system

Abstract

We provide the system. [Solution] A data collection means for collecting robot motion information, Data preprocessing means for cleansing and normalizing the collected information, A skill evaluation method that numerically analyzes robot skills based on normalized information, A requirements analysis tool that analyzes the requirements of factory work processes and quantifies the necessary skill sets, A robot recommendation method for selecting the optimal robot by matching skill evaluations with work process requirements, A system including a means for generating and displaying selection results as a report.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] The placement of employees in suitable positions has been a problem in conventional personnel management. In particular, flexible and effective personnel placement according to the organization's strategy is required. However, in conventional methods, objective evaluation based on data is not sufficient, so human bias and personal judgment often intervene. As a result, there have been many cases where the efficiency and competitiveness of the organization are inhibited.

Means for Solving the Problems

[0005] This invention provides a system that includes means for collecting employee work data, means for cleansing and normalizing the data, means for numerically evaluating skills based on the normalized data, means for analyzing project requirements and quantifying the necessary skills, means for matching skill evaluations with project requirements to select the most suitable personnel, and means for generating and displaying the results as a report. This system solves the challenges of conventional human resource management. This system enables objective, data-driven personnel allocation, improving organizational efficiency and competitiveness.

[0006] "Data collection means" refers to devices or programs that have the function of acquiring employee work data from various databases.

[0007] "Data preprocessing means" refers to devices or programs that have the function of cleansing and normalizing collected data and converting it into a format suitable for analysis.

[0008] "Skill evaluation tools" refer to devices or programs that have the function of numerically analyzing employees' skills and experience based on normalized data.

[0009] "Project requirements analysis tools" refer to devices or programs that have the function of analyzing project requirements in order to quantify the skill sets and experience necessary for an organization's projects.

[0010] "Talent recommendation tools" refer to devices or programs that have the function of matching skill assessments with project requirements and recommending the most suitable personnel.

[0011] "Result reporting means" refers to devices or programs that have the function of displaying analysis and recommendation results generated by AI in a report format. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

[0015] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a system that effectively utilizes employee data within an organization, enabling objective and data-driven decision-making in human resource management. This system is comprised of the following means:

[0034] First, the server collects employee work data from the organization's database. This work data includes past project performance, skill information, and evaluation results. Next, the server cleanses and formats this collected data to make it usable by the AI ​​model.

[0035] Next, an AI model, used as a skill assessment tool, analyzes the data and quantifies each employee's skills and experience. This analysis utilizes natural language processing techniques, in particular, to extract useful information from text data. This quantified skill assessment data is then updated in the organization's database.

[0036] As a project requirements analysis tool, the server analyzes the requirements for each project within the organization. The results of this analysis are quantified and registered, including the necessary skill sets and years of experience for each project. This clarifies the technical requirements for each project.

[0037] Next, an AI model, used as a talent recommendation tool, selects the most suitable personnel by matching employee skill data with project requirements. This matching process utilizes machine learning algorithms to determine which employee is best suited for a specific job requirement.

[0038] The selected candidates are generated in report format by the results reporting system. The server generates this report, and the terminal displays it in a format that is easy for the user to view. This report details the skills assessment of the recommended candidates and the project requirements, helping the user make informed decisions.

[0039] For example, if a project requires members with advanced Java skills, the server will identify employees with high Java skills based on past project and current work data and recommend them to the terminal. This consistent process allows the organization to efficiently place the right people in the right positions, thereby increasing the likelihood of project success.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server accesses various databases to collect employee work data. This includes results from past projects, daily work logs, and skill information. The data is retrieved using APIs and SQL queries.

[0043] Step 2:

[0044] The server cleanses and normalizes the business data it collects. Specifically, this includes imputing missing values, removing outliers, and standardizing data formats. This prepares the data for analysis by AI models.

[0045] Step 3:

[0046] The server passes the cleansed data to an AI model to perform skill assessments. The AI ​​analyzes the data and quantifies employees' skills and experience. In particular, natural language processing is used to extract useful information from the text data.

[0047] Step 4:

[0048] The server collects project requirements data from within the organization. This data includes information such as the required skill sets, years of experience, and roles for each project. An AI model analyzes this information and registers it in a database as numerical project requirements.

[0049] Step 5:

[0050] The AI ​​model matches skill assessment data with project requirements. Using machine learning algorithms, it compares employee skills with project needs to select the most suitable personnel.

[0051] Step 6:

[0052] The server generates a report based on the suitability of the personnel selected by the AI. The report shows details of the recommended employee's skills assessment and how well they fit the project requirements.

[0053] Step 7:

[0054] The terminal displays the generated report to the user. Based on the report, the user considers personnel allocation for the project and provides feedback to the system as needed.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In today's business environment, a key challenge is how to effectively utilize the vast amount of employee data an organization possesses to achieve optimal talent placement. Traditional talent management methods involve manual data analysis, which is time-consuming and labor-intensive, and also lack objectivity due to the inclusion of many subjective judgments.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes information acquisition means for obtaining worker performance information, information processing means for organizing and formalizing the acquired information, and ability evaluation means for numerically analyzing the worker's abilities based on the formalized information. This makes it possible to automatically and objectively analyze vast amounts of employee data and quickly recommend the most suitable personnel according to the organization's needs.

[0060] "Worker performance information" refers to data including past work results, skills, and evaluations of employees within an organization.

[0061] "Information acquisition means" refers to a process or device for collecting necessary data from information sources such as databases.

[0062] "Information processing means" refers to methods or systems for cleansing, standardizing, and shaping acquired data into a form suitable for analysis.

[0063] A "competency evaluation system" is a mechanism that uses data analysis technology to quantify a worker's skills and experience and objectively evaluate their abilities.

[0064] "Group work requirements" refer to conditions such as the skills and years of experience required for a project or task.

[0065] "Work requirements analysis methods" are techniques for analyzing project requirements, extracting the necessary capabilities and conditions, and quantifying them.

[0066] "Worker recommendation methods" refer to algorithms and techniques used to identify and recommend employees who are best suited to project requirements, based on collected data.

[0067] A "results display method" refers to a platform or format for providing users with the results of analysis and recommendations in a way that they can view.

[0068] "Natural language processing" refers to the techniques or methods used by computers to understand, analyze, and extract meaningful information from human language.

[0069] In this embodiment of the invention, a server retrieves employee performance information from a database within the organization. Database queries using SQL are generally used to retrieve the data, and suitable database management systems include MySQL® and PostgreSQL. The data retrieved includes work performance, skill evaluations, and roles and performance in past projects.

[0070] The server then organizes and formats the acquired data using information processing tools. This process utilizes libraries such as Pandas, a Python programming language library, to handle missing data and standardize data formats. As a result, the data is transformed into a format that can be analyzed by the AI ​​model.

[0071] Next, the server uses a competency assessment tool to quantify each worker's skills and experience. Natural language processing technology plays a particularly important role at this stage. For example, using Hugging Face's Transformers library, it's possible to extract useful information from text data and calculate skill points.

[0072] To analyze project requirements, the server uses a work requirements analysis tool. Natural language processing technology is employed here to quantify the required skill sets and years of experience from the requirements document text. Through this analysis, the specific abilities and conditions required for each project become clear.

[0073] Ultimately, the server uses worker recommendation mechanisms to match skill assessments with work requirements and select the most suitable personnel. This matching process can utilize machine learning algorithms such as scikit-learn to identify the optimal worker.

[0074] For example, if a project requires advanced programming skills, the server will select the most suitable worker based on past data and recommend them to the terminal. A possible prompt might be something like, "Recommend an employee with Python skill level 7 or higher," which is input into the generating AI model, and the response is used as a reference. This entire process allows organizations to quickly find the right talent and lead projects to success.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server retrieves worker performance information from the organization's database. The input data consists of work results, skill information, and evaluation data stored in the database. SQL queries are used to extract this data, and the retrieved data is loaded into memory. This process prepares the raw data for analysis, allowing the server to proceed to the next step.

