual and displays them on a communication terminal. Furthermore, by collecting feedback on the results of implementing the proposals, the accuracy of subsequent proposals is improved. This enables efficient and flexible human resource management.

[0006] "Within an organization" refers to the internal workings of a group, such as a company or organization, that is formed with a specific purpose.

[0007] "Diverse information data about an individual" refers to a wide range of information, including an individual's skills, qualifications, work history, personality traits, usage history, etc.

[0008] "Integration" refers to the process of gathering and compiling collected data in a consistent manner.

[0009] A "machine learning algorithm" refers to a set of techniques for automatically training computers to discover patterns.

[0010] "Analysis" refers to the process of examining data in detail to understand its structure and characteristics.

[0011] "Placement strategy" refers to the plan and methods for placing individuals within an organization in the most optimal positions and roles.

[0012] A "development strategy" refers to a plan of education and support aimed at improving an individual's abilities and skills.

[0013] A "communication terminal" refers to a device that has the ability to send and receive information. Examples include smartphones and computers.

[0014] "Feedback" refers to information collected—reactions and opinions—to actions and results, and used for evaluation and improvement. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention provides a system that collects diverse information data about individuals within an organization, analyzes it, and proposes optimal personnel placement and development strategies. This system is implemented as follows:

[0037] First, the server connects with various databases within the organization to collect diverse information, including individuals' skills, qualifications, work history, self-reported information, and psychological state. This data collection is performed using APIs and other methods from existing systems and platforms.

[0038] Next, the server integrates the collected data and converts it into a consistent format. This prepares the data for subsequent analysis. The integrated dataset is stored in a database on the server.

[0039] Subsequently, the server uses machine learning algorithms to analyze the integrated data. The purpose of the analysis is to evaluate individuals' skill strengths, career tendencies, and psychological states, and to develop talent placement strategies tailored to the organization's needs. It is also possible to extract psychological tendencies from self-reported data using natural language processing technology.

[0040] Based on the analysis results, the server generates suggestions for optimal placement and training strategies. These suggestions are delivered to the user (HR personnel) via a communication terminal. HR personnel can then use the suggested strategies to make specific placement decisions and implement training plans.

[0041] After execution, the server collects data again on the results of placement and training, and uses that feedback to inform future proposals. For example, to address a lack of programming skills in a project team, the AI ​​analyzes the data and suggests the most suitable transfer candidates from among existing employees. In this way, efficient personnel allocation and resource optimization can be achieved.

[0042] This system leverages AI technology to comprehensively and efficiently optimize human resources strategies in order to address challenges faced by organizations, such as talent shortages and increasing operational complexity.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server first connects to the organization's database and human resources information system, and retrieves personal information via APIs. This information includes an individual's skills, qualifications, work history, and self-reported data. It also retrieves PC usage history and various log data, which are anonymized according to security standards.

[0046] Step 2:

[0047] The server converts the acquired information into a unified format and generates an analyzable dataset. During this process, it cross-references data from different sources to verify data consistency and removes duplicate data.

[0048] Step 3:

[0049] The server applies machine learning algorithms using organized datasets. Clustering algorithms and regression analysis are performed to analyze individual skills and career tendencies. In addition, natural language processing is used to perform sentiment analysis on self-reported data and assess psychological state.

[0050] Step 4:

[0051] The server generates suggestions for personnel placement and training strategies based on the analysis results. This includes matching individual skills, assessing position suitability, and reassessing roles within teams. The suggestions are visually displayed to the user in a dashboard format.

[0052] Step 5:

[0053] The terminal notifies HR personnel of the generated proposals and makes them able to download a detailed report of the proposals. HR personnel can review the proposed placements and training plans and make modifications as needed.

[0054] Step 6:

[0055] The user (HR representative) makes the final placement decision based on the information provided by the server. They communicate transfer orders and training instructions to the target individuals and provide specific instructions via communication terminals so that they are reflected in the system.

[0056] Step 7:

[0057] The server collects the results data of the implemented HR measures and stores it in a database for future analysis. This data is used to evaluate performance indicators and verify the effectiveness of proposals. The recollected data facilitates the streamlining of future decision-making processes.

[0058] (Example 1)

[0059] 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."

[0060] Effectively leveraging individual characteristics within companies and organizations to develop optimal talent allocation and training strategies is complex and requires the collection and analysis of appropriate data. However, existing systems often fail to smoothly manage the entire process from data collection to analysis and feedback, making efficient talent management difficult. A more comprehensive and rapid solution is needed to address these problems.

[0061] 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.

[0062] In this invention, the server includes means for collecting diverse information about individuals belonging to an organization, means for integrating the information, converting it into a unified format, and analyzing it using machine learning processing, and means for generating and proposing strategies for the placement and development of individuals based on the analysis results. This makes it possible to efficiently and effectively place and develop personnel within the organization.

[0063] An "organization" is a group of individuals formed to achieve a specific purpose, and includes companies and other organizations.

[0064] An "individual" refers to a single member of an organization, and is the subject of information collection and analysis.

[0065] "Information" refers to data about an individual, such as their abilities, qualifications, and history, and is the subject of collection and integration.

[0066] "Integration" refers to the process of standardizing diverse forms of information and converting them into a single, unified format.

[0067] "Machine learning processing" refers to techniques that automatically learn from data and perform identification and prediction, and are used in analysis.

[0068] "Analysis" refers to the process of extracting meaningful information from collected data and evaluating the characteristics and tendencies of individuals.

[0069] "Proposal" refers to the placement and training strategies generated based on the analysis results, and is transmitted to the display device.

[0070] A "display device" refers to a device that receives proposal content and displays it visually to the user.

[0071] "Feedback" refers to the process of collecting data again based on the results of implemented suggestions and using it for the next analysis.

[0072] This invention provides a specific system for collecting diverse information about individuals belonging to an organization and for forming personnel allocation and training strategies. Embodiments thereof are shown below.

[0073] The server connects to various databases within the organization and collects information on individual capabilities, qualifications, and history using APIs. This includes data management systems and evaluation platforms. The server centralizes the collected information and converts it into a unified format, preparing it for subsequent machine learning processing.

[0074] The server uses the prepared data and applies machine learning algorithms to perform analysis. This analysis utilizes clustering and natural language processing techniques to evaluate the psychological tendencies and career path tendencies of individuals. In this process, the server uses a generative AI model to generate optimal placement and training strategies.

[0075] The generated proposals are sent from the server to the terminal and visually reviewed by the HR personnel who are the users. Based on the provided strategies, users can then implement specific personnel placement and training plans. For example, if a project requires specific technical skills, the server can propose appropriate personnel placement plans, enabling a rapid response.

[0076] The results after execution are collected again as data by the server and used as feedback for the next analysis. This allows the system to be continuously improved and generate more refined suggestions.

[0077] An example of a prompt message to be input to a generative AI model is: "Please suggest the optimal personnel allocation to contribute to the success of a certain project."

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

[0079] Step 1:

[0080] The server collects information about individuals from various databases within the organization using APIs. The specific inputs include data on each individual's abilities, qualifications, and history, which the server centrally manages. This collected data is then used in subsequent analysis processes.

[0081] Step 2:

[0082] The server integrates the collected data and converts it to a standard format. Here, data from different formats is made consistent, missing values ​​are imputed, and duplicate data is removed. The input is the diverse data formats collected in Step 1, and the output is a unified format dataset. This consistent dataset is used in the next analysis step.

[0083] Step 3:

[0084] The server applies machine learning algorithms to a dataset in a unified format and performs analysis. In this step, clustering and natural language processing techniques are used to analyze individual characteristics and psychological tendencies based on the input data. The output of the analysis is a specific evaluation result that includes individual strengths and strategies suitable for placement.

[0085] Step 4:

[0086] The server uses a generated AI model based on the analysis results to produce appropriate placement and training strategies. The input is the analysis results obtained in step 3, and the output is a specific personnel placement strategy and training plan. The generated proposals are sent to the next notification step.

[0087] Step 5:

[0088] The server sends the generated proposal to the terminal, notifying the HR representative (the user). The terminal displays the strategy in a visually easy-to-understand format. The input is the generated proposal, and the output is the visual display received by the user. The user reviews the proposal on the terminal and decides whether to implement it.

[0089] Step 6:

[0090] The server collects the results of the proposed implementations and stores them as feedback data to be used in subsequent analyses. The input is the result of the implemented personnel allocation and training, and the output is feedback data for the next analysis. This feedback allows the server's analysis model to be continuously improved.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] In production sites such as factories, there is a need to improve work efficiency by achieving the optimal allocation of personnel and equipment. However, conventional methods make it difficult to grasp the work situation in real time and to propose flexible allocations based on individual skills and psychological states, hindering the realization of efficient production activities.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for collecting information about individuals within an organization, means for integrating and analyzing the information, and means for proposing a placement strategy to improve the efficiency of the manufacturing process. This makes it possible to grasp the work status in real time and make appropriate placement suggestions that take into account individual skills and psychological states.

[0096] "Information about individuals within an organization" includes data such as an individual's skills, qualifications, operational history, and operator feedback.

[0097] "Means for integrating and analyzing information" refers to systems and processes that convert diverse collected data into a consistent format and perform analysis using machine learning algorithms.

[0098] "Means for proposing deployment strategies" refer to systems and processes that propose optimal personnel and equipment deployments based on analysis results, with the aim of improving work efficiency.

[0099] "Real-time monitoring of work status" means instantly monitoring and recording the current progress and workload of work on the production floor using sensors and data collection systems.

[0100] "Appropriate placement proposals that take into account individual skills and psychological state" means analyzing each worker's abilities, past work history, and current psychological state, and then providing a placement plan that maximizes productivity based on that analysis.

[0101] To implement this invention, a system in which a server plays a central role will be constructed. The server will interact with various databases within the organization to collect information data, including individual skills, qualifications, operation history, and operator feedback. Specifically, it will connect to IoT sensors placed within the factory and existing databases to collect data in real time. This data collection will be performed using data communication means such as APIs.

[0102] The collected data is integrated into a consistent format on the server and analyzed using machine learning algorithms. For this analysis, database management is performed using the Django framework with Python. A machine learning model utilizing scikit-learn is used to propose optimal placement strategies for individual workers and equipment. Additionally, spaCy is introduced as a natural language processing technique to evaluate operator feedback and psychological state.

[0103] Based on the analysis results, the server proposes the optimal layout and work strategy and sends it to an information terminal. This communication terminal could be a smartphone or tablet, which factory managers and operators would use to receive the proposal.

[0104] As a concrete example, consider a situation where work efficiency is declining on a factory line. In this case, the generative AI model evaluates equipment operation data and worker feedback, and proposes to deploy highly skilled workers as a countermeasure. This proposal is immediately delivered to the factory manager as a smartphone notification, enabling rapid decision-making.

[0105] Example prompt: "Based on recent integrated data, please provide the optimal operator placement plan to improve operational efficiency within the factory."

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

[0107] Step 1:

[0108] The server collects individual skills, qualifications, operational history, and work status data from IoT sensors within the factory and existing databases. Inputs include real-time sensor data and historical data, and output is the generation of an integrated dataset of this information. The data is retrieved using APIs, undergoes initial processing, and is converted into a well-formatted form.

[0109] Step 2:

[0110] The server integrates the collected data into a consistent format and then stores it in a database using the Django framework. By accepting a processed dataset as input and saving it to the database as output, the information becomes easily accessible, preparing it for subsequent analysis.

[0111] Step 3:

[0112] The server uses scikit-learn to apply machine learning models to and analyze integrated data in a database. The input is a dataset obtained from the database, and the output is the analysis results. This process models individual skill tendencies and psychological states, and develops placement strategies based on these evaluations.

[0113] Step 4:

[0114] The server generates optimal personnel and equipment allocation strategies based on the analysis results. The input is the evaluation results from machine learning, and the output is an allocation proposal. This proposal is generated as a prompt message and provided as information for decision-making.

[0115] Step 5:

[0116] The server sends the generated proposals to information terminals, where they are displayed on the smartphones and tablets of factory managers and operators. The input is a placement proposal, and the output is a notification sent to the user's terminal. This step allows for timely decisions regarding placement changes and support assignments.