[0078] Step 2:

[0079] The server formats the acquired raw data using information processing tools. The input data is the performance information acquired in step 1. Specifically, the Python Pandas library is used to impute missing values ​​and standardize the data format. For example, missing skill evaluations are imputed with the average value. This process creates a dataset that the AI ​​model can analyze.

[0080] Step 3:

[0081] The server uses a competency assessment tool to quantify the skills and experience of workers based on the formatted data. The input data is the data formatted in step 2. In this process, natural language processing is used to extract useful information from the text. Self-assessment statements are analyzed using tools such as Hugging Face's Transformers, and skill points are calculated. The output is quantified skill assessment data.

[0082] Step 4:

[0083] The server uses a work requirements analysis tool to analyze project requirements. The input data is the project requirements document within the organization. Natural language processing technology is used to quantify required skill sets and years of experience, generating quantified skill requirements information. This clarifies the specific capabilities and conditions for each project.

[0084] Step 5:

[0085] The server uses a worker recommendation system to match skill assessment data with project requirements. The input data consists of skill assessment data from step 3 and skill requirement information from step 4. Machine learning algorithms such as scikit-learn are applied to identify and select the most suitable worker. The output is a list of the workers best suited to the project.

[0086] Step 6:

[0087] The server generates a report based on the selection results and displays it on the terminal. The input data is the list of workers created in step 5. The generated report details the skills evaluation and suitability of the selected workers for the project. The terminal displays this in a user-friendly format to help the user make the best decision.

[0088] (Application Example 1)

[0089] 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."

[0090] In factories, multiple robots are required to function efficiently and optimally in increasingly complex work environments. However, accurately analyzing the skills of robots and the requirements of each work process, and then appropriately deploying them, is difficult. Therefore, there is a need for a system that quantitatively evaluates robot skills and work process requirements and uses that evaluation to support optimal robot deployment.

[0091] 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.

[0092] In this invention, the server includes data collection means for collecting robot motion information, data preprocessing means for cleansing and normalizing the collected information, and skill evaluation means for numerically analyzing the robot's skills based on the normalized information. This enables the optimal placement of robots within the factory.

[0093] "Robot operation information" refers to all data related to the status and operation of robots operating within a factory.

[0094] "Data collection means" refers to a device or process for efficiently collecting robot operation information.

[0095] "Data preprocessing means" refers to methods and techniques for organizing and arranging collected robot motion information into an analyzable format.

[0096] "Skill evaluation method" refers to a technology for numerically evaluating the capabilities and efficiency of each robot based on robot motion data.

[0097] "Requirements analysis means" refers to a technology that analyzes the necessary skills and capabilities for each work process in a factory and presents the results as numerical values.

[0098] "Robot recommendation methods" refer to technologies that select and propose the most suitable robot based on skill evaluations and work process requirements.

[0099] "Result reporting method" refers to a method for displaying the recommended placement of selected robots in a way that is easily understandable to the user.

[0100] "Smart glasses and head-mounted displays" refer to electronic devices that users wear and use to receive information visually.

[0101] "User verification means" refers to an interface that allows users to provide opinions and feedback on the system's output.

[0102] This invention is a system for achieving the effective and optimal placement of robots in a factory. A server uses data collection means to collect operational information from each robot in the factory. This information relates to the robot's skills and operating status. Next, the collected data is cleansed and normalized by data preprocessing means. This preprocessing can utilize Python's Pandas library or NumPy.

[0103] After the data has been formatted, the server uses skill evaluation tools to quantify the capabilities of each robot. Suitable AI models for this process include machine learning frameworks such as TENSORFLOW® and PyTorch. These tools extract useful information from the robot's motion data.

[0104] Subsequently, the server uses requirements analysis tools to analyze and quantify the technical requirements for each work process in the factory. This clarifies the robot skills required for a specific work process. Finally, the robot recommendation tool selects the optimal robot based on the results of the skill evaluation and requirements analysis.

[0105] The placement information for the selected robots is generated by a results reporting system and displayed on smart glasses or a head-mounted display. Users can visually confirm this information and provide feedback to the system. For example, a system can be built to recommend the optimal robot for a specific part assembly process. In this case, an example of a prompt message to the generated AI model would be, "Analyze the operation data of the robots currently in use on the factory line, evaluate their handling skills for specific tasks, and select the optimal robot based on the next work plan."

[0106] In this way, appropriate and efficient robot placement is achieved, improving factory productivity.

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

[0108] Step 1:

[0109] The server collects operational and motion data from robots within the factory. Input is real-time motion data from each robot, and output is collected raw data. This data includes sensor information, operating time, and work details.

[0110] Step 2:

[0111] The server cleanses and normalizes the collected data using data preprocessing tools. The input is the collected raw data, and the output is formatted data that can be analyzed by the AI ​​model. Here, missing values ​​are imputed using Python's Pandas and NumPy, and preprocessing such as scaling is performed as needed.

[0112] Step 3:

[0113] The server uses an AI model to quantify the skills of each robot based on the formatted data. The input is the formatted data, and the output is data that quantifies the skills of each robot. TensorFlow and PyTorch are used for the AI ​​model, and skills are evaluated based on the performance the robots have shown in past tasks.

[0114] Step 4:

[0115] The server analyzes the factory's work process requirements and quantifies the necessary skill sets. The input is factory process information, and the output is quantified information on the skills required for each process. Based on project specifications and requirements, it utilizes natural language processing techniques to analyze the requirements.

[0116] Step 5:

[0117] The server matches the optimal robot based on skill evaluation data and work process requirements data. The input is quantified skill data and requirements data, and the output is the recommendation of the optimal robot. Here, a machine learning algorithm is used to select the robot that best meets the given requirements.

[0118] Step 6:

[0119] The terminal outputs the generated recommendation results as a report, which is displayed to the user via smart glasses or a head-mounted display. The input is data on the recommended robots, and the output is a report that the user can visually review. Web technologies are used for the user interface, providing information in real time.

[0120] 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.

[0121] This invention combines an emotion engine with a system for analyzing employee data and optimizing personnel allocation, thereby recognizing user emotions and achieving more flexible and effective personnel management. This system mainly consists of the following means.

[0122] First, the server collects employee work data from various databases. This data includes past project performance, skill information, and work history. Next, the server cleanses this data and processes it into a format suitable for AI analysis.

[0123] Next, the server uses an AI model to quantify employee skills from the collected data. This involves a process that leverages natural language processing techniques to extract insights from text data. This skills assessment data is updated in the organization's database, ensuring it is always up-to-date.

[0124] Meanwhile, the server quantifies the skills and experience required for the project using project requirements analysis tools. This clarifies the technical requirements for each project. Next, the AI ​​model matches the skill evaluation data with the project requirements and selects the most suitable personnel.

[0125] Furthermore, this system incorporates an emotion engine that analyzes user feedback and identifies emotions. The terminal sends user feedback to the emotion engine, which then identifies the user's emotional state. This information influences skill evaluations and talent recommendations, and is used to adjust recommendation results.

[0126] The generated talent recommendation results are drafted by the server in report format. This report is presented to the user via their device in an appropriate manner based on their emotional state. Specifically, if the user's emotional state is positive, detailed technical information is emphasized, while if their emotional state is negative, more supportive information is provided.

[0127] For example, if progress on an existing project is unsatisfactory, the server uses an emotion engine to detect the user's stress and provide more helpful guidance to help the user make optimal decisions. This entire process enables organizations to implement strategic talent allocation more effectively.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server accesses various databases within the organization to collect employee work data. This includes project performance records, skill information, and past work history. An API is used to retrieve the necessary data.

[0131] Step 2:

[0132] The server cleanses the collected data, removing noise and imputing missing values, and converts it to a standard format. This makes the data suitable for analysis by AI models.

[0133] Step 3:

[0134] The server uses an AI model to analyze normalized data and quantify each employee's skills. Natural language processing techniques are used to extract meaningful skill-related information from text data.