[0117] Step 6:

[0118] Users issue specific work instructions and implement deployment strategies based on suggestions displayed on their terminals. Input is notification information from the terminal, and output is reflected on-site in the form of work instruction execution. This result is then fed back into subsequent data collection and used for analysis in the future.

[0119] 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.

[0120] This invention combines an emotion engine with an AI-powered optimization system for human resource management to provide richer HR strategies. This system collects diverse information data about individuals within an organization, integrates and analyzes it, and proposes placement strategies and training plans. Furthermore, the emotion engine recognizes the user's emotional state in real time and utilizes this information for analysis.

[0121] The server first retrieves information such as an individual's skills, qualifications, and operational history from various databases maintained by the organization. It also uses an emotion engine to collect user voice and facial expression data, analyzing this data to determine the user's current emotions. Machine learning is employed for emotion recognition, analyzing voice tone and facial expression patterns to determine emotions such as stress, joy, and confusion.

[0122] All collected data is integrated by a server and sent to an analysis module. Here, machine learning algorithms are applied to generate optimal placement and development strategies for each individual. In addition to individual data, emotional data is incorporated to evaluate how psychological state affects optimal work performance and reflect this in the strategy.

[0123] The generated suggestions are sent to a communication terminal, and users can review them through the system interface. For example, a team leader can receive system suggestions and make decisions to streamline the allocation of team members to a particular project. If members are not sufficiently refreshed, the suggestions may include adjusting their workload to match their mental state.

[0124] The terminal recollects data from the execution results of the proposal and sends it to the server as feedback for the next analysis. This feedback process allows the system to improve the accuracy and effectiveness of its proposals over time.

[0125] In this way, the present invention expands the dimensions of human resource management within organizations by utilizing an emotion engine in addition to conventional information data, thereby enabling the construction of more adaptable and dynamic human resource strategies.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The server accesses various databases within the organization to collect data such as individual skills, qualifications, and operational history. It uses APIs and data interfaces to accurately retrieve the necessary data in real time.

[0129] Step 2:

[0130] The server uses an emotion engine to collect emotional data in real time from the user's voice and facial expressions. Voice data is acquired through recording devices, and facial expression data is captured by cameras and image analysis tools. This data is used to instantly determine the user's psychological state.

[0131] Step 3:

[0132] The server integrates all collected data and stores it appropriately in the database. To maintain data consistency, it standardizes and organizes information from different data sources.

[0133] Step 4:

[0134] The server operates machine learning algorithms and analyzes integrated data. By incorporating emotional data in addition to individual skill sets and qualifications, it generates personalized placement and development strategies. The algorithms evaluate stress levels and motivation from the emotional data and optimize the recommendations.

[0135] Step 5:

[0136] The terminal notifies the user of generated deployment and training strategy proposals. The information is presented to the user in a visually easy-to-understand dashboard format. The user can then review the proposals in detail and develop their next action plan.

[0137] Step 6:

[0138] The user (HR representative) reviews the server's proposal and provides specific instructions as needed, such as adjusting workloads or rearranging team assignments. They utilize the system's feedback function to track the results of their decisions and adapt as necessary.

[0139] Step 7:

[0140] The server collects feedback data after execution and uses it to improve the accuracy of future suggestions. The feedback includes productivity metrics after reassignment and results from employee satisfaction surveys, which are used to measure the overall effectiveness of the system and serve as a basis for future improvements.

[0141] (Example 2)

[0142] 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".

[0143] To achieve effective human resource management within an organization, it is necessary to accurately understand the psychological state of individual members, in addition to their abilities and qualifications, and to reflect this in their job assignments and training plans. However, conventional systems have a problem in that they do not adequately consider such psychological states in human resource management.

[0144] 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.

[0145] In this invention, the server includes means for acquiring diverse attribute data about members of an organization, means for integrating the attribute data and analyzing it based on machine learning technology, and means for providing member placement plans and training plans based on the analysis results. This makes it possible to propose human resource strategies that take into account not only the members' skills and usage history, but also their psychological state.

[0146] An "organization" is a group of people who share a common purpose and function based on a certain structure and division of roles.

[0147] "Members" refer to individuals or groups who belong to an organization and contribute to achieving its objectives.

[0148] "Attribute data" refers to data that includes information such as the abilities, qualifications, and history of the members.

[0149] "Machine learning technology" refers to techniques that analyze large amounts of data, extract patterns from it, and use that information for predictions and decision-making.

[0150] "Analysis" refers to the process of processing collected data using statistical methods and algorithms to obtain meaningful information.

[0151] A "staffing plan" is a plan that determines how to allocate members within an organization.

[0152] A "development plan" is a plan for a series of activities aimed at improving the skills and abilities of its members.

[0153] An "information processing device" refers to an electronic device used for inputting, processing, storing, and outputting information.

[0154] "Feedback" refers to information returned to the system, which is used to improve operations and for future analyses.

[0155] This invention is a system that utilizes diverse attribute data of its members to achieve personnel management that takes psychological state into consideration. This system is mainly implemented through the process of collecting, analyzing, proposing, and providing feedback on various data between the server, terminals, and users.

[0156] The server first retrieves attribute data such as members' skills, qualifications, and usage history from the organization's database. This process utilizes SQL database management systems and specific data retrieval APIs. It also uses an emotion engine to collect user voice and facial expression data, and uses machine learning techniques to determine their psychological state based on this data. Speech recognition (e.g., speech processing APIs) and image processing (e.g., OpenCV) are used for analysis.

[0157] All acquired data is integrated and processed by machine learning algorithms on the server. During this process, generative AI models are used to derive appropriate work assignments and training plans for each member. This analysis is expected to utilize libraries such as Scikit-learn and TENSORFLOW®.

[0158] The generated work assignment and training plan proposals are sent to the terminal and reviewed by the user through the interface. For example, a team leader can use the proposed HR strategy to select the most suitable members for a project and improve work efficiency.

[0159] Furthermore, the results of the proposal execution are collected on the terminal, and this data is sent back to the server to provide feedback for the next proposal. This feedback allows for continuous improvement in the accuracy and reliability of the proposals.

[0160] A specific example of a prompt message might be, "Create an optimal workload adjustment plan based on the member's past operational data and current emotional state." This allows the server to generate suggestions that reflect the current psychological state.

[0161] By implementing this system, organizations can expect to achieve flexible and adaptable talent management that takes psychological states into account, thereby maximizing the performance of their members.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The server connects to the organization's database and retrieves attribute data such as members' skills, qualifications, and usage history. The input data is extracted using SQL queries, and the output is a dataset organized by member. This dataset will serve as the basis for subsequent integrated analysis.

[0165] Step 2:

[0166] The server uses an emotion engine to collect user voice and facial expression data in real time. This process uses a microphone to record voice and a camera to capture facial expressions. Voice signals and image data are collected as input, and this data is analyzed by an emotion recognition algorithm as output. Specifically, a voice recognition API is used for voice tone analysis, and an image processing library is used for facial expression analysis.

[0167] Step 3:

[0168] The server integrates acquired attribute and sentiment data and applies machine learning algorithms. A unified dataset of member data and sentiments is used as input, and optimal placement and training plans for each member are generated as output. Data processing primarily utilizes libraries such as TensorFlow and Scikit-learn, and generative AI models are used to derive strategies.

[0169] Step 4:

[0170] The generated work assignments and training plans are sent to the terminal, where the user reviews them through the system interface. The generated plans are the input, and the plans are displayed on the user's screen as output. Based on the suggestions, the user can make decisions that meet the organization's needs.

[0171] Step 5:

[0172] The terminal recollects the results of the proposed execution and sends the data to the server. User feedback data is collected as input, and the output becomes analysis data that will be used next. Based on this feedback, the system aims to improve the accuracy of the next proposal. In this process, data trends are identified and categorized, and the content of the proposal is improved.

[0173] (Application Example 2)

[0174] 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".

[0175] While streamlining personnel allocation and development within an organization is crucial for improving corporate productivity, conventional technologies have made it difficult to implement optimal allocation strategies that take into account individual psychological states and workloads. Furthermore, the lack of mechanisms to dynamically adjust tasks in conjunction with automated machinery based on these strategies has led to a growing demand for further efficiency improvements.

[0176] 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.

[0177] In this invention, the server includes means for collecting diverse informational and emotional data about individuals within an organization, means for integrating the informational and emotional data and analyzing it based on a machine learning algorithm, and means for proposing individual placement and development strategies based on the analysis results and reflecting these proposals in automated work machines to adjust tasks. This enables optimal work placement and efficient task shifting based on each individual's skills and psychological state.

[0178] "Diverse information data about individuals within an organization" refers to an individual's skills, qualifications, work history, and other work-related information necessary for evaluation.

[0179] "Emotional data" refers to information about an individual's psychological state obtained by analyzing their facial expressions and voice data.

[0180] A "machine learning algorithm" refers to a program or method used to analyze large amounts of data, find patterns, and make predictions or decisions.

[0181] "Placement strategy" refers to a plan for assigning personnel within an organization to the most suitable teams and projects.

[0182] A "development strategy" refers to a plan for improving an individual's skills and abilities.

[0183] "Automated work machines" refer to mechanical devices used in factories and organizations to perform tasks and operations through automated processes.

[0184] "Task shifting" refers to reassigning some or all of a task or operation to another worker or machine.

[0185] The system that implements this application example is server-centered, collecting individual user information data and emotional data, and performing integrated analysis. The server retrieves user skills, qualifications, and operation history from a database as information data, and collects facial expression data and voice data using a camera and microphone as emotional data. This data is analyzed in real time using Microsoft® Azure® Face API and Google® Cloud Speech-to-Text API.

[0186] The server uses machine learning libraries such as TensorFlow and PyTorch to analyze the collected data and evaluate the user's current psychological state. Based on this evaluation, it dynamically adjusts task assignments to automated machines to ensure that workers receive appropriate instructions. The generated suggestions are sent to a communication terminal, which the user can review through the system interface.

[0187] For example, if a worker in a factory shows signs of fatigue, this system can immediately detect this condition and instruct automated machinery to shift the workload. This reduces the burden on the worker and improves productivity.

[0188] Examples of prompts for a generative AI model:

[0189] "Analyze the worker's real-time facial expression and voice data to assess their current psychological state. Based on the assessment results, propose how to adjust their work tasks."

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

[0191] Step 1:

[0192] The server retrieves information data about users within the organization. As input, it collects user skills, qualifications, and operational history from a database to construct the information data. As output, it obtains a user information dataset necessary for analysis. This step verifies the completeness and accuracy of the data.

[0193] Step 2:

[0194] The server uses a camera and microphone to collect user emotion data. It takes real-time facial expression and voice data as input. The output provides data for analyzing the user's current psychological state. Here, Microsoft Azure's Face API and Google Cloud's Speech-to-Text API are used to analyze emotions.

[0195] Step 3:

[0196] The server analyzes collected informational and emotional data using machine learning algorithms. The input is the dataset obtained in steps 1 and 2. The output is the user's psychological state evaluation results and, based on these, suggestions for placement and training strategies. This step involves complex data analysis using TensorFlow or PyTorch.

[0197] Step 4:

[0198] The server adjusts task assignments to automated machines based on the analysis results. It uses the evaluation results and suggestions from the previous step as input. The output is an adjusted task instruction designed to reduce the workload on workers. Specifically, it changes the proportion of tasks assigned to the robots, taking into account the current workload of the workers.

[0199] Step 5:

[0200] The server sends the generated proposals and task instructions to the communication terminal. It provides the information obtained in step 4 as input. The information is delivered as output in a format that the user can view through the system interface. This step ensures accurate and rapid information transmission.

[0201] Step 6:

[0202] The terminal and user record the proposed content and its execution results, and send feedback back to the server. This data is then reused for the next analysis, contributing to the improvement of the system's accuracy. The input is the execution results of the proposed content, and the output is feedback data for system improvement.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] This invention provides a system that collects diverse information data about individuals within an organization, analyzes it, and proposes optimal personnel placement and development strategies. This system is implemented as follows:

[0220] First, the server connects with various databases within the organization to collect diverse information, including individuals' skills, qualifications, work history, self-reported information, and psychological state. This data collection is performed using APIs and other methods from existing systems and platforms.

[0221] Next, the server integrates the collected data and converts it into a consistent format. This prepares the data for subsequent analysis. The integrated dataset is stored in a database on the server.