[0135] Step 4:

[0136] The server collects project requirements within the organization and quantifies the necessary skill sets and experience. It analyzes information input from project management systems and other sources to generate precise technical requirements.

[0137] Step 5:

[0138] An AI model matches skill assessment data with project requirements. Using machine learning techniques, it compares each employee's skills with project needs to select the most suitable personnel.

[0139] Step 6:

[0140] The server generates a report based on the selected personnel. The report includes an assessment of employee skills and their suitability for project requirements, and is prepared in an easy-to-read format.

[0141] Step 7:

[0142] The device receives user feedback and analyzes it using an emotion engine. It analyzes user input as text and dynamically determines the level of positive or negative emotion.

[0143] Step 8:

[0144] The emotion engine assesses the user's emotions and uses this information to adjust skill evaluations and talent recommendations. For example, if a user is feeling stressed, it presents information in a way that allows them to understand it in a relaxed state.

[0145] Step 9:

[0146] The device displays a tailored recommendation report. Users receive reports in different formats based on their emotions, and more comprehensive and supportive information is provided to help them make decisions.

[0147] In this way, the present invention enables data-driven personnel allocation while recognizing user emotions.

[0148] (Example 2)

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

[0150] In organizations, the appropriate allocation of talent is a critical issue directly linked to operational efficiency and project success. However, traditional methods have made it difficult to quantitatively evaluate employee skills and quickly and accurately match them to project requirements. Furthermore, talent allocation that takes employee emotions and feedback into account has not been achieved. Therefore, a new system is needed that enables flexible and effective talent management.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes information gathering means for collecting employee work information, information preprocessing means for cleansing and normalizing the collected information, and skill evaluation means for numerically analyzing the employee's skills based on the normalized information. This makes it possible to quantitatively evaluate employee skills and recommend the most suitable personnel after adjustments based on sentiment analysis.

[0153] "Information gathering methods" refer to techniques for obtaining information related to employees' work from various data sources.

[0154] "Information preprocessing means" refers to the process of cleansing and normalizing collected raw data and preparing it in a format suitable for analysis.

[0155] A "skill evaluation method" is a method of quantitatively evaluating an employee's skills and abilities numerically based on normalized data.

[0156] "Planning requirements analysis methods" are processes that quantify the skills and experience required for an organization's projects and compare them with skill assessments.

[0157] "Talent recommendation methods" refer to the process of matching evaluated skills with project requirements to select the most suitable personnel.

[0158] The "results reporting method" is a function that generates a report containing information about the selected personnel and displays that information.

[0159] "Emotional analysis methods" are technologies that analyze user feedback to understand their emotions and their emotional state.

[0160] "Result adjustment means" refers to a function that adjusts the recommended personnel and information according to the user's state, based on the results of sentiment analysis.

[0161] A description of embodiments for carrying out the present invention will be provided.

[0162] In this system, the server plays a central role in acquiring employee work information from various databases using information gathering tools. This includes hardware such as cloud storage and internal databases, and database management software such as SQL is used. After this, the server uses information preprocessing tools to cleanse and normalize the collected data and convert it into an appropriate format for analysis.

[0163] Next, the server uses software that leverages natural language processing technology to execute the skills assessment and analyze the normalized data. This software is based on a generative AI model and has the ability to analyze text data and quantify employee skills.

[0164] The server also uses a planning requirements analysis tool to analyze project requirements and quantify the necessary skill sets. Based on this information, a talent recommendation tool operates, matching skill assessment results with project requirements to select the most suitable personnel.

[0165] Furthermore, the terminal receives feedback from the user and identifies the user's emotions through sentiment analysis. This involves determining whether the user's emotions are positive or negative through text analysis. Based on this sentiment information, the server uses result adjustment means to adjust the recommendation results according to the user's emotions.

[0166] For example, if an existing project is stalled, the server can sense the user's stress level from their feedback and provide detailed technical information and support to reduce that stress based on the recommendations.

[0167] An example of a prompt message would be, "The project is stalled; please suggest advice to alleviate the user's concerns."

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The server uses information gathering tools to retrieve employee work information from various databases. Inputs are queries based on employee IDs and project IDs, while outputs include employee data such as past project performance, skill information, and work history. At this stage, the server queries each database for the necessary information and then integrates it.

[0171] Step 2:

[0172] The server cleanses and normalizes the collected data using preprocessing tools. The input is the output data from step 1, and the output is clean data with outliers and duplicates removed. This step performs specific data processing such as unifying data types, handling missing values, and unifying formats.

[0173] Step 3:

[0174] The server executes a skill assessment using a generative AI model, quantifying employee skills from cleansed data. The input is the output data from step 2, and the output is numerical data representing skills. This includes extracting skill information by performing text mining from work reports and feedback comments using natural language processing.

[0175] Step 4:

[0176] The server utilizes planning requirements analysis tools to analyze project requirements and quantify the necessary skill sets. The input is a specific project-based requirements specification, and the output is numerical data indicating the skills required for the project. This analysis includes quantifying relevant technical elements and experience requirements.

[0177] Step 5:

[0178] The server utilizes talent recommendation tools to match skill assessments with project requirements and select the most suitable personnel. The input is the numerical data from steps 3 and 4, and the output is a list of the most suitable employees. Here, a scoring algorithm is used to perform specific data calculations that compare each employee's skill score with the project requirements.

[0179] Step 6:

[0180] The device acquires user feedback and identifies the emotional state using sentiment analysis tools. Input is user comments and reviews, and output is quantitative data indicating emotion. This process utilizes text analysis techniques to determine and quantify the positive / negative nature of the emotion.

[0181] Step 7:

[0182] The server uses result adjustment mechanisms based on the sentiment analysis results to adjust the recommendation results to suit the user's state. The input is the sentiment data from step 6, and the output is the adjusted list of recommended personnel. This stage includes adding supportive content and adjusting the emphasis of detailed information.

[0183] Step 8:

[0184] The server generates a report based on the talent recommendation results and presents it to the user via the terminal. The input is the adjusted recommendation results, and the output is a report appropriately formatted for the user. The final report includes information with emphasis adjusted according to the user's sentiment.

[0185] (Application Example 2)

[0186] 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".

[0187] In the face of the need to improve worker productivity and reduce workload at the worksite, achieving optimal work assignments that take into account workers' emotional states has been difficult. Furthermore, analyzing workers' skills and emotional states in real time to optimize work efficiency has also been challenging.

[0188] 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.

[0189] In this invention, the server includes information gathering means for collecting worker work information, emotion recognition means for recognizing the worker's emotional state and adjusting work assignments, and automatic work adjustment means for autonomously distributing tasks based on emotion recognition. This enables flexible and efficient work assignments based on the worker's emotional state.

[0190] "Information gathering means" refers to methods and devices for collecting worker work information, and has the function of acquiring worker work performance and behavioral data.

[0191] "Information preprocessing means" refers to methods or devices used to cleanse and normalize collected information, and are responsible for converting it into a format suitable for data analysis.

[0192] A "technical evaluation tool" is a method or device for numerically evaluating a worker's skills based on normalized information, and is used to quantitatively measure a worker's skills and abilities.

[0193] A "process requirements analysis tool" is a method or device for analyzing an organization's process requirements and quantifying the necessary set of technologies, and is used to clarify the technologies and experience required for a specific task or project.

[0194] A "worker recommendation system" is a method or device for selecting the most suitable worker by matching technical evaluations with process requirements, and has the function of identifying the worker best suited to the project or task.

[0195] "Result reporting means" refers to a method or apparatus for generating and displaying selection results as a report, and provides a means for communicating analysis results to users.

[0196] "Emotional recognition means" refers to a method or device for recognizing the emotional state of workers and adjusting their work assignments, and is used to understand the mental state of workers and to achieve an appropriate work assignment for the work environment.

[0197] "Automatic work adjustment means" refers to a method or device for autonomously distributing tasks based on emotion recognition, in which robots or other devices support the work according to the worker's condition, thereby assisting in efficient work execution.