[0222] Subsequently, the server uses machine learning algorithms to analyze the integrated data. The purpose of the analysis is to evaluate individuals' skill strengths, career tendencies, and psychological states, and to develop talent placement strategies tailored to the organization's needs. It is also possible to extract psychological tendencies from self-reported data using natural language processing technology.

[0223] Based on the analysis results, the server generates suggestions for optimal placement and training strategies. These suggestions are delivered to the user (HR personnel) via a communication terminal. HR personnel can then use the suggested strategies to make specific placement decisions and implement training plans.

[0224] After execution, the server collects data again on the results of placement and training, and uses that feedback to inform future proposals. For example, to address a lack of programming skills in a project team, the AI ​​analyzes the data and suggests the most suitable transfer candidates from among existing employees. In this way, efficient personnel allocation and resource optimization can be achieved.

[0225] This system leverages AI technology to comprehensively and efficiently optimize human resources strategies in order to address challenges faced by organizations, such as talent shortages and increasing operational complexity.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The server first connects to the organization's database and human resources information system, and retrieves personal information via APIs. This information includes an individual's skills, qualifications, work history, and self-reported data. It also retrieves PC usage history and various log data, which are anonymized according to security standards.

[0229] Step 2:

[0230] The server converts the acquired information into a unified format and generates an analyzable dataset. During this process, it cross-references data from different sources to verify data consistency and removes duplicate data.

[0231] Step 3:

[0232] The server applies machine learning algorithms using organized datasets. Clustering algorithms and regression analysis are performed to analyze individual skills and career tendencies. In addition, natural language processing is used to perform sentiment analysis on self-reported data and assess psychological state.

[0233] Step 4:

[0234] The server generates suggestions for personnel placement and training strategies based on the analysis results. This includes matching individual skills, assessing position suitability, and reassessing roles within teams. The suggestions are visually displayed to the user in a dashboard format.

[0235] Step 5:

[0236] The terminal notifies HR personnel of the generated proposals and makes them able to download a detailed report of the proposals. HR personnel can review the proposed placements and training plans and make modifications as needed.

[0237] Step 6:

[0238] The user (HR representative) makes the final placement decision based on the information provided by the server. They communicate transfer orders and training instructions to the target individuals and provide specific instructions via communication terminals so that they are reflected in the system.

[0239] Step 7:

[0240] The server collects the results data of the implemented HR measures and stores it in a database for future analysis. This data is used to evaluate performance indicators and verify the effectiveness of proposals. The recollected data facilitates the streamlining of future decision-making processes.

[0241] (Example 1)

[0242] 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."

[0243] Effectively leveraging individual characteristics within companies and organizations to develop optimal talent allocation and training strategies is complex and requires the collection and analysis of appropriate data. However, existing systems often fail to smoothly manage the entire process from data collection to analysis and feedback, making efficient talent management difficult. A more comprehensive and rapid solution is needed to address these problems.

[0244] 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.

[0245] In this invention, the server includes means for collecting diverse information about individuals belonging to an organization, means for integrating the information, converting it into a unified format, and analyzing it using machine learning processing, and means for generating and proposing strategies for the placement and development of individuals based on the analysis results. This makes it possible to efficiently and effectively place and develop personnel within the organization.

[0246] An "organization" is a group of individuals formed to achieve a specific purpose, and includes companies and other organizations.

[0247] An "individual" refers to a single member of an organization, and is the subject of information collection and analysis.

[0248] "Information" refers to data about an individual, such as their abilities, qualifications, and history, and is the subject of collection and integration.

[0249] "Integration" refers to the process of standardizing diverse forms of information and converting them into a single, unified format.

[0250] "Machine learning processing" refers to techniques that automatically learn from data and perform identification and prediction, and are used in analysis.

[0251] "Analysis" refers to the process of extracting meaningful information from collected data and evaluating the characteristics and tendencies of individuals.

[0252] "Proposal" refers to the placement and training strategies generated based on the analysis results, and is transmitted to the display device.

[0253] A "display device" refers to a device that receives proposal content and displays it visually to the user.

[0254] "Feedback" refers to the process of collecting data again based on the results of implemented suggestions and using it for the next analysis.

[0255] This invention provides a specific system for collecting diverse information about individuals belonging to an organization and for forming personnel allocation and training strategies. Embodiments thereof are shown below.

[0256] The server connects to various databases within the organization and collects information on individual capabilities, qualifications, and history using APIs. This includes data management systems and evaluation platforms. The server centralizes the collected information and converts it into a unified format, preparing it for subsequent machine learning processing.

[0257] The server uses the prepared data and applies machine learning algorithms to perform analysis. This analysis utilizes clustering and natural language processing techniques to evaluate the psychological tendencies and career path tendencies of individuals. In this process, the server uses a generative AI model to generate optimal placement and training strategies.

[0258] The generated proposals are sent from the server to the terminal and visually reviewed by the HR personnel who are the users. Based on the provided strategies, users can then implement specific personnel placement and training plans. For example, if a project requires specific technical skills, the server can propose appropriate personnel placement plans, enabling a rapid response.

[0259] The results after execution are collected again as data by the server and used as feedback for the next analysis. This allows the system to be continuously improved and generate more refined suggestions.

[0260] An example of a prompt message to be input to a generative AI model is: "Please suggest the optimal personnel allocation to contribute to the success of a certain project."

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The server collects information about individuals from various databases within the organization using APIs. The specific inputs include data on each individual's abilities, qualifications, and history, which the server centrally manages. This collected data is then used in subsequent analysis processes.

[0264] Step 2:

[0265] The server integrates the collected data and converts it to a standard format. Here, data from different formats is made consistent, missing values ​​are imputed, and duplicate data is removed. The input is the diverse data formats collected in Step 1, and the output is a unified format dataset. This consistent dataset is used in the next analysis step.

[0266] Step 3:

[0267] The server applies machine learning algorithms to a dataset in a unified format and performs analysis. In this step, clustering and natural language processing techniques are used to analyze individual characteristics and psychological tendencies based on the input data. The output of the analysis is a specific evaluation result that includes individual strengths and strategies suitable for placement.

[0268] Step 4:

[0269] The server uses a generated AI model based on the analysis results to produce appropriate placement and training strategies. The input is the analysis results obtained in step 3, and the output is a specific personnel placement strategy and training plan. The generated proposals are sent to the next notification step.

[0270] Step 5:

[0271] The server sends the generated proposal to the terminal, notifying the HR representative (the user). The terminal displays the strategy in a visually easy-to-understand format. The input is the generated proposal, and the output is the visual display received by the user. The user reviews the proposal on the terminal and decides whether to implement it.

[0272] Step 6:

[0273] The server collects the results of the proposed implementations and stores them as feedback data to be used in subsequent analyses. The input is the result of the implemented personnel allocation and training, and the output is feedback data for the next analysis. This feedback allows the server's analysis model to be continuously improved.

[0274] (Application Example 1)

[0275] 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."

[0276] In production sites such as factories, there is a need to improve work efficiency by achieving the optimal allocation of personnel and equipment. However, conventional methods make it difficult to grasp the work situation in real time and to propose flexible allocations based on individual skills and psychological states, hindering the realization of efficient production activities.

[0277] 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.

[0278] In this invention, the server includes means for collecting information about individuals within an organization, means for integrating and analyzing the information, and means for proposing a placement strategy to improve the efficiency of the manufacturing process. This makes it possible to grasp the work status in real time and make appropriate placement suggestions that take into account individual skills and psychological states.

[0279] "Information about individuals within an organization" includes data such as an individual's skills, qualifications, operational history, and operator feedback.

[0280] "Means for integrating and analyzing information" refers to systems and processes that convert diverse collected data into a consistent format and perform analysis using machine learning algorithms.

[0281] The "means of proposing an allocation strategy" is a system or process for proposing optimal personnel allocation and equipment allocation aimed at improving work efficiency based on the analysis results.

[0282] "Grasping the work situation in real time" means immediately monitoring and recording the progress and load of the current work at the production site using sensors and data collection systems.

[0283] "Proposing an appropriate allocation considering an individual's skills and mental state" means analyzing the abilities, past work history, and current mental state of individual workers and providing an allocation plan that maximizes productivity based on this analysis.

[0284] To implement this invention, a system in which a server plays a central role is constructed. The server collaborates with various databases within the organization and collects information data including skills, qualifications, operation history, and operator feedback regarding individuals. Specifically, it connects to IoT sensors and existing databases arranged within the factory and collects data in real time. This data collection is performed using data communication means such as APIs.

[0285] The collected data is integrated into a consistent format on the server and analyzed using machine learning algorithms. For this analysis, database management using the Django framework with Python is performed. A machine learning model utilizing scikit-learn is used to propose optimal allocation strategies for individual workers and equipment. Also, spaCy is introduced as natural language processing technology to evaluate operator feedback and mental state.

[0286] Based on the analysis results, the server proposes optimal allocation and work strategies and transmits them to the information terminal. As this communication terminal, smartphones and tablets are considered, and factory managers and operators use these to receive the proposals.

[0287] As a concrete example, consider a situation where work efficiency is declining on a factory line. In this case, the generative AI model evaluates equipment operation data and worker feedback, and proposes to deploy highly skilled workers as a countermeasure. This proposal is immediately delivered to the factory manager as a smartphone notification, enabling rapid decision-making.

[0288] Example prompt: "Based on recent integrated data, please provide the optimal operator placement plan to improve operational efficiency within the factory."

[0289] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0290] Step 1:

[0291] The server collects individual skills, qualifications, operational history, and work status data from IoT sensors within the factory and existing databases. Inputs include real-time sensor data and historical data, and output is the generation of an integrated dataset of this information. The data is retrieved using APIs, undergoes initial processing, and is converted into a well-formatted form.

[0292] Step 2:

[0293] The server integrates the collected data into a consistent format and then stores it in a database using the Django framework. By accepting a processed dataset as input and saving it to the database as output, the information becomes easily accessible, preparing it for subsequent analysis.

[0294] Step 3:

[0295] The server uses scikit-learn to apply machine learning models to and analyze integrated data in a database. The input is a dataset obtained from the database, and the output is the analysis results. This process models individual skill tendencies and psychological states, and develops placement strategies based on these evaluations.

[0296] Step 4:

[0297] The server generates optimal personnel and equipment allocation strategies based on the analysis results. The input is the evaluation results from machine learning, and the output is an allocation proposal. This proposal is generated as a prompt message and provided as information for decision-making.

[0298] Step 5:

[0299] The server sends the generated proposals to information terminals, where they are displayed on the smartphones and tablets of factory managers and operators. The input is a placement proposal, and the output is a notification sent to the user's terminal. This step allows for timely decisions regarding placement changes and support assignments.

[0300] Step 6:

[0301] Users issue specific work instructions and implement deployment strategies based on suggestions displayed on their terminals. Input is notification information from the terminal, and output is reflected on-site in the form of work instruction execution. This result is then fed back into subsequent data collection and used for analysis in the future.

[0302] 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.

[0303] The present invention combines an emotion engine with an optimization system driven by AI in human resource management to provide a richer personnel strategy. This system collects various information data regarding individuals within an organization, integrates and analyzes this data, and then proposes placement strategies and training plans. Furthermore, the emotion engine recognizes the user's emotional state in real-time and utilizes this in the analysis.

[0304] First, the server obtains information such as an individual's skills, qualifications, and operation history from various databases held by the organization. Also, the emotion engine collects the user's voice data and facial expression data, and analyzes the current emotions from these data. Machine learning is utilized for emotion recognition, analyzing voice tones and facial expression patterns to determine emotions such as stress, joy, and confusion.

[0305] All the collected data is integrated by the server and sent to an analysis module. Here, a machine learning algorithm is applied to generate strategies for the optimal placement and training of each individual. By incorporating emotion data in addition to the data for each individual, it evaluates how the psychological state affects the optimal business results and reflects this in the strategy.

[0306] The generated proposal is sent to a communication terminal, and the user can confirm this through the system interface. For example, a team leader can receive the system proposal and make a decision to optimize the placement of team members for a specific project. If the members are not sufficiently refreshed, the proposal may include an adjustment of the workload suitable for the psychological state.

[0307] The terminal re-collects the data on the execution results of the proposal and sends it to the server as feedback for the next analysis. Through this feedback process, the system can improve the accuracy and effectiveness of its proposals over time.

[0308] In this way, the present invention expands the dimensions of human resource management within organizations by utilizing an emotion engine in addition to conventional information data, thereby enabling the construction of more adaptable and dynamic human resource strategies.

[0309] The following describes the processing flow.

[0310] Step 1:

[0311] The server accesses various databases within the organization to collect data such as individual skills, qualifications, and operational history. It uses APIs and data interfaces to accurately retrieve the necessary data in real time.

[0312] Step 2:

[0313] The server uses an emotion engine to collect emotional data in real time from the user's voice and facial expressions. Voice data is acquired through recording devices, and facial expression data is captured by cameras and image analysis tools. This data is used to instantly determine the user's psychological state.

[0314] Step 3:

[0315] The server integrates all collected data and stores it appropriately in the database. To maintain data consistency, it standardizes and organizes information from different data sources.

[0316] Step 4:

[0317] The server operates machine learning algorithms and analyzes integrated data. By incorporating emotional data in addition to individual skill sets and qualifications, it generates personalized placement and development strategies. The algorithms evaluate stress levels and motivation from the emotional data and optimize the recommendations.

[0318] Step 5:

[0319] The terminal notifies the user of generated deployment and training strategy proposals. The information is presented to the user in a visually easy-to-understand dashboard format. The user can then review the proposals in detail and develop their next action plan.

[0320] Step 6:

[0321] The user (HR representative) reviews the server's proposal and provides specific instructions as needed, such as adjusting workloads or rearranging team assignments. They utilize the system's feedback function to track the results of their decisions and adapt as necessary.

[0322] Step 7:

[0323] The server collects feedback data after execution and uses it to improve the accuracy of future suggestions. The feedback includes productivity metrics after reassignment and results from employee satisfaction surveys, which are used to measure the overall effectiveness of the system and serve as a basis for future improvements.

[0324] (Example 2)

[0325] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0326] To achieve effective human resource management within an organization, it is necessary to accurately understand the psychological state of individual members, in addition to their abilities and qualifications, and to reflect this in their job assignments and training plans. However, conventional systems have a problem in that they do not adequately consider such psychological states in human resource management.

[0327] 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.

[0328] In this invention, the server includes means for acquiring diverse attribute data about members of an organization, means for integrating the attribute data and analyzing it based on machine learning technology, and means for providing member placement plans and training plans based on the analysis results. This makes it possible to propose human resource strategies that take into account not only the members' skills and usage history, but also their psychological state.

[0329] An "organization" is a group of people who share a common purpose and function based on a certain structure and division of roles.

[0330] "Members" refer to individuals or groups who belong to an organization and contribute to achieving its objectives.

[0331] "Attribute data" refers to data that includes information such as the abilities, qualifications, and history of the members.

[0332] "Machine learning technology" refers to techniques that analyze large amounts of data, extract patterns from it, and use that information for predictions and decision-making.

[0333] "Analysis" refers to the process of processing collected data using statistical methods and algorithms to obtain meaningful information.

[0334] A "staffing plan" is a plan that determines how to allocate members within an organization.

[0335] A "development plan" is a plan for a series of activities aimed at improving the skills and abilities of its members.

[0336] An "information processing device" refers to an electronic device used for inputting, processing, storing, and outputting information.

[0337] "Feedback" refers to information returned to the system, which is used to improve operations and for future analyses.

[0338] This invention is a system that utilizes diverse attribute data of its members to achieve personnel management that takes psychological state into consideration. This system is mainly implemented through the process of collecting, analyzing, proposing, and providing feedback on various data between the server, terminals, and users.

[0339] The server first retrieves attribute data such as members' skills, qualifications, and usage history from the organization's database. This process utilizes SQL database management systems and specific data retrieval APIs. It also uses an emotion engine to collect user voice and facial expression data, and uses machine learning techniques to determine their psychological state based on this data. Speech recognition (e.g., speech processing APIs) and image processing (e.g., OpenCV) are used for analysis.

[0340] All acquired data is integrated and processed by machine learning algorithms on the server. Generative AI models are used to derive appropriate work assignments and training plans for each member. This analysis is expected to utilize libraries such as Scikit-learn and TensorFlow.

[0341] The generated work assignment and training plan proposals are sent to the terminal and reviewed by the user through the interface. For example, a team leader can use the proposed HR strategy to select the most suitable members for a project and improve work efficiency.

[0342] Furthermore, the results of the proposal execution are collected on the terminal, and this data is sent back to the server to provide feedback for the next proposal. This feedback allows for continuous improvement in the accuracy and reliability of the proposals.

[0343] A specific example of a prompt message might be, "Create an optimal workload adjustment plan based on the member's past operational data and current emotional state." This allows the server to generate suggestions that reflect the current psychological state.

[0344] By implementing this system, organizations can expect to achieve flexible and adaptable talent management that takes psychological states into account, thereby maximizing the performance of their members.

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The server connects to the organization's database and retrieves attribute data such as members' skills, qualifications, and usage history. The input data is extracted using SQL queries, and the output is a dataset organized by member. This dataset will serve as the basis for subsequent integrated analysis.

[0348] Step 2:

[0349] The server uses an emotion engine to collect user voice and facial expression data in real time. This process uses a microphone to record voice and a camera to capture facial expressions. Voice signals and image data are collected as input, and this data is analyzed by an emotion recognition algorithm as output. Specifically, a voice recognition API is used for voice tone analysis, and an image processing library is used for facial expression analysis.

[0350] Step 3:

[0351] The server integrates acquired attribute and sentiment data and applies machine learning algorithms. A unified dataset of member data and sentiments is used as input, and optimal placement and training plans for each member are generated as output. Data processing primarily utilizes libraries such as TensorFlow and Scikit-learn, and generative AI models are used to derive strategies.

[0352] Step 4:

[0353] The generated work assignments and training plans are sent to the terminal, where the user reviews them through the system interface. The generated plans are the input, and the plans are displayed on the user's screen as output. Based on the suggestions, the user can make decisions that meet the organization's needs.

[0354] Step 5:

[0355] The terminal recollects the results of the proposed execution and sends the data to the server. User feedback data is collected as input, and the output becomes analysis data that will be used next. Based on this feedback, the system aims to improve the accuracy of the next proposal. In this process, data trends are identified and categorized, and the content of the proposal is improved.

[0356] (Application Example 2)

[0357] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0358] While streamlining personnel allocation and development within an organization is crucial for improving corporate productivity, conventional technologies have made it difficult to implement optimal allocation strategies that take into account individual psychological states and workloads. Furthermore, the lack of mechanisms to dynamically adjust tasks in conjunction with automated machinery based on these strategies has led to a growing demand for further efficiency improvements.

[0359] 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.

[0360] In this invention, the server includes means for collecting diverse informational and emotional data about individuals within an organization, means for integrating the informational and emotional data and analyzing it based on a machine learning algorithm, and means for proposing individual placement and development strategies based on the analysis results and reflecting these proposals in automated work machines to adjust tasks. This enables optimal work placement and efficient task shifting based on each individual's skills and psychological state.

[0361] "Diverse information data about individuals within an organization" refers to an individual's skills, qualifications, work history, and other work-related information necessary for evaluation.

[0362] "Emotional data" refers to information about an individual's psychological state obtained by analyzing their facial expressions and voice data.

[0363] A "machine learning algorithm" refers to a program or method used to analyze large amounts of data, find patterns, and make predictions or decisions.

[0364] "Placement strategy" refers to a plan for assigning personnel within an organization to the most suitable teams and projects.

[0365] A "development strategy" refers to a plan for improving an individual's skills and abilities.

[0366] "Automated work machines" refer to mechanical devices used in factories and organizations to perform tasks and operations through automated processes.

[0367] "Task shifting" refers to reassigning some or all of a task or operation to another worker or machine.

[0368] The system that implements this application example is server-centered, collecting individual user information and emotional data and performing integrated analysis. The server retrieves user skills, qualifications, and operational history from a database as information data, and collects facial expression and voice data using cameras and microphones as emotional data. This data is analyzed in real time using Microsoft Azure's Face API and Google Cloud's Speech-to-Text API.

[0369] The server uses machine learning libraries such as TensorFlow and PyTorch to analyze the collected data and evaluate the user's current psychological state. Based on this evaluation, it dynamically adjusts task assignments to automated machines to ensure that workers receive appropriate instructions. The generated suggestions are sent to a communication terminal, which the user can review through the system interface.

[0370] For example, if a worker in a factory shows signs of fatigue, this system can immediately detect this condition and instruct automated machinery to shift the workload. This reduces the burden on the worker and improves productivity.

[0371] Examples of prompts for a generative AI model:

[0372] "Analyze the worker's real-time facial expression and voice data to assess their current psychological state. Based on the assessment results, propose how to adjust their work tasks."

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] The server retrieves information data about users within the organization. As input, it collects user skills, qualifications, and operational history from a database to construct the information data. As output, it obtains a user information dataset necessary for analysis. This step verifies the completeness and accuracy of the data.

[0376] Step 2:

[0377] The server uses a camera and microphone to collect user emotion data. It takes real-time facial expression and voice data as input. The output provides data for analyzing the user's current psychological state. Here, Microsoft Azure's Face API and Google Cloud's Speech-to-Text API are used to analyze emotions.

[0378] Step 3:

[0379] The server analyzes collected informational and emotional data using machine learning algorithms. The input is the dataset obtained in steps 1 and 2. The output is the user's psychological state evaluation results and, based on these, suggestions for placement and training strategies. This step involves complex data analysis using TensorFlow or PyTorch.

[0380] Step 4:

[0381] The server adjusts task assignments to automated machines based on the analysis results. It uses the evaluation results and suggestions from the previous step as input. The output is an adjusted task instruction designed to reduce the workload on workers. Specifically, it changes the proportion of tasks assigned to the robots, taking into account the current workload of the workers.

[0382] Step 5:

[0383] The server sends the generated proposals and task instructions to the communication terminal. It provides the information obtained in step 4 as input. The information is delivered as output in a format that the user can view through the system interface. This step ensures accurate and rapid information transmission.

[0384] Step 6:

[0385] The terminal and user record the proposed content and its execution results, and send feedback back to the server. This data is then reused for the next analysis, contributing to the improvement of the system's accuracy. The input is the execution results of the proposed content, and the output is feedback data for system improvement.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] [Third Embodiment]

[0390] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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".

[0402] This invention provides a system that collects diverse information data about individuals within an organization, analyzes it, and proposes optimal personnel placement and development strategies. This system is implemented as follows:

[0403] First, the server connects with various databases within the organization to collect diverse information, including individuals' skills, qualifications, work history, self-reported information, and psychological state. This data collection is performed using APIs and other methods from existing systems and platforms.

[0404] Next, the server integrates the collected data and converts it into a consistent format. This prepares the data for subsequent analysis. The integrated dataset is stored in a database on the server.

[0405] Subsequently, the server uses machine learning algorithms to analyze the integrated data. The purpose of the analysis is to evaluate individuals' skill strengths, career tendencies, and psychological states, and to develop talent placement strategies tailored to the organization's needs. It is also possible to extract psychological tendencies from self-reported data using natural language processing technology.

[0406] Based on the analysis results, the server generates suggestions for optimal placement and training strategies. These suggestions are delivered to the user (HR personnel) via a communication terminal. HR personnel can then use the suggested strategies to make specific placement decisions and implement training plans.

[0407] After execution, the server collects data again on the results of placement and training, and uses that feedback to inform future proposals. For example, to address a lack of programming skills in a project team, the AI ​​analyzes the data and suggests the most suitable transfer candidates from among existing employees. In this way, efficient personnel allocation and resource optimization can be achieved.

[0408] This system leverages AI technology to comprehensively and efficiently optimize human resources strategies in order to address challenges faced by organizations, such as talent shortages and increasing operational complexity.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] The server first connects to the organization's database and human resources information system, and retrieves personal information via APIs. This information includes an individual's skills, qualifications, work history, and self-reported data. It also retrieves PC usage history and various log data, which are anonymized according to security standards.

[0412] Step 2:

[0413] The server converts the acquired information into a unified format and generates an analyzable dataset. During this process, it cross-references data from different sources to verify data consistency and removes duplicate data.