[0198] In the system that implements this application example, the server plays a central role. First, the server collects work information from each worker using information gathering means. This information includes the worker's performance data and work history. Next, the collected data is cleansed and normalized by information preprocessing means and converted into a format suitable for analysis.

[0199] The server uses technical evaluation tools to quantify the skills of workers from normalized data. This process employs natural language processing techniques to gain insights from workers' comments and reports. The technical data thus evaluated is matched with the organization's process requirements, which are quantified by process requirements analysis tools, and the most suitable workers are selected through worker recommendation tools.

[0200] Furthermore, this system utilizes emotion recognition technology to analyze the emotional state of workers in real time. Based on this information, the server uses an automated work adjustment mechanism to distribute tasks among robots as needed, thereby achieving efficient work.

[0201] As a concrete example, if a worker is feeling fatigued, the server detects this state using emotion recognition and uses an automated task adjustment system to assign tasks to robots in a way that reduces the worker's burden. For instance, assigning heavy lifting tasks to robots reduces the worker's workload.

[0202] Prompts utilizing the generative AI model, such as "Collect recent emotional feedback from employee A and analyze it with the emotion engine. In particular, suggest how to optimize task assignment when the employee is experiencing high stress levels," can be used to achieve more precise emotion recognition and task allocation. This approach makes it possible to improve efficiency in the workplace and the comfort of workers.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The server collects work information from each worker using information gathering means. Specifically, it acquires worker performance data and work history from sensors and input devices and stores them in the system. The input in this step is raw work data, and the output is that data being incorporated into the system.

[0206] Step 2:

[0207] The server cleanses and normalizes the collected data using preprocessing tools. This includes noise reduction and data format matching. The input is the raw data collected in step 1, and the output is the cleansed and normalized data. This data is then prepared for smooth processing in subsequent analysis steps.

[0208] Step 3:

[0209] The server uses technical evaluation tools to quantify the worker's skills from normalized data. Specifically, it employs natural language processing algorithms to extract insights from text data. The input here is the data obtained in step 2, and the output is numerical data indicating the worker's skills.

[0210] Step 4:

[0211] The server uses process requirements analysis tools to quantify the organization's process requirements. It analyzes project details and clarifies the necessary skill sets. The input in this step is project specifications, etc., and the output is a list of quantified skills.

[0212] Step 5:

[0213] The server uses a worker recommendation system to match technical evaluations with process requirements and select the most suitable worker. This process uses a selection algorithm to identify the most suitable worker. The input is the numerical data obtained in steps 3 and 4, and the output is a list of selected workers.

[0214] Step 6:

[0215] The server recognizes the worker's emotional state using emotion recognition means. It identifies the worker's emotions through facial pattern recognition and voice analysis. The input for this step is real-time data from the worker, and the output is the evaluation result of their emotional state.

[0216] Step 7:

[0217] The server uses automated task adjustment mechanisms to automate tasks based on the results of emotion recognition. Through instructions to the robots, it appropriately distributes or redistributes tasks. The input is the emotion evaluation from step 6, and the output is the adjusted task process. This reduces the burden on the workers.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] This invention provides a system that effectively utilizes employee data within an organization, enabling objective and data-driven decision-making in human resource management. This system is comprised of the following means:

[0235] First, the server collects employee work data from the organization's database. This work data includes past project performance, skill information, and evaluation results. Next, the server cleanses and formats this collected data to make it usable by the AI ​​model.

[0236] Next, an AI model, used as a skill assessment tool, analyzes the data and quantifies each employee's skills and experience. This analysis utilizes natural language processing techniques, in particular, to extract useful information from text data. This quantified skill assessment data is then updated in the organization's database.

[0237] As a project requirements analysis tool, the server analyzes the requirements for each project within the organization. The results of this analysis are quantified and registered, including the necessary skill sets and years of experience for each project. This clarifies the technical requirements for each project.

[0238] Next, an AI model, used as a talent recommendation tool, selects the most suitable personnel by matching employee skill data with project requirements. This matching process utilizes machine learning algorithms to determine which employee is best suited for a specific job requirement.

[0239] The selected candidates are generated in report format by the results reporting system. The server generates this report, and the terminal displays it in a format that is easy for the user to view. This report details the skills assessment of the recommended candidates and the project requirements, helping the user make informed decisions.

[0240] For example, if a project requires a member with advanced Java skills, the server will identify employees with high Java skills based on past project and current work data and recommend them to the terminal. This consistent process allows the organization to efficiently place the right people in the right positions, thereby increasing the likelihood of project success.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The server accesses various databases to collect employee work data. This includes results from past projects, daily work logs, and skill information. The data is retrieved using APIs and SQL queries.

[0244] Step 2:

[0245] The server cleanses and normalizes the business data it collects. Specifically, this includes imputing missing values, removing outliers, and standardizing data formats. This prepares the data for analysis by AI models.

[0246] Step 3:

[0247] The server passes the cleansed data to an AI model to perform skill assessments. The AI ​​analyzes the data and quantifies employees' skills and experience. In particular, natural language processing is used to extract useful information from the text data.

[0248] Step 4:

[0249] The server collects project requirements data from within the organization. This data includes information such as the required skill sets, years of experience, and roles for each project. An AI model analyzes this information and registers it in a database as numerical project requirements.

[0250] Step 5:

[0251] The AI ​​model matches skill assessment data with project requirements. Using machine learning algorithms, it compares employee skills with project needs to select the most suitable personnel.

[0252] Step 6:

[0253] The server generates a report based on the suitability of the personnel selected by the AI. The report shows details of the recommended employee's skills assessment and how well they fit the project requirements.

[0254] Step 7:

[0255] The terminal displays the generated report to the user. Based on the report, the user considers personnel allocation for the project and provides feedback to the system as needed.

[0256] (Example 1)

[0257] 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."

[0258] In today's business environment, a key challenge is how to effectively utilize the vast amount of employee data an organization possesses to achieve optimal talent placement. Traditional talent management methods involve manual data analysis, which is time-consuming and labor-intensive, and also lack objectivity due to the inclusion of many subjective judgments.

[0259] 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.

[0260] In this invention, the server includes information acquisition means for obtaining worker performance information, information processing means for organizing and formalizing the acquired information, and ability evaluation means for numerically analyzing the worker's abilities based on the formalized information. This makes it possible to automatically and objectively analyze vast amounts of employee data and quickly recommend the most suitable personnel according to the organization's needs.

[0261] "Worker performance information" refers to data including past work results, skills, and evaluations of employees within an organization.

[0262] "Information acquisition means" refers to a process or device for collecting necessary data from information sources such as databases.

[0263] "Information processing means" refers to methods or systems for cleansing, standardizing, and shaping acquired data into a form suitable for analysis.

[0264] A "competency evaluation system" is a mechanism that uses data analysis technology to quantify a worker's skills and experience and objectively evaluate their abilities.

[0265] "Group work requirements" refer to conditions such as the skills and years of experience required for a project or task.

[0266] "Work requirements analysis methods" are techniques for analyzing project requirements, extracting the necessary capabilities and conditions, and quantifying them.

[0267] "Worker recommendation methods" refer to algorithms and techniques used to identify and recommend employees who are best suited to project requirements, based on collected data.

[0268] A "results display method" refers to a platform or format for providing users with the results of analysis and recommendations in a way that they can view.

[0269] "Natural language processing" refers to the techniques or methods used by computers to understand, analyze, and extract meaningful information from human language.

[0270] In this embodiment of the invention, a server retrieves employee performance information from a database within the organization. Database queries using SQL are generally used to retrieve the data, and MySQL or PostgreSQL are suitable database management systems. The data retrieved includes work performance, skill evaluations, and roles and performance in past projects.

[0271] The server then organizes and formats the acquired data using information processing tools. This process utilizes libraries such as Pandas, a Python programming language library, to handle missing data and standardize data formats. As a result, the data is transformed into a format that can be analyzed by the AI ​​model.