[0414] Step 3:

[0415] The server applies machine learning algorithms using organized datasets. Clustering algorithms and regression analysis are performed to analyze individual skills and career tendencies. In addition, natural language processing is used to perform sentiment analysis on self-reported data and assess psychological state.

[0416] Step 4:

[0417] The server generates suggestions for personnel placement and training strategies based on the analysis results. This includes matching individual skills, assessing position suitability, and reassessing roles within teams. The suggestions are visually displayed to the user in a dashboard format.

[0418] Step 5:

[0419] The terminal notifies HR personnel of the generated proposals and makes them able to download a detailed report of the proposals. HR personnel can review the proposed placements and training plans and make modifications as needed.

[0420] Step 6:

[0421] The user (HR representative) makes the final placement decision based on the information provided by the server. They communicate transfer orders and training instructions to the target individuals and provide specific instructions via communication terminals so that they are reflected in the system.

[0422] Step 7:

[0423] The server collects the results data of the implemented HR measures and stores it in a database for future analysis. This data is used to evaluate performance indicators and verify the effectiveness of proposals. The recollected data facilitates the streamlining of future decision-making processes.

[0424] (Example 1)

[0425] 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."

[0426] Effectively leveraging individual characteristics within companies and organizations to develop optimal talent allocation and training strategies is complex and requires the collection and analysis of appropriate data. However, existing systems often fail to smoothly manage the entire process from data collection to analysis and feedback, making efficient talent management difficult. A more comprehensive and rapid solution is needed to address these problems.

[0427] 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.

[0428] In this invention, the server includes means for collecting diverse information about individuals belonging to an organization, means for integrating the information, converting it into a unified format, and analyzing it using machine learning processing, and means for generating and proposing strategies for the placement and development of individuals based on the analysis results. This makes it possible to efficiently and effectively place and develop personnel within the organization.

[0429] An "organization" is a group of individuals formed to achieve a specific purpose, and includes companies and other organizations.

[0430] An "individual" refers to a single member of an organization, and is the subject of information collection and analysis.

[0431] "Information" refers to data about an individual, such as their abilities, qualifications, and history, and is the subject of collection and integration.

[0432] "Integration" refers to the process of standardizing diverse forms of information and converting them into a single, unified format.

[0433] "Machine learning processing" refers to techniques that automatically learn from data and perform identification and prediction, and are used in analysis.

[0434] "Analysis" refers to the process of extracting meaningful information from collected data and evaluating the characteristics and tendencies of individuals.

[0435] "Proposal" refers to the placement and training strategies generated based on the analysis results, and is transmitted to the display device.

[0436] A "display device" refers to a device that receives proposal content and displays it visually to the user.

[0437] "Feedback" refers to the process of collecting data again based on the results of implemented suggestions and using it for the next analysis.

[0438] This invention provides a specific system for collecting diverse information about individuals belonging to an organization and for forming personnel allocation and training strategies. Embodiments thereof are shown below.

[0439] The server connects to various databases within the organization and collects information on individual capabilities, qualifications, and history using APIs. This includes data management systems and evaluation platforms. The server centralizes the collected information and converts it into a unified format, preparing it for subsequent machine learning processing.

[0440] The server uses the prepared data and applies machine learning algorithms to perform analysis. This analysis utilizes clustering and natural language processing techniques to evaluate the psychological tendencies and career path tendencies of individuals. In this process, the server uses a generative AI model to generate optimal placement and training strategies.

[0441] The generated proposals are sent from the server to the terminal and visually reviewed by the HR personnel who are the users. Based on the provided strategies, users can then implement specific personnel placement and training plans. For example, if a project requires specific technical skills, the server can propose appropriate personnel placement plans, enabling a rapid response.

[0442] The results after execution are collected again as data by the server and used as feedback for the next analysis. This allows the system to be continuously improved and generate more refined suggestions.

[0443] An example of a prompt message to be input to a generative AI model is: "Please suggest the optimal personnel allocation to contribute to the success of a certain project."

[0444] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0445] Step 1:

[0446] The server collects information about individuals from various databases within the organization using APIs. The specific inputs include data on each individual's abilities, qualifications, and history, which the server centrally manages. This collected data is then used in subsequent analysis processes.

[0447] Step 2:

[0448] The server integrates the collected data and converts it to a standard format. Here, data from different formats is made consistent, missing values ​​are imputed, and duplicate data is removed. The input is the diverse data formats collected in Step 1, and the output is a unified format dataset. This consistent dataset is used in the next analysis step.

[0449] Step 3:

[0450] The server applies machine learning algorithms to a dataset in a unified format and performs analysis. In this step, clustering and natural language processing techniques are used to analyze individual characteristics and psychological tendencies based on the input data. The output of the analysis is a specific evaluation result that includes individual strengths and strategies suitable for placement.

[0451] Step 4:

[0452] The server uses a generated AI model based on the analysis results to produce appropriate placement and training strategies. The input is the analysis results obtained in step 3, and the output is a specific personnel placement strategy and training plan. The generated proposals are sent to the next notification step.

[0453] Step 5:

[0454] The server sends the generated proposal to the terminal, notifying the HR representative (the user). The terminal displays the strategy in a visually easy-to-understand format. The input is the generated proposal, and the output is the visual display received by the user. The user reviews the proposal on the terminal and decides whether to implement it.

[0455] Step 6:

[0456] The server collects the results of the proposed implementations and stores them as feedback data to be used in subsequent analyses. The input is the result of the implemented personnel allocation and training, and the output is feedback data for the next analysis. This feedback allows the server's analysis model to be continuously improved.

[0457] (Application Example 1)

[0458] 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."

[0459] In production sites such as factories, there is a need to improve work efficiency by achieving the optimal allocation of personnel and equipment. However, conventional methods make it difficult to grasp the work situation in real time and to propose flexible allocations based on individual skills and psychological states, hindering the realization of efficient production activities.

[0460] 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.

[0461] In this invention, the server includes means for collecting information about individuals within an organization, means for integrating and analyzing the information, and means for proposing a placement strategy to improve the efficiency of the manufacturing process. This makes it possible to grasp the work status in real time and make appropriate placement suggestions that take into account individual skills and psychological states.

[0462] "Information about individuals within an organization" includes data such as an individual's skills, qualifications, operational history, and operator feedback.

[0463] "Means for integrating and analyzing information" refers to systems and processes that convert diverse collected data into a consistent format and perform analysis using machine learning algorithms.

[0464] "Means for proposing deployment strategies" refer to systems and processes that propose optimal personnel and equipment deployments based on analysis results, with the aim of improving work efficiency.

[0465] "Real-time monitoring of work status" means instantly monitoring and recording the current progress and workload of work on the production floor using sensors and data collection systems.

[0466] "Appropriate placement proposals that take into account individual skills and psychological state" means analyzing each worker's abilities, past work history, and current psychological state, and then providing a placement plan that maximizes productivity based on that analysis.

[0467] To implement this invention, a system in which a server plays a central role will be constructed. The server will interact with various databases within the organization to collect information data, including individual skills, qualifications, operation history, and operator feedback. Specifically, it will connect to IoT sensors placed within the factory and existing databases to collect data in real time. This data collection will be performed using data communication means such as APIs.

[0468] The collected data is integrated into a consistent format on the server and analyzed using machine learning algorithms. For this analysis, database management is performed using the Django framework with Python. A machine learning model utilizing scikit-learn is used to propose optimal placement strategies for individual workers and equipment. Additionally, spaCy is introduced as a natural language processing technique to evaluate operator feedback and psychological state.

[0469] Based on the analysis results, the server proposes the optimal layout and work strategy and sends it to an information terminal. This communication terminal could be a smartphone or tablet, which factory managers and operators would use to receive the proposal.

[0470] As a concrete example, consider a situation where work efficiency is declining on a factory line. In this case, the generative AI model evaluates equipment operation data and worker feedback, and proposes to deploy highly skilled workers as a countermeasure. This proposal is immediately delivered to the factory manager as a smartphone notification, enabling rapid decision-making.

[0471] Example prompt: "Based on recent integrated data, please provide the optimal operator placement plan to improve operational efficiency within the factory."

[0472] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0473] Step 1:

[0474] The server collects individual skills, qualifications, operational history, and work status data from IoT sensors within the factory and existing databases. Inputs include real-time sensor data and historical data, and output is the generation of an integrated dataset of this information. The data is retrieved using APIs, undergoes initial processing, and is converted into a well-formatted form.

[0475] Step 2:

[0476] The server integrates the collected data into a consistent format and then stores it in a database using the Django framework. By accepting a processed dataset as input and saving it to the database as output, the information becomes easily accessible, preparing it for subsequent analysis.

[0477] Step 3:

[0478] The server uses scikit-learn to apply machine learning models to and analyze integrated data in a database. The input is a dataset obtained from the database, and the output is the analysis results. This process models individual skill tendencies and psychological states, and develops placement strategies based on these evaluations.

[0479] Step 4:

[0480] The server generates optimal personnel and equipment allocation strategies based on the analysis results. The input is the evaluation results from machine learning, and the output is an allocation proposal. This proposal is generated as a prompt message and provided as information for decision-making.

[0481] Step 5:

[0482] The server sends the generated proposals to information terminals, where they are displayed on the smartphones and tablets of factory managers and operators. The input is a placement proposal, and the output is a notification sent to the user's terminal. This step allows for timely decisions regarding placement changes and support assignments.

[0483] Step 6:

[0484] Users issue specific work instructions and implement deployment strategies based on suggestions displayed on their terminals. Input is notification information from the terminal, and output is reflected on-site in the form of work instruction execution. This result is then fed back into subsequent data collection and used for analysis in the future.

[0485] 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.

[0486] This invention combines an emotion engine with an AI-powered optimization system for human resource management to provide richer HR strategies. This system collects diverse information data about individuals within an organization, integrates and analyzes it, and proposes placement strategies and training plans. Furthermore, the emotion engine recognizes the user's emotional state in real time and utilizes this information for analysis.

[0487] The server first retrieves information such as an individual's skills, qualifications, and operational history from various databases maintained by the organization. It also uses an emotion engine to collect user voice and facial expression data, analyzing this data to determine the user's current emotions. Machine learning is employed for emotion recognition, analyzing voice tone and facial expression patterns to determine emotions such as stress, joy, and confusion.

[0488] All collected data is integrated by a server and sent to an analysis module. Here, machine learning algorithms are applied to generate optimal placement and development strategies for each individual. In addition to individual data, emotional data is incorporated to evaluate how psychological state affects optimal work performance and reflect this in the strategy.

[0489] The generated suggestions are sent to a communication terminal, and users can review them through the system interface. For example, a team leader can receive system suggestions and make decisions to streamline the allocation of team members to a particular project. If members are not sufficiently refreshed, the suggestions may include adjusting their workload to match their mental state.

[0490] The terminal recollects data from the execution results of the proposal and sends it to the server as feedback for the next analysis. This feedback process allows the system to improve the accuracy and effectiveness of its proposals over time.

[0491] In this way, the present invention expands the dimensions of human resource management within organizations by utilizing an emotion engine in addition to conventional information data, thereby enabling the construction of more adaptable and dynamic human resource strategies.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The server accesses various databases within the organization to collect data such as individual skills, qualifications, and operational history. It uses APIs and data interfaces to accurately retrieve the necessary data in real time.

[0495] Step 2:

[0496] The server uses an emotion engine to collect emotional data in real time from the user's voice and facial expressions. Voice data is acquired through recording devices, and facial expression data is captured by cameras and image analysis tools. This data is used to instantly determine the user's psychological state.

[0497] Step 3:

[0498] The server integrates all collected data and stores it appropriately in the database. To maintain data consistency, it standardizes and organizes information from different data sources.

[0499] Step 4:

[0500] The server operates machine learning algorithms and analyzes integrated data. By incorporating emotional data in addition to individual skill sets and qualifications, it generates personalized placement and development strategies. The algorithms evaluate stress levels and motivation from the emotional data and optimize the recommendations.

[0501] Step 5:

[0502] The terminal notifies the user of generated deployment and training strategy proposals. The information is presented to the user in a visually easy-to-understand dashboard format. The user can then review the proposals in detail and develop their next action plan.

[0503] Step 6:

[0504] The user (HR representative) reviews the server's proposal and provides specific instructions as needed, such as adjusting workloads or rearranging team assignments. They utilize the system's feedback function to track the results of their decisions and adapt as necessary.