[0272] Next, the server uses a competency assessment tool to quantify each worker's skills and experience. Natural language processing technology plays a particularly important role at this stage. For example, using Hugging Face's Transformers library, it's possible to extract useful information from text data and calculate skill points.

[0273] To analyze project requirements, the server uses a work requirements analysis tool. Natural language processing technology is employed here to quantify the required skill sets and years of experience from the requirements document text. Through this analysis, the specific abilities and conditions required for each project become clear.

[0274] Ultimately, the server uses worker recommendation mechanisms to match skill assessments with work requirements and select the most suitable personnel. This matching process can utilize machine learning algorithms such as scikit-learn to identify the optimal worker.

[0275] For example, if a project requires advanced programming skills, the server will select the most suitable worker based on past data and recommend them to the terminal. A possible prompt might be something like, "Recommend an employee with Python skill level 7 or higher," which is input into the generating AI model, and the response is used as a reference. This entire process allows organizations to quickly find the right talent and lead projects to success.

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The server retrieves the performance information of workers from the database within the organization. The input data is the business results, skill information, and evaluation data stored in the database. These data are extracted using SQL queries and the retrieved data is loaded into memory. Through this process, the server prepares the raw data to be analyzed and proceeds to the next step.

[0279] Step 2:

[0280] The server formats the retrieved raw data using information processing means. The input data is the performance information obtained in Step 1. As specific operations, the Pandas library in Python is used to complement missing values and standardize data formats. For example, the missing skill evaluations are complemented with the average value. Through this processing, a dataset that can be analyzed by the AI model is created.

[0281] Step 3:

[0282] Based on the formatted data, the server quantifies the skills and experience of workers using the ability evaluation means. The input data is the data formatted in Step 2. In this process, natural language processing is utilized to extract useful information from the text. Transformers from Hugging Face, etc. are used to analyze the self-evaluation sentences and calculate the skill points. The output is the quantified skill evaluation data.

[0283] Step 4:

[0284] The server analyzes the requirements of the project using the work requirement analysis means. The input data is the project requirement document within the organization. By utilizing natural language processing technology, the required skill set, number of years of experience, etc. are quantified to generate the quantified skill requirement information. By doing so, the specific capabilities and conditions of each project are clarified.

[0285] Step 5:

[0286] The server uses worker recommendation means to match the skill evaluation data with the project requirements. The input data is the skill evaluation data in step 3 and the skill requirement information in step 4. Apply machine learning algorithms such as scikit-learn to identify and select the optimal worker. The output is a list of workers most suitable for the project.

[0287] Step 6:

[0288] The server generates a report based on the selection result and displays it on the terminal. The input data is the list of workers created in step 5. The generated report details the skill evaluation of the selected workers and their suitability for the project. The terminal supports the user in making an optimal decision by presenting this in an easy-to-understand format.

[0289] (Application Example 1)

[0290] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] In a factory, multiple robots are required to function efficiently and optimally in an increasingly complex working environment. However, it is difficult to accurately analyze the skills of the robots and the requirements of each work process and make appropriate arrangements. Therefore, there is a need for a system that quantitatively evaluates the skills of robots and the requirements of work processes and supports optimal robot placement based on this.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0293] In this invention, the server includes data collection means for collecting the operation information of robots, data preprocessing means for cleansing and normalizing the collected information, and skill evaluation means for numerically analyzing the skills of robots based on the normalized information. This enables optimal placement of robots within the factory.

[0294] "Robot operation information" refers to all data related to the status and operation of robots operating within a factory.

[0295] "Data collection means" refers to a device or process for efficiently collecting robot operation information.

[0296] "Data preprocessing means" refers to methods and techniques for organizing and arranging collected robot motion information into an analyzable format.

[0297] "Skill evaluation method" refers to a technology for numerically evaluating the capabilities and efficiency of each robot based on robot motion data.

[0298] "Requirements analysis means" refers to a technology that analyzes the necessary skills and capabilities for each work process in a factory and presents the results as numerical values.

[0299] "Robot recommendation methods" refer to technologies that select and propose the most suitable robot based on skill evaluations and work process requirements.

[0300] "Result reporting method" refers to a method for displaying the recommended placement of selected robots in a way that is easily understandable to the user.

[0301] "Smart glasses and head-mounted displays" refer to electronic devices that users wear and use to receive information visually.

[0302] "User verification means" refers to an interface that allows users to provide opinions and feedback on the system's output.

[0303] This invention is a system that realizes an effective and optimal arrangement of robots in a factory. The server uses data collection means for collecting operation information from each robot in the factory. This information relates to the skills and operating status of the robots. Next, the collected data is cleansed and normalized by data preprocessing means. For this preprocessing, libraries such as Python's Pandas library and NumPy can be utilized.

[0304] After the data is formatted, the server uses skill evaluation means to quantify the capabilities of each robot. As the AI models used here, TensorFlow and PyTorch of machine learning frameworks are suitable. Through these, effective information is extracted from the operation data of the robots.

[0305] After that, the server uses requirement analysis means to analyze and quantify the technical requirements necessary for each work process in the factory. This clarifies the skills of the robots required for a specific work process. Finally, based on the results of skill evaluation and requirement analysis, the robot recommendation means selects the optimal robot.

[0306] The arrangement information of the selected robots is generated by the result reporting means and displayed on smart glasses or head-mounted displays. The user can visually confirm this information and can also provide feedback to the system. As a specific example, in the process of assembling a specific part, a system that recommends an optimal robot can be constructed. At this time, examples of prompt sentences for the generation AI model include "Analyze the operation data of the robots in use on the factory line, evaluate the handling skills for a specific task, and select the optimal robot based on the next work plan."

[0307] In this way, an appropriate and efficient arrangement of robots is realized, improving the productivity of the factory.

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The server collects operational and motion data from robots within the factory. Input is real-time motion data from each robot, and output is collected raw data. This data includes sensor information, operating time, and work details.

[0311] Step 2:

[0312] The server cleanses and normalizes the collected data using data preprocessing tools. The input is the collected raw data, and the output is formatted data that can be analyzed by the AI ​​model. Here, missing values ​​are imputed using Python's Pandas and NumPy, and preprocessing such as scaling is performed as needed.

[0313] Step 3:

[0314] The server uses an AI model to quantify the skills of each robot based on the formatted data. The input is the formatted data, and the output is data that quantifies the skills of each robot. TensorFlow and PyTorch are used for the AI ​​model, and skills are evaluated based on the performance the robots have shown in past tasks.

[0315] Step 4:

[0316] The server analyzes the factory's work process requirements and quantifies the necessary skill sets. The input is factory process information, and the output is quantified information on the skills required for each process. Based on project specifications and requirements, it utilizes natural language processing techniques to analyze the requirements.

[0317] Step 5:

[0318] The server matches the optimal robot based on skill evaluation data and work process requirements data. The input is quantified skill data and requirements data, and the output is the recommendation of the optimal robot. Here, a machine learning algorithm is used to select the robot that best meets the given requirements.

[0319] Step 6:

[0320] The terminal outputs the generated recommendation results as a report, which is displayed to the user via smart glasses or a head-mounted display. The input is data on the recommended robots, and the output is a report that the user can visually review. Web technologies are used for the user interface, providing information in real time.

[0321] 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.

[0322] This invention combines an emotion engine with a system for analyzing employee data and optimizing personnel allocation, thereby recognizing user emotions and achieving more flexible and effective personnel management. This system mainly consists of the following means.

[0323] First, the server collects employee work data from various databases. This data includes past project performance, skill information, and work history. Next, the server cleanses this data and processes it into a format suitable for AI analysis.

[0324] Next, the server uses an AI model to quantify employee skills from the collected data. This involves a process that leverages natural language processing techniques to extract insights from text data. This skills assessment data is updated in the organization's database, ensuring it is always up-to-date.

[0325] Meanwhile, the server quantifies the skills and experience required for the project using project requirements analysis tools. This clarifies the technical requirements for each project. Next, the AI ​​model matches the skill evaluation data with the project requirements and selects the most suitable personnel.

[0326] Furthermore, this system incorporates an emotion engine that analyzes user feedback and identifies emotions. The terminal sends user feedback to the emotion engine, which then identifies the user's emotional state. This information influences skill evaluations and talent recommendations, and is used to adjust recommendation results.

[0327] The generated talent recommendation results are drafted by the server in report format. This report is presented to the user via their device in an appropriate manner based on their emotional state. Specifically, if the user's emotional state is positive, detailed technical information is emphasized, while if their emotional state is negative, more supportive information is provided.

[0328] For example, if progress on an existing project is unsatisfactory, the server uses an emotion engine to detect the user's stress and provide more helpful guidance to help the user make optimal decisions. This entire process enables organizations to implement strategic talent allocation more effectively.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The server accesses various databases within the organization to collect employee work data. This includes project performance records, skill information, and past work history. An API is used to retrieve the necessary data.

[0332] Step 2:

[0333] The server cleanses the collected data, removing noise and imputing missing values, and converts it to a standard format. This makes the data suitable for analysis by AI models.

[0334] Step 3:

[0335] The server uses an AI model to analyze normalized data and quantify each employee's skills. Natural language processing techniques are used to extract meaningful skill-related information from text data.

[0336] Step 4:

[0337] The server collects project requirements within the organization and quantifies the necessary skill sets and experience. It analyzes information input from project management systems and other sources to generate precise technical requirements.

[0338] Step 5:

[0339] An AI model matches skill assessment data with project requirements. Using machine learning techniques, it compares each employee's skills with project needs to select the most suitable personnel.

[0340] Step 6:

[0341] The server generates a report based on the selected personnel. The report includes an assessment of employee skills and their suitability for project requirements, and is prepared in an easy-to-read format.

[0342] Step 7:

[0343] The device receives user feedback and analyzes it using an emotion engine. It analyzes user input as text and dynamically determines the level of positive or negative emotion.

[0344] Step 8:

[0345] The emotion engine assesses the user's emotions and uses this information to adjust skill evaluations and talent recommendations. For example, if a user is feeling stressed, it presents information in a way that allows them to understand it in a relaxed state.

[0346] Step 9:

[0347] The device displays a tailored recommendation report. Users receive reports in different formats based on their emotions, and more comprehensive and supportive information is provided to help them make decisions.

[0348] In this way, the present invention enables data-driven personnel allocation while recognizing user emotions.

[0349] (Example 2)

[0350] 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".

[0351] In organizations, the appropriate allocation of talent is a critical issue directly linked to operational efficiency and project success. However, traditional methods have made it difficult to quantitatively evaluate employee skills and quickly and accurately match them to project requirements. Furthermore, talent allocation that takes employee emotions and feedback into account has not been achieved. Therefore, a new system is needed that enables flexible and effective talent management.

[0352] 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.

[0353] In this invention, the server includes information gathering means for collecting employee work information, information preprocessing means for cleansing and normalizing the collected information, and skill evaluation means for numerically analyzing the employee's skills based on the normalized information. This makes it possible to quantitatively evaluate employee skills and recommend the most suitable personnel after adjustments based on sentiment analysis.

[0354] "Information gathering methods" refer to techniques for obtaining information related to employees' work from various data sources.

[0355] "Information preprocessing means" refers to the process of cleansing and normalizing collected raw data and preparing it in a format suitable for analysis.

[0356] A "skill evaluation method" is a method of quantitatively evaluating an employee's skills and abilities numerically based on normalized data.

[0357] "Planning requirements analysis methods" are processes that quantify the skills and experience required for an organization's projects and compare them with skill assessments.

[0358] "Talent recommendation methods" refer to the process of matching evaluated skills with project requirements to select the most suitable personnel.

[0359] The "results reporting method" is a function that generates a report containing information about the selected personnel and displays that information.

[0360] "Emotional analysis methods" are technologies that analyze user feedback to understand their emotions and their emotional state.

[0361] "Result adjustment means" refers to a function that adjusts the recommended personnel and information according to the user's state, based on the results of sentiment analysis.

[0362] A description of embodiments for carrying out the present invention will be provided.

[0363] In this system, the server plays a central role in acquiring employee work information from various databases using information gathering tools. This includes hardware such as cloud storage and internal databases, and database management software such as SQL is used. After this, the server uses information preprocessing tools to cleanse and normalize the collected data and convert it into an appropriate format for analysis.

[0364] Next, the server uses software that leverages natural language processing technology to execute the skills assessment and analyze the normalized data. This software is based on a generative AI model and has the ability to analyze text data and quantify employee skills.

[0365] The server also uses a planning requirements analysis tool to analyze project requirements and quantify the necessary skill sets. Based on this information, a talent recommendation tool operates, matching skill assessment results with project requirements to select the most suitable personnel.

[0366] Furthermore, the terminal receives feedback from the user and identifies the user's emotions through sentiment analysis. This involves determining whether the user's emotions are positive or negative through text analysis. Based on this sentiment information, the server uses result adjustment means to adjust the recommendation results according to the user's emotions.

[0367] For example, if an existing project is stalled, the server can sense the user's stress level from their feedback and provide detailed technical information and support to reduce that stress based on the recommendations.

[0368] An example of a prompt message would be, "The project is stalled; please suggest advice to alleviate the user's concerns."

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The server uses information gathering tools to retrieve employee work information from various databases. Inputs are queries based on employee IDs and project IDs, while outputs include employee data such as past project performance, skill information, and work history. At this stage, the server queries each database for the necessary information and then integrates it.

[0372] Step 2:

[0373] The server cleanses and normalizes the collected data using preprocessing tools. The input is the output data from step 1, and the output is clean data with outliers and duplicates removed. This step performs specific data processing such as unifying data types, handling missing values, and unifying formats.

[0374] Step 3:

[0375] The server executes a skill assessment using a generative AI model, quantifying employee skills from cleansed data. The input is the output data from step 2, and the output is numerical data representing skills. This includes extracting skill information by performing text mining from work reports and feedback comments using natural language processing.

[0376] Step 4:

[0377] The server utilizes planning requirements analysis tools to analyze project requirements and quantify the necessary skill sets. The input is a specific project-based requirements specification, and the output is numerical data indicating the skills required for the project. This analysis includes quantifying relevant technical elements and experience requirements.

[0378] Step 5:

[0379] The server utilizes talent recommendation tools to match skill assessments with project requirements and select the most suitable personnel. The input is the numerical data from steps 3 and 4, and the output is a list of the most suitable employees. Here, a scoring algorithm is used to perform specific data calculations that compare each employee's skill score with the project requirements.

[0380] Step 6:

[0381] The device acquires user feedback and identifies the emotional state using sentiment analysis tools. Input is user comments and reviews, and output is quantitative data indicating emotion. This process utilizes text analysis techniques to determine and quantify the positive / negative nature of the emotion.

[0382] Step 7:

[0383] The server uses result adjustment mechanisms based on the sentiment analysis results to adjust the recommendation results to suit the user's state. The input is the sentiment data from step 6, and the output is the adjusted list of recommended personnel. This stage includes adding supportive content and adjusting the emphasis of detailed information.

[0384] Step 8:

[0385] The server generates a report based on the talent recommendation results and presents it to the user via the terminal. The input is the adjusted recommendation results, and the output is a report appropriately formatted for the user. The final report includes information with emphasis adjusted according to the user's sentiment.

[0386] (Application Example 2)

[0387] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0388] In the face of the need to improve worker productivity and reduce workload at the worksite, achieving optimal work assignments that take into account workers' emotional states has been difficult. Furthermore, analyzing workers' skills and emotional states in real time to optimize work efficiency has also been challenging.

[0389] 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.

[0390] In this invention, the server includes information gathering means for collecting worker work information, emotion recognition means for recognizing the worker's emotional state and adjusting work assignments, and automatic work adjustment means for autonomously distributing tasks based on emotion recognition. This enables flexible and efficient work assignments based on the worker's emotional state.