[0505] Step 7:

[0506] The server collects feedback data after execution and uses it to improve the accuracy of future suggestions. The feedback includes productivity metrics after reassignment and results from employee satisfaction surveys, which are used to measure the overall effectiveness of the system and serve as a basis for future improvements.

[0507] (Example 2)

[0508] 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."

[0509] To achieve effective human resource management within an organization, it is necessary to accurately understand the psychological state of individual members, in addition to their abilities and qualifications, and to reflect this in their job assignments and training plans. However, conventional systems have a problem in that they do not adequately consider such psychological states in human resource management.

[0510] 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.

[0511] In this invention, the server includes means for acquiring diverse attribute data about members of an organization, means for integrating the attribute data and analyzing it based on machine learning technology, and means for providing member placement plans and training plans based on the analysis results. This makes it possible to propose human resource strategies that take into account not only the members' skills and usage history, but also their psychological state.

[0512] An "organization" is a group of people who share a common purpose and function based on a certain structure and division of roles.

[0513] "Members" refer to individuals or groups who belong to an organization and contribute to achieving its objectives.

[0514] "Attribute data" refers to data that includes information such as the abilities, qualifications, and history of the members.

[0515] "Machine learning technology" refers to techniques that analyze large amounts of data, extract patterns from it, and use that information for predictions and decision-making.

[0516] "Analysis" refers to the process of processing collected data using statistical methods and algorithms to obtain meaningful information.

[0517] A "staffing plan" is a plan that determines how to allocate members within an organization.

[0518] A "development plan" is a plan for a series of activities aimed at improving the skills and abilities of its members.

[0519] An "information processing device" refers to an electronic device used for inputting, processing, storing, and outputting information.

[0520] "Feedback" refers to information returned to the system, which is used to improve operations and for future analyses.

[0521] This invention is a system that utilizes diverse attribute data of its members to achieve personnel management that takes psychological state into consideration. This system is mainly implemented through the process of collecting, analyzing, proposing, and providing feedback on various data between the server, terminals, and users.

[0522] The server first retrieves attribute data such as members' skills, qualifications, and usage history from the organization's database. This process utilizes SQL database management systems and specific data retrieval APIs. It also uses an emotion engine to collect user voice and facial expression data, and uses machine learning techniques to determine their psychological state based on this data. Speech recognition (e.g., speech processing APIs) and image processing (e.g., OpenCV) are used for analysis.

[0523] All acquired data is integrated and processed by machine learning algorithms on the server. Generative AI models are used to derive appropriate work assignments and training plans for each member. This analysis is expected to utilize libraries such as Scikit-learn and TensorFlow.

[0524] The generated work assignment and training plan proposals are sent to the terminal and reviewed by the user through the interface. For example, a team leader can use the proposed HR strategy to select the most suitable members for a project and improve work efficiency.

[0525] Furthermore, the results of the proposal execution are collected on the terminal, and this data is sent back to the server to provide feedback for the next proposal. This feedback allows for continuous improvement in the accuracy and reliability of the proposals.

[0526] A specific example of a prompt message might be, "Create an optimal workload adjustment plan based on the member's past operational data and current emotional state." This allows the server to generate suggestions that reflect the current psychological state.

[0527] By implementing this system, organizations can expect to achieve flexible and adaptable talent management that takes psychological states into account, thereby maximizing the performance of their members.

[0528] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0529] Step 1:

[0530] The server connects to the organization's database and retrieves attribute data such as members' skills, qualifications, and usage history. The input data is extracted using SQL queries, and the output is a dataset organized by member. This dataset will serve as the basis for subsequent integrated analysis.

[0531] Step 2:

[0532] The server uses an emotion engine to collect user voice and facial expression data in real time. This process uses a microphone to record voice and a camera to capture facial expressions. Voice signals and image data are collected as input, and this data is analyzed by an emotion recognition algorithm as output. Specifically, a voice recognition API is used for voice tone analysis, and an image processing library is used for facial expression analysis.

[0533] Step 3:

[0534] The server integrates acquired attribute and sentiment data and applies machine learning algorithms. A unified dataset of member data and sentiments is used as input, and optimal placement and training plans for each member are generated as output. Data processing primarily utilizes libraries such as TensorFlow and Scikit-learn, and generative AI models are used to derive strategies.

[0535] Step 4:

[0536] The generated work assignments and training plans are sent to the terminal, where the user reviews them through the system interface. The generated plans are the input, and the plans are displayed on the user's screen as output. Based on the suggestions, the user can make decisions that meet the organization's needs.

[0537] Step 5:

[0538] The terminal recollects the results of the proposed execution and sends the data to the server. User feedback data is collected as input, and the output becomes analysis data that will be used next. Based on this feedback, the system aims to improve the accuracy of the next proposal. In this process, data trends are identified and categorized, and the content of the proposal is improved.

[0539] (Application Example 2)

[0540] 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."

[0541] While streamlining personnel allocation and development within an organization is crucial for improving corporate productivity, conventional technologies have made it difficult to implement optimal allocation strategies that take into account individual psychological states and workloads. Furthermore, the lack of mechanisms to dynamically adjust tasks in conjunction with automated machinery based on these strategies has led to a growing demand for further efficiency improvements.

[0542] 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.

[0543] In this invention, the server includes means for collecting diverse informational and emotional data about individuals within an organization, means for integrating the informational and emotional data and analyzing it based on a machine learning algorithm, and means for proposing individual placement and development strategies based on the analysis results and reflecting these proposals in automated work machines to adjust tasks. This enables optimal work placement and efficient task shifting based on each individual's skills and psychological state.

[0544] "Diverse information data about individuals within an organization" refers to an individual's skills, qualifications, work history, and other work-related information necessary for evaluation.

[0545] "Emotional data" refers to information about an individual's psychological state obtained by analyzing their facial expressions and voice data.

[0546] A "machine learning algorithm" refers to a program or method used to analyze large amounts of data, find patterns, and make predictions or decisions.

[0547] "Placement strategy" refers to a plan for assigning personnel within an organization to the most suitable teams and projects.

[0548] A "development strategy" refers to a plan for improving an individual's skills and abilities.

[0549] "Automated work machines" refer to mechanical devices used in factories and organizations to perform tasks and operations through automated processes.

[0550] "Task shifting" refers to reassigning some or all of a task or operation to another worker or machine.

[0551] The system that implements this application example is server-centered, collecting individual user information and emotional data and performing integrated analysis. The server retrieves user skills, qualifications, and operational history from a database as information data, and collects facial expression and voice data using cameras and microphones as emotional data. This data is analyzed in real time using Microsoft Azure's Face API and Google Cloud's Speech-to-Text API.

[0552] The server uses machine learning libraries such as TensorFlow and PyTorch to analyze the collected data and evaluate the user's current psychological state. Based on this evaluation, it dynamically adjusts task assignments to automated machines to ensure that workers receive appropriate instructions. The generated suggestions are sent to a communication terminal, which the user can review through the system interface.

[0553] For example, if a worker in a factory shows signs of fatigue, this system can immediately detect this condition and instruct automated machinery to shift the workload. This reduces the burden on the worker and improves productivity.

[0554] Examples of prompts for a generative AI model:

[0555] "Analyze the worker's real-time facial expression and voice data to assess their current psychological state. Based on the assessment results, propose how to adjust their work tasks."

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] The server retrieves information data about users within the organization. As input, it collects user skills, qualifications, and operational history from a database to construct the information data. As output, it obtains a user information dataset necessary for analysis. This step verifies the completeness and accuracy of the data.

[0559] Step 2:

[0560] The server uses a camera and microphone to collect user emotion data. It takes real-time facial expression and voice data as input. The output provides data for analyzing the user's current psychological state. Here, Microsoft Azure's Face API and Google Cloud's Speech-to-Text API are used to analyze emotions.

[0561] Step 3:

[0562] The server analyzes collected informational and emotional data using machine learning algorithms. The input is the dataset obtained in steps 1 and 2. The output is the user's psychological state evaluation results and, based on these, suggestions for placement and training strategies. This step involves complex data analysis using TensorFlow or PyTorch.

[0563] Step 4:

[0564] The server adjusts task assignments to automated machines based on the analysis results. It uses the evaluation results and suggestions from the previous step as input. The output is an adjusted task instruction designed to reduce the workload on workers. Specifically, it changes the proportion of tasks assigned to the robots, taking into account the current workload of the workers.

[0565] Step 5:

[0566] The server sends the generated proposals and task instructions to the communication terminal. It provides the information obtained in step 4 as input. The information is delivered as output in a format that the user can view through the system interface. This step ensures accurate and rapid information transmission.

[0567] Step 6:

[0568] The terminal and user record the proposed content and its execution results, and send feedback back to the server. This data is then reused for the next analysis, contributing to the improvement of the system's accuracy. The input is the execution results of the proposed content, and the output is feedback data for system improvement.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] [Fourth Embodiment]

[0573] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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".

[0586] This invention provides a system that collects diverse information data about individuals within an organization, analyzes it, and proposes optimal personnel placement and development strategies. This system is implemented as follows:

[0587] First, the server connects with various databases within the organization to collect diverse information, including individuals' skills, qualifications, work history, self-reported information, and psychological state. This data collection is performed using APIs and other methods from existing systems and platforms.

[0588] Next, the server integrates the collected data and converts it into a consistent format. This prepares the data for subsequent analysis. The integrated dataset is stored in a database on the server.

[0589] Subsequently, the server uses machine learning algorithms to analyze the integrated data. The purpose of the analysis is to evaluate individuals' skill strengths, career tendencies, and psychological states, and to develop talent placement strategies tailored to the organization's needs. It is also possible to extract psychological tendencies from self-reported data using natural language processing technology.

[0590] Based on the analysis results, the server generates suggestions for optimal placement and training strategies. These suggestions are delivered to the user (HR personnel) via a communication terminal. HR personnel can then use the suggested strategies to make specific placement decisions and implement training plans.

[0591] After execution, the server collects data again on the results of placement and training, and uses that feedback to inform future proposals. For example, to address a lack of programming skills in a project team, the AI ​​analyzes the data and suggests the most suitable transfer candidates from among existing employees. In this way, efficient personnel allocation and resource optimization can be achieved.

[0592] This system leverages AI technology to comprehensively and efficiently optimize human resources strategies in order to address challenges faced by organizations, such as talent shortages and increasing operational complexity.

[0593] The following describes the processing flow.

[0594] Step 1:

[0595] The server first connects to the organization's database and human resources information system, and retrieves personal information via APIs. This information includes an individual's skills, qualifications, work history, and self-reported data. It also retrieves PC usage history and various log data, which are anonymized according to security standards.

[0596] Step 2:

[0597] The server converts the acquired information into a unified format and generates an analyzable dataset. During this process, it cross-references data from different sources to verify data consistency and removes duplicate data.

[0598] Step 3:

[0599] The server applies machine learning algorithms using organized datasets. Clustering algorithms and regression analysis are performed to analyze individual skills and career tendencies. In addition, natural language processing is used to perform sentiment analysis on self-reported data and assess psychological state.

[0600] Step 4:

[0601] The server generates suggestions for personnel placement and training strategies based on the analysis results. This includes matching individual skills, assessing position suitability, and reassessing roles within teams. The suggestions are visually displayed to the user in a dashboard format.

[0602] Step 5:

[0603] The terminal notifies HR personnel of the generated proposals and makes them able to download a detailed report of the proposals. HR personnel can review the proposed placements and training plans and make modifications as needed.

[0604] Step 6:

[0605] The user (HR representative) makes the final placement decision based on the information provided by the server. They communicate transfer orders and training instructions to the target individuals and provide specific instructions via communication terminals so that they are reflected in the system.

[0606] Step 7:

[0607] The server collects the results data of the implemented HR measures and stores it in a database for future analysis. This data is used to evaluate performance indicators and verify the effectiveness of proposals. The recollected data facilitates the streamlining of future decision-making processes.

[0608] (Example 1)

[0609] 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".

[0610] Effectively leveraging individual characteristics within companies and organizations to develop optimal talent allocation and training strategies is complex and requires the collection and analysis of appropriate data. However, existing systems often fail to smoothly manage the entire process from data collection to analysis and feedback, making efficient talent management difficult. A more comprehensive and rapid solution is needed to address these problems.

[0611] 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.

[0612] In this invention, the server includes means for collecting diverse information about individuals belonging to an organization, means for integrating the information, converting it into a unified format, and analyzing it using machine learning processing, and means for generating and proposing strategies for the placement and development of individuals based on the analysis results. This makes it possible to efficiently and effectively place and develop personnel within the organization.

[0613] An "organization" is a group of individuals formed to achieve a specific purpose, and includes companies and other organizations.

[0614] An "individual" refers to a single member of an organization, and is the subject of information collection and analysis.

[0615] "Information" refers to data about an individual, such as their abilities, qualifications, and history, and is the subject of collection and integration.

[0616] "Integration" refers to the process of standardizing diverse forms of information and converting them into a single, unified format.

[0617] "Machine learning processing" refers to techniques that automatically learn from data and perform identification and prediction, and are used in analysis.

[0618] "Analysis" refers to the process of extracting meaningful information from collected data and evaluating the characteristics and tendencies of individuals.

[0619] "Proposal" refers to the placement and training strategies generated based on the analysis results, and is transmitted to the display device.

[0620] A "display device" refers to a device that receives proposal content and displays it visually to the user.

[0621] "Feedback" refers to the process of collecting data again based on the results of implemented suggestions and using it for the next analysis.

[0622] This invention provides a specific system for collecting diverse information about individuals belonging to an organization and for forming personnel allocation and training strategies. Embodiments thereof are shown below.

[0623] The server connects to various databases within the organization and collects information on individual capabilities, qualifications, and history using APIs. This includes data management systems and evaluation platforms. The server centralizes the collected information and converts it into a unified format, preparing it for subsequent machine learning processing.

[0624] The server uses the prepared data and applies machine learning algorithms to perform analysis. This analysis utilizes clustering and natural language processing techniques to evaluate the psychological tendencies and career path tendencies of individuals. In this process, the server uses a generative AI model to generate optimal placement and training strategies.

[0625] The generated proposals are sent from the server to the terminal and visually reviewed by the HR personnel who are the users. Based on the provided strategies, users can then implement specific personnel placement and training plans. For example, if a project requires specific technical skills, the server can propose appropriate personnel placement plans, enabling a rapid response.

[0626] The results after execution are collected again as data by the server and used as feedback for the next analysis. This allows the system to be continuously improved and generate more refined suggestions.

[0627] An example of a prompt message to be input to a generative AI model is: "Please suggest the optimal personnel allocation to contribute to the success of a certain project."

[0628] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0629] Step 1:

[0630] The server collects information about individuals from various databases within the organization using APIs. The specific inputs include data on each individual's abilities, qualifications, and history, which the server centrally manages. This collected data is then used in subsequent analysis processes.

[0631] Step 2:

[0632] The server integrates the collected data and converts it to a standard format. Here, data from different formats is made consistent, missing values ​​are imputed, and duplicate data is removed. The input is the diverse data formats collected in Step 1, and the output is a unified format dataset. This consistent dataset is used in the next analysis step.

[0633] Step 3:

[0634] The server applies machine learning algorithms to a dataset in a unified format and performs analysis. In this step, clustering and natural language processing techniques are used to analyze individual characteristics and psychological tendencies based on the input data. The output of the analysis is a specific evaluation result that includes individual strengths and strategies suitable for placement.

[0635] Step 4:

[0636] The server uses a generated AI model based on the analysis results to produce appropriate placement and training strategies. The input is the analysis results obtained in step 3, and the output is a specific personnel placement strategy and training plan. The generated proposals are sent to the next notification step.

[0637] Step 5:

[0638] The server sends the generated proposal to the terminal, notifying the HR representative (the user). The terminal displays the strategy in a visually easy-to-understand format. The input is the generated proposal, and the output is the visual display received by the user. The user reviews the proposal on the terminal and decides whether to implement it.

[0639] Step 6:

[0640] The server collects the results of the proposed implementations and stores them as feedback data to be used in subsequent analyses. The input is the result of the implemented personnel allocation and training, and the output is feedback data for the next analysis. This feedback allows the server's analysis model to be continuously improved.

[0641] (Application Example 1)

[0642] 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".

[0643] In production sites such as factories, there is a need to improve work efficiency by achieving the optimal allocation of personnel and equipment. However, conventional methods make it difficult to grasp the work situation in real time and to propose flexible allocations based on individual skills and psychological states, hindering the realization of efficient production activities.

[0644] 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.

[0645] In this invention, the server includes means for collecting information about individuals within an organization, means for integrating and analyzing the information, and means for proposing a placement strategy to improve the efficiency of the manufacturing process. This makes it possible to grasp the work status in real time and make appropriate placement suggestions that take into account individual skills and psychological states.

[0646] "Information about individuals within an organization" includes data such as an individual's skills, qualifications, operational history, and operator feedback.

[0647] "Means for integrating and analyzing information" refers to systems and processes that convert diverse collected data into a consistent format and perform analysis using machine learning algorithms.

[0648] "Means for proposing deployment strategies" refer to systems and processes that propose optimal personnel and equipment deployments based on analysis results, with the aim of improving work efficiency.

[0649] "Real-time monitoring of work status" means instantly monitoring and recording the current progress and workload of work on the production floor using sensors and data collection systems.

[0650] "Appropriate placement proposals that take into account individual skills and psychological state" means analyzing each worker's abilities, past work history, and current psychological state, and then providing a placement plan that maximizes productivity based on that analysis.

[0651] To implement this invention, a system in which a server plays a central role will be constructed. The server will interact with various databases within the organization to collect information data, including individual skills, qualifications, operation history, and operator feedback. Specifically, it will connect to IoT sensors placed within the factory and existing databases to collect data in real time. This data collection will be performed using data communication means such as APIs.

[0652] The collected data is integrated into a consistent format on the server and analyzed using machine learning algorithms. For this analysis, database management is performed using the Django framework with Python. A machine learning model utilizing scikit-learn is used to propose optimal placement strategies for individual workers and equipment. Additionally, spaCy is introduced as a natural language processing technique to evaluate operator feedback and psychological state.

[0653] Based on the analysis results, the server proposes the optimal layout and work strategy and sends it to an information terminal. This communication terminal could be a smartphone or tablet, which factory managers and operators would use to receive the proposal.

[0654] As a concrete example, consider a situation where work efficiency is declining on a factory line. In this case, the generative AI model evaluates equipment operation data and worker feedback, and proposes to deploy highly skilled workers as a countermeasure. This proposal is immediately delivered to the factory manager as a smartphone notification, enabling rapid decision-making.

[0655] Example prompt: "Based on recent integrated data, please provide the optimal operator placement plan to improve operational efficiency within the factory."

[0656] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0657] Step 1:

[0658] The server collects individual skills, qualifications, operational history, and work status data from IoT sensors within the factory and existing databases. Inputs include real-time sensor data and historical data, and output is the generation of an integrated dataset of this information. The data is retrieved using APIs, undergoes initial processing, and is converted into a well-formatted form.

[0659] Step 2:

[0660] The server integrates the collected data into a consistent format and then stores it in a database using the Django framework. By accepting a processed dataset as input and saving it to the database as output, the information becomes easily accessible, preparing it for subsequent analysis.

[0661] Step 3:

[0662] The server uses scikit-learn to apply machine learning models to and analyze integrated data in a database. The input is a dataset obtained from the database, and the output is the analysis results. This process models individual skill tendencies and psychological states, and develops placement strategies based on these evaluations.

[0663] Step 4:

[0664] The server generates optimal personnel and equipment allocation strategies based on the analysis results. The input is the evaluation results from machine learning, and the output is an allocation proposal. This proposal is generated as a prompt message and provided as information for decision-making.

[0665] Step 5:

[0666] The server sends the generated proposals to information terminals, where they are displayed on the smartphones and tablets of factory managers and operators. The input is a placement proposal, and the output is a notification sent to the user's terminal. This step allows for timely decisions regarding placement changes and support assignments.

[0667] Step 6:

[0668] Users issue specific work instructions and implement deployment strategies based on suggestions displayed on their terminals. Input is notification information from the terminal, and output is reflected on-site in the form of work instruction execution. This result is then fed back into subsequent data collection and used for analysis in the future.

[0669] 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.

[0670] This invention combines an emotion engine with an AI-powered optimization system for human resource management to provide richer HR strategies. This system collects diverse information data about individuals within an organization, integrates and analyzes it, and proposes placement strategies and training plans. Furthermore, the emotion engine recognizes the user's emotional state in real time and utilizes this information for analysis.

[0671] The server first retrieves information such as an individual's skills, qualifications, and operational history from various databases maintained by the organization. It also uses an emotion engine to collect user voice and facial expression data, analyzing this data to determine the user's current emotions. Machine learning is employed for emotion recognition, analyzing voice tone and facial expression patterns to determine emotions such as stress, joy, and confusion.

[0672] All collected data is integrated by a server and sent to an analysis module. Here, machine learning algorithms are applied to generate optimal placement and development strategies for each individual. In addition to individual data, emotional data is incorporated to evaluate how psychological state affects optimal work performance and reflect this in the strategy.

[0673] The generated suggestions are sent to a communication terminal, and users can review them through the system interface. For example, a team leader can receive system suggestions and make decisions to streamline the allocation of team members to a particular project. If members are not sufficiently refreshed, the suggestions may include adjusting their workload to match their mental state.

[0674] The terminal recollects data from the execution results of the proposal and sends it to the server as feedback for the next analysis. This feedback process allows the system to improve the accuracy and effectiveness of its proposals over time.

[0675] In this way, the present invention expands the dimensions of human resource management within organizations by utilizing an emotion engine in addition to conventional information data, thereby enabling the construction of more adaptable and dynamic human resource strategies.

[0676] The following describes the processing flow.

[0677] Step 1:

[0678] The server accesses various databases within the organization to collect data such as individual skills, qualifications, and operational history. It uses APIs and data interfaces to accurately retrieve the necessary data in real time.

[0679] Step 2:

[0680] The server uses an emotion engine to collect emotional data in real time from the user's voice and facial expressions. Voice data is acquired through recording devices, and facial expression data is captured by cameras and image analysis tools. This data is used to instantly determine the user's psychological state.

[0681] Step 3:

[0682] The server integrates all collected data and stores it appropriately in the database. To maintain data consistency, it standardizes and organizes information from different data sources.

[0683] Step 4:

[0684] The server operates machine learning algorithms and analyzes integrated data. By incorporating emotional data in addition to individual skill sets and qualifications, it generates personalized placement and development strategies. The algorithms evaluate stress levels and motivation from the emotional data and optimize the recommendations.

[0685] Step 5:

[0686] The terminal notifies the user of generated deployment and training strategy proposals. The information is presented to the user in a visually easy-to-understand dashboard format. The user can then review the proposals in detail and develop their next action plan.

[0687] Step 6:

[0688] The user (HR representative) reviews the server's proposal and provides specific instructions as needed, such as adjusting workloads or rearranging team assignments. They utilize the system's feedback function to track the results of their decisions and adapt as necessary.

[0689] Step 7:

[0690] The server collects feedback data after execution and uses it to improve the accuracy of future suggestions. The feedback includes productivity metrics after reassignment and results from employee satisfaction surveys, which are used to measure the overall effectiveness of the system and serve as a basis for future improvements.

[0691] (Example 2)

[0692] 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".

[0693] To achieve effective human resource management within an organization, it is necessary to accurately understand the psychological state of individual members, in addition to their abilities and qualifications, and to reflect this in their job assignments and training plans. However, conventional systems have a problem in that they do not adequately consider such psychological states in human resource management.

[0694] 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.

[0695] In this invention, the server includes means for acquiring diverse attribute data about members of an organization, means for integrating the attribute data and analyzing it based on machine learning technology, and means for providing member placement plans and training plans based on the analysis results. This makes it possible to propose human resource strategies that take into account not only the members' skills and usage history, but also their psychological state.

[0696] An "organization" is a group of people who share a common purpose and function based on a certain structure and division of roles.

[0697] "Members" refer to individuals or groups who belong to an organization and contribute to achieving its objectives.

[0698] "Attribute data" refers to data that includes information such as the abilities, qualifications, and history of the members.

[0699] "Machine learning technology" refers to techniques that analyze large amounts of data, extract patterns from it, and use that information for predictions and decision-making.