[0391] "Information gathering means" refers to methods and devices for collecting worker work information, and has the function of acquiring worker work performance and behavioral data.

[0392] "Information preprocessing means" refers to methods or devices used to cleanse and normalize collected information, and are responsible for converting it into a format suitable for data analysis.

[0393] A "technical evaluation tool" is a method or device for numerically evaluating a worker's skills based on normalized information, and is used to quantitatively measure a worker's skills and abilities.

[0394] A "process requirements analysis tool" is a method or device for analyzing an organization's process requirements and quantifying the necessary set of technologies, and is used to clarify the technologies and experience required for a specific task or project.

[0395] A "worker recommendation system" is a method or device for selecting the most suitable worker by matching technical evaluations with process requirements, and has the function of identifying the worker best suited to the project or task.

[0396] "Result reporting means" refers to a method or apparatus for generating and displaying selection results as a report, and provides a means for communicating analysis results to users.

[0397] "Emotional recognition means" refers to a method or device for recognizing the emotional state of workers and adjusting their work assignments, and is used to understand the mental state of workers and to achieve an appropriate work assignment for the work environment.

[0398] "Automatic work adjustment means" refers to a method or device for autonomously distributing tasks based on emotion recognition, in which robots or other devices support the work according to the worker's condition, thereby assisting in efficient work execution.

[0399] In the system that implements this application example, the server plays a central role. First, the server collects work information from each worker using information gathering means. This information includes the worker's performance data and work history. Next, the collected data is cleansed and normalized by information preprocessing means and converted into a format suitable for analysis.

[0400] The server uses technical evaluation tools to quantify the skills of workers from normalized data. This process employs natural language processing techniques to gain insights from workers' comments and reports. The technical data thus evaluated is matched with the organization's process requirements, which are quantified by process requirements analysis tools, and the most suitable workers are selected through worker recommendation tools.

[0401] Furthermore, this system utilizes emotion recognition technology to analyze the emotional state of workers in real time. Based on this information, the server uses an automated work adjustment mechanism to distribute tasks among robots as needed, thereby achieving efficient work.

[0402] As a concrete example, if a worker is feeling fatigued, the server detects this state using emotion recognition and uses an automated task adjustment system to assign tasks to robots in a way that reduces the worker's burden. For instance, assigning heavy lifting tasks to robots reduces the worker's workload.

[0403] Prompts utilizing the generative AI model, such as "Collect recent emotional feedback from employee A and analyze it with the emotion engine. In particular, suggest how to optimize task assignment when the employee is experiencing high stress levels," can be used to achieve more precise emotion recognition and task allocation. This approach makes it possible to improve efficiency in the workplace and the comfort of workers.

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The server collects work information from each worker using information gathering means. Specifically, it acquires worker performance data and work history from sensors and input devices and stores them in the system. The input in this step is raw work data, and the output is that data being incorporated into the system.

[0407] Step 2:

[0408] The server cleanses and normalizes the collected data using preprocessing tools. This includes noise reduction and data format matching. The input is the raw data collected in step 1, and the output is the cleansed and normalized data. This data is then prepared for smooth processing in subsequent analysis steps.

[0409] Step 3:

[0410] The server uses technical evaluation tools to quantify the worker's skills from normalized data. Specifically, it employs natural language processing algorithms to extract insights from text data. The input here is the data obtained in step 2, and the output is numerical data indicating the worker's skills.

[0411] Step 4:

[0412] The server uses process requirements analysis tools to quantify the organization's process requirements. It analyzes project details and clarifies the necessary skill sets. The input in this step is project specifications, etc., and the output is a list of quantified skills.

[0413] Step 5:

[0414] The server uses a worker recommendation system to match technical evaluations with process requirements and select the most suitable worker. This process uses a selection algorithm to identify the most suitable worker. The input is the numerical data obtained in steps 3 and 4, and the output is a list of selected workers.

[0415] Step 6:

[0416] The server recognizes the worker's emotional state using emotion recognition means. It identifies the worker's emotions through facial pattern recognition and voice analysis. The input for this step is real-time data from the worker, and the output is the evaluation result of their emotional state.

[0417] Step 7:

[0418] The server uses automated task adjustment mechanisms to automate tasks based on the results of emotion recognition. Through instructions to the robots, it appropriately distributes or redistributes tasks. The input is the emotion evaluation from step 6, and the output is the adjusted task process. This reduces the burden on the workers.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] This invention provides a system that effectively utilizes employee data within an organization, enabling objective and data-driven decision-making in human resource management. This system is comprised of the following means:

[0436] First, the server collects employee work data from the organization's database. This work data includes past project performance, skill information, and evaluation results. Next, the server cleanses and formats this collected data to make it usable by the AI ​​model.

[0437] Next, an AI model, used as a skill assessment tool, analyzes the data and quantifies each employee's skills and experience. This analysis utilizes natural language processing techniques, in particular, to extract useful information from text data. This quantified skill assessment data is then updated in the organization's database.

[0438] As a project requirements analysis tool, the server analyzes the requirements for each project within the organization. The results of this analysis are quantified and registered, including the necessary skill sets and years of experience for each project. This clarifies the technical requirements for each project.

[0439] Next, an AI model, used as a talent recommendation tool, selects the most suitable personnel by matching employee skill data with project requirements. This matching process utilizes machine learning algorithms to determine which employee is best suited for a specific job requirement.

[0440] The selected candidates are generated in report format by the results reporting system. The server generates this report, and the terminal displays it in a format that is easy for the user to view. This report details the skills assessment of the recommended candidates and the project requirements, helping the user make informed decisions.

[0441] For example, if a project requires a member with advanced Java skills, the server will identify employees with high Java skills based on past project and current work data and recommend them to the terminal. This consistent process allows the organization to efficiently place the right people in the right positions, thereby increasing the likelihood of project success.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] The server accesses various databases to collect employee work data. This includes results from past projects, daily work logs, and skill information. The data is retrieved using APIs and SQL queries.

[0445] Step 2:

[0446] The server cleanses and normalizes the business data it collects. Specifically, this includes imputing missing values, removing outliers, and standardizing data formats. This prepares the data for analysis by AI models.

[0447] Step 3:

[0448] The server passes the cleansed data to an AI model to perform skill assessments. The AI ​​analyzes the data and quantifies employees' skills and experience. In particular, natural language processing is used to extract useful information from the text data.

[0449] Step 4:

[0450] The server collects project requirements data from within the organization. This data includes information such as the required skill sets, years of experience, and roles for each project. An AI model analyzes this information and registers it in a database as numerical project requirements.

[0451] Step 5:

[0452] The AI ​​model matches skill assessment data with project requirements. Using machine learning algorithms, it compares employee skills with project needs to select the most suitable personnel.

[0453] Step 6:

[0454] The server generates a report based on the suitability of the personnel selected by the AI. The report shows details of the recommended employee's skills assessment and how well they fit the project requirements.

[0455] Step 7:

[0456] The terminal displays the generated report to the user. Based on the report, the user considers personnel allocation for the project and provides feedback to the system as needed.

[0457] (Example 1)

[0458] 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."

[0459] In today's business environment, a key challenge is how to effectively utilize the vast amount of employee data an organization possesses to achieve optimal talent placement. Traditional talent management methods involve manual data analysis, which is time-consuming and labor-intensive, and also lack objectivity due to the inclusion of many subjective judgments.

[0460] 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.

[0461] In this invention, the server includes information acquisition means for obtaining worker performance information, information processing means for organizing and formalizing the acquired information, and ability evaluation means for numerically analyzing the worker's abilities based on the formalized information. This makes it possible to automatically and objectively analyze vast amounts of employee data and quickly recommend the most suitable personnel according to the organization's needs.

[0462] "Worker performance information" refers to data including past work results, skills, and evaluations of employees within an organization.

[0463] "Information acquisition means" refers to a process or device for collecting necessary data from information sources such as databases.