[0700] "Analysis" refers to the process of processing collected data using statistical methods and algorithms to obtain meaningful information.

[0701] A "staffing plan" is a plan that determines how to allocate members within an organization.

[0702] A "development plan" is a plan for a series of activities aimed at improving the skills and abilities of its members.

[0703] An "information processing device" refers to an electronic device used for inputting, processing, storing, and outputting information.

[0704] "Feedback" refers to information returned to the system, which is used to improve operations and for future analyses.

[0705] This invention is a system that utilizes diverse attribute data of its members to achieve personnel management that takes psychological state into consideration. This system is mainly implemented through the process of collecting, analyzing, proposing, and providing feedback on various data between the server, terminals, and users.

[0706] The server first retrieves attribute data such as members' skills, qualifications, and usage history from the organization's database. This process utilizes SQL database management systems and specific data retrieval APIs. It also uses an emotion engine to collect user voice and facial expression data, and uses machine learning techniques to determine their psychological state based on this data. Speech recognition (e.g., speech processing APIs) and image processing (e.g., OpenCV) are used for analysis.

[0707] All acquired data is integrated and processed by machine learning algorithms on the server. Generative AI models are used to derive appropriate work assignments and training plans for each member. This analysis is expected to utilize libraries such as Scikit-learn and TensorFlow.

[0708] The generated work assignment and training plan proposals are sent to the terminal and reviewed by the user through the interface. For example, a team leader can use the proposed HR strategy to select the most suitable members for a project and improve work efficiency.

[0709] Furthermore, the results of the proposal execution are collected on the terminal, and this data is sent back to the server to provide feedback for the next proposal. This feedback allows for continuous improvement in the accuracy and reliability of the proposals.

[0710] A specific example of a prompt message might be, "Create an optimal workload adjustment plan based on the member's past operational data and current emotional state." This allows the server to generate suggestions that reflect the current psychological state.

[0711] By implementing this system, organizations can expect to achieve flexible and adaptable talent management that takes psychological states into account, thereby maximizing the performance of their members.

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] The server connects to the organization's database and retrieves attribute data such as members' skills, qualifications, and usage history. The input data is extracted using SQL queries, and the output is a dataset organized by member. This dataset will serve as the basis for subsequent integrated analysis.

[0715] Step 2:

[0716] The server uses an emotion engine to collect user voice and facial expression data in real time. This process uses a microphone to record voice and a camera to capture facial expressions. Voice signals and image data are collected as input, and this data is analyzed by an emotion recognition algorithm as output. Specifically, a voice recognition API is used for voice tone analysis, and an image processing library is used for facial expression analysis.

[0717] Step 3:

[0718] The server integrates acquired attribute and sentiment data and applies machine learning algorithms. A unified dataset of member data and sentiments is used as input, and optimal placement and training plans for each member are generated as output. Data processing primarily utilizes libraries such as TensorFlow and Scikit-learn, and generative AI models are used to derive strategies.

[0719] Step 4:

[0720] The generated work assignments and training plans are sent to the terminal, where the user reviews them through the system interface. The generated plans are the input, and the plans are displayed on the user's screen as output. Based on the suggestions, the user can make decisions that meet the organization's needs.

[0721] Step 5:

[0722] The terminal recollects the results of the proposed execution and sends the data to the server. User feedback data is collected as input, and the output becomes analysis data that will be used next. Based on this feedback, the system aims to improve the accuracy of the next proposal. In this process, data trends are identified and categorized, and the content of the proposal is improved.

[0723] (Application Example 2)

[0724] 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".

[0725] While streamlining personnel allocation and development within an organization is crucial for improving corporate productivity, conventional technologies have made it difficult to implement optimal allocation strategies that take into account individual psychological states and workloads. Furthermore, the lack of mechanisms to dynamically adjust tasks in conjunction with automated machinery based on these strategies has led to a growing demand for further efficiency improvements.

[0726] 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.

[0727] In this invention, the server includes means for collecting diverse informational and emotional data about individuals within an organization, means for integrating the informational and emotional data and analyzing it based on a machine learning algorithm, and means for proposing individual placement and development strategies based on the analysis results and reflecting these proposals in automated work machines to adjust tasks. This enables optimal work placement and efficient task shifting based on each individual's skills and psychological state.

[0728] "Diverse information data about individuals within an organization" refers to an individual's skills, qualifications, work history, and other work-related information necessary for evaluation.

[0729] "Emotional data" refers to information about an individual's psychological state obtained by analyzing their facial expressions and voice data.

[0730] A "machine learning algorithm" refers to a program or method used to analyze large amounts of data, find patterns, and make predictions or decisions.

[0731] "Placement strategy" refers to a plan for assigning personnel within an organization to the most suitable teams and projects.

[0732] A "development strategy" refers to a plan for improving an individual's skills and abilities.

[0733] "Automated work machines" refer to mechanical devices used in factories and organizations to perform tasks and operations through automated processes.

[0734] "Task shifting" refers to reassigning some or all of a task or operation to another worker or machine.

[0735] The system that implements this application example is server-centered, collecting individual user information and emotional data and performing integrated analysis. The server retrieves user skills, qualifications, and operational history from a database as information data, and collects facial expression and voice data using cameras and microphones as emotional data. This data is analyzed in real time using Microsoft Azure's Face API and Google Cloud's Speech-to-Text API.

[0736] The server uses machine learning libraries such as TensorFlow and PyTorch to analyze the collected data and evaluate the user's current psychological state. Based on this evaluation, it dynamically adjusts task assignments to automated machines to ensure that workers receive appropriate instructions. The generated suggestions are sent to a communication terminal, which the user can review through the system interface.

[0737] For example, if a worker in a factory shows signs of fatigue, this system can immediately detect this condition and instruct automated machinery to shift the workload. This reduces the burden on the worker and improves productivity.

[0738] Examples of prompts for a generative AI model:

[0739] "Analyze the worker's real-time facial expression and voice data to assess their current psychological state. Based on the assessment results, propose how to adjust their work tasks."

[0740] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0741] Step 1:

[0742] The server retrieves information data about users within the organization. As input, it collects user skills, qualifications, and operational history from a database to construct the information data. As output, it obtains a user information dataset necessary for analysis. This step verifies the completeness and accuracy of the data.

[0743] Step 2:

[0744] The server uses a camera and microphone to collect user emotion data. It takes real-time facial expression and voice data as input. The output provides data for analyzing the user's current psychological state. Here, Microsoft Azure's Face API and Google Cloud's Speech-to-Text API are used to analyze emotions.

[0745] Step 3:

[0746] The server analyzes collected informational and emotional data using machine learning algorithms. The input is the dataset obtained in steps 1 and 2. The output is the user's psychological state evaluation results and, based on these, suggestions for placement and training strategies. This step involves complex data analysis using TensorFlow or PyTorch.

[0747] Step 4:

[0748] The server adjusts task assignments to automated machines based on the analysis results. It uses the evaluation results and suggestions from the previous step as input. The output is an adjusted task instruction designed to reduce the workload on workers. Specifically, it changes the proportion of tasks assigned to the robots, taking into account the current workload of the workers.

[0749] Step 5:

[0750] The server sends the generated proposals and task instructions to the communication terminal. It provides the information obtained in step 4 as input. The information is delivered as output in a format that the user can view through the system interface. This step ensures accurate and rapid information transmission.

[0751] Step 6:

[0752] The terminal and user record the proposed content and its execution results, and send feedback back to the server. This data is then reused for the next analysis, contributing to the improvement of the system's accuracy. The input is the execution results of the proposed content, and the output is feedback data for system improvement.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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."

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] The following is further disclosed regarding the embodiments described above.

[0775] (Claim 1)

[0776] Means for collecting diverse information data about individuals within an organization,

[0777] The aforementioned information data is integrated and analyzed based on a machine learning algorithm,

[0778] Based on the analysis results, we propose individual placement and training strategies and methods.

[0779] Means for transmitting the aforementioned proposal to a communication terminal,

[0780] A means to recollect the results of the aforementioned proposal and feed them back into the next analysis,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, wherein the aforementioned information data includes an individual's skills, qualifications, and operational history.

[0784] (Claim 3)

[0785] The system according to claim 1, wherein the machine learning algorithm uses natural language processing to evaluate an individual's psychological state.

[0786] "Example 1"

[0787] (Claim 1)

[0788] Means for collecting diverse information about individuals belonging to an organization,

[0789] A means for integrating the aforementioned information, converting it into a unified format, and analyzing it using machine learning processing,

[0790] A means for generating and proposing strategies for individual placement and rearing based on analysis results,

[0791] Means for transmitting the above proposal to a display device,

[0792] A means of collecting the results of the aforementioned proposal again and reflecting them in the next analysis,

[0793] A device that includes this.

[0794] (Claim 2)

[0795] The apparatus according to claim 1, wherein the information includes the individual's abilities, qualifications, and history.

[0796] (Claim 3)

[0797] The apparatus according to claim 1, wherein the machine learning process evaluates the psychological tendencies of an individual using natural language processing technology.

[0798] "Application Example 1"

[0799] (Claim 1)

[0800] Means for collecting diverse information data about individuals within an organization,

[0801] The aforementioned information data is integrated and analyzed based on a machine learning algorithm,

[0802] Based on the analysis results, we propose individual placement and training strategies and methods.

[0803] Means for transmitting the aforementioned proposal to an information terminal that displays the proposal,

[0804] A means to recollect the results of the aforementioned proposal and feed them back into the next analysis,

[0805] We propose and implement strategies for arranging equipment and operators to improve the efficiency of the manufacturing process.

[0806] A means of collecting and analyzing work conditions in the manufacturing process in real time,

[0807] A system that includes this.

[0808] (Claim 2)

[0809] The system according to claim 1, wherein the aforementioned information data includes an individual's skills, qualifications, operation history, and operator feedback.

[0810] (Claim 3)

[0811] The system according to claim 1, wherein the machine learning algorithm uses natural language processing to evaluate an individual's psychological state and the operator's feedback.

[0812] "Example 2 of combining an emotion engine"

[0813] (Claim 1)

[0814] Means for obtaining diverse attribute data about members within an organization,

[0815] A means for integrating the aforementioned attribute data and analyzing it based on machine learning technology,

[0816] A means of providing member placement and training plans based on analysis results,

[0817] means for transmitting to an information processing device that displays the aforementioned plan,

[0818] A means to re-obtain the results of the aforementioned plan and feed them back into the next analysis,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, wherein the attribute data includes the members' special skills, qualifications, and usage history.

[0822] (Claim 3)

[0823] The system according to claim 1, wherein the machine learning technology determines the psychological state of the members using voice and image processing.

[0824] "Application example 2 when combining with an emotional engine"

[0825] (Claim 1)

[0826] Means for collecting diverse informational and emotional data about individuals within an organization,

[0827] The aforementioned informational data and emotional data are integrated and analyzed based on a machine learning algorithm,

[0828] Based on the analysis results, we propose individual placement and training strategies, and a means of adjusting tasks by reflecting these proposals in automated work machines.

[0829] Means for transmitting the aforementioned proposal to a communication terminal,

[0830] A means to recollect the results of the aforementioned proposal and feed them back into the next analysis,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, wherein the information data includes an individual's skills, qualifications, operational history, and facial expression data and voice data used for emotion recognition.

[0834] (Claim 3)

[0835] The system according to claim 1, wherein the machine learning algorithm uses natural language processing and emotion recognition to evaluate an individual's psychological state and optimizes task shifting with automated work machines. [Explanation of Symbols]

[0836] 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. Means for collecting diverse information data about individuals within an organization, The aforementioned information data is integrated and analyzed based on a machine learning algorithm, Based on the analysis results, we propose individual placement and training strategies and methods. Means for transmitting the aforementioned proposal to an information terminal that displays the proposal, A means to recollect the results of the aforementioned proposal and feed them back into the next analysis, We propose and implement strategies for arranging equipment and operators to improve the efficiency of the manufacturing process. A means of collecting and analyzing work conditions in the manufacturing process in real time, A system that includes this.

2. The system according to claim 1, wherein the aforementioned information data includes an individual's skills, qualifications, operation history, and operator feedback.

3. The system according to claim 1, wherein the machine learning algorithm uses natural language processing to evaluate an individual's psychological state and the operator's feedback.

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