[0464] "Information processing means" refers to methods or systems for cleansing, standardizing, and shaping acquired data into a form suitable for analysis.

[0465] A "competency evaluation system" is a mechanism that uses data analysis technology to quantify a worker's skills and experience and objectively evaluate their abilities.

[0466] "Group work requirements" refer to conditions such as the skills and years of experience required for a project or task.

[0467] "Work requirements analysis methods" are techniques for analyzing project requirements, extracting the necessary capabilities and conditions, and quantifying them.

[0468] "Worker recommendation methods" refer to algorithms and techniques used to identify and recommend employees who are best suited to project requirements, based on collected data.

[0469] A "results display method" refers to a platform or format for providing users with the results of analysis and recommendations in a way that they can view.

[0470] "Natural language processing" refers to the techniques or methods used by computers to understand, analyze, and extract meaningful information from human language.

[0471] In this embodiment of the invention, a server retrieves employee performance information from a database within the organization. Database queries using SQL are generally used to retrieve the data, and MySQL or PostgreSQL are suitable database management systems. The data retrieved includes work performance, skill evaluations, and roles and performance in past projects.

[0472] The server then organizes and formats the acquired data using information processing tools. This process utilizes libraries such as Pandas, a Python programming language library, to handle missing data and standardize data formats. As a result, the data is transformed into a format that can be analyzed by the AI ​​model.

[0473] Next, the server uses a competency assessment tool to quantify each worker's skills and experience. Natural language processing technology plays a particularly important role at this stage. For example, using Hugging Face's Transformers library, it's possible to extract useful information from text data and calculate skill points.

[0474] To analyze project requirements, the server uses a work requirements analysis tool. Natural language processing technology is employed here to quantify the required skill sets and years of experience from the requirements document text. Through this analysis, the specific abilities and conditions required for each project become clear.

[0475] Ultimately, the server uses worker recommendation mechanisms to match skill assessments with work requirements and select the most suitable personnel. This matching process can utilize machine learning algorithms such as scikit-learn to identify the optimal worker.

[0476] For example, if a project requires advanced programming skills, the server will select the most suitable worker based on past data and recommend them to the terminal. A possible prompt might be something like, "Recommend an employee with Python skill level 7 or higher," which is input into the generating AI model, and the response is used as a reference. This entire process allows organizations to quickly find the right talent and lead projects to success.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The server retrieves worker performance information from the organization's database. The input data consists of work results, skill information, and evaluation data stored in the database. SQL queries are used to extract this data, and the retrieved data is loaded into memory. This process prepares the raw data for analysis, allowing the server to proceed to the next step.

[0480] Step 2:

[0481] The server formats the acquired raw data using information processing tools. The input data is the performance information acquired in step 1. Specifically, the Python Pandas library is used to impute missing values ​​and standardize the data format. For example, missing skill evaluations are imputed with the average value. This process creates a dataset that the AI ​​model can analyze.

[0482] Step 3:

[0483] The server uses a competency assessment tool to quantify the skills and experience of workers based on the formatted data. The input data is the data formatted in step 2. In this process, natural language processing is used to extract useful information from the text. Self-assessment statements are analyzed using tools such as Hugging Face's Transformers, and skill points are calculated. The output is quantified skill assessment data.

[0484] Step 4:

[0485] The server uses a work requirements analysis tool to analyze project requirements. The input data is the project requirements document within the organization. Natural language processing technology is used to quantify required skill sets and years of experience, generating quantified skill requirements information. This clarifies the specific capabilities and conditions for each project.

[0486] Step 5:

[0487] The server uses a worker recommendation system to match skill assessment data with project requirements. The input data consists of skill assessment data from step 3 and skill requirement information from step 4. Machine learning algorithms such as scikit-learn are applied to identify and select the most suitable worker. The output is a list of the workers best suited to the project.

[0488] Step 6:

[0489] The server generates a report based on the selection results and displays it on the terminal. The input data is the list of workers created in step 5. The generated report details the skills evaluation and suitability of the selected workers for the project. The terminal displays this in a user-friendly format to help the user make the best decision.

[0490] (Application Example 1)

[0491] 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."

[0492] In factories, multiple robots are required to function efficiently and optimally in increasingly complex work environments. However, accurately analyzing the skills of robots and the requirements of each work process, and then appropriately deploying them, is difficult. Therefore, there is a need for a system that quantitatively evaluates robot skills and work process requirements and uses that evaluation to support optimal robot deployment.

[0493] 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.

[0494] In this invention, the server includes data collection means for collecting robot motion information, data preprocessing means for cleansing and normalizing the collected information, and skill evaluation means for numerically analyzing the robot's skills based on the normalized information. This enables the optimal placement of robots within the factory.

[0495] "Robot operation information" refers to all data related to the status and operation of robots operating within a factory.

[0496] "Data collection means" refers to a device or process for efficiently collecting robot operation information.

[0497] "Data preprocessing means" refers to methods and techniques for organizing and arranging collected robot motion information into an analyzable format.

[0498] "Skill evaluation method" refers to a technology for numerically evaluating the capabilities and efficiency of each robot based on robot motion data.

[0499] "Requirements analysis means" refers to a technology that analyzes the necessary skills and capabilities for each work process in a factory and presents the results as numerical values.

[0500] "Robot recommendation methods" refer to technologies that select and propose the most suitable robot based on skill evaluations and work process requirements.

[0501] "Result reporting method" refers to a method for displaying the recommended placement of selected robots in a way that is easily understandable to the user.

[0502] "Smart glasses and head-mounted displays" refer to electronic devices that users wear and use to receive information visually.

[0503] "User verification means" refers to an interface that allows users to provide opinions and feedback on the system's output.

[0504] This invention is a system for achieving the effective and optimal placement of robots in a factory. A server uses data collection means to collect operational information from each robot in the factory. This information relates to the robot's skills and operating status. Next, the collected data is cleansed and normalized by data preprocessing means. This preprocessing can utilize Python's Pandas library or NumPy.

[0505] After the data has been formatted, the server uses skill evaluation tools to quantify the capabilities of each robot. Suitable AI models for this process include machine learning frameworks such as TensorFlow and PyTorch. These tools extract valuable information from the robot's motion data.

[0506] Subsequently, the server uses requirements analysis tools to analyze and quantify the technical requirements for each work process in the factory. This clarifies the robot skills required for a specific work process. Finally, the robot recommendation tool selects the optimal robot based on the results of the skill evaluation and requirements analysis.

[0507] The placement information for the selected robots is generated by a results reporting system and displayed on smart glasses or a head-mounted display. Users can visually confirm this information and provide feedback to the system. For example, a system can be built to recommend the optimal robot for a specific part assembly process. In this case, an example of a prompt message to the generated AI model would be, "Analyze the operation data of the robots currently in use on the factory line, evaluate their handling skills for specific tasks, and select the optimal robot based on the next work plan."

[0508] In this way, appropriate and efficient robot placement is achieved, improving factory productivity.

[0509] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0510] Step 1:

[0511] The server collects operational and motion data from robots within the factory. Input is real-time motion data from each robot, and output is collected raw data. This data includes sensor information, operating time, and work details.

[0512] Step 2:

[0513] The server cleanses and normalizes the collected data using data preprocessing tools. The input is the collected raw data, and the output is formatted data that can be analyzed by the AI ​​model. Here, missing values ​​are imputed using Python's Pandas and NumPy, and preprocessing such as scaling is performed as needed.

[0514] Step 3:

[0515] The server uses an AI model to quantify the skills of each robot based on the formatted data. The input is the formatted data, and the output is data that quantifies the skills of each robot. TensorFlow and PyTorch are used for the AI ​​model, and skills are evaluated based on the performance the robots have shown in past tasks.

[0516] Step 4:

[0517] The server analyzes the factory's work process requirements and quantifies the necessary skill sets. The input is factory process information, and the output is quantified information on the skills required for each process. Based on project specifications and requirements, it utilizes natural language processing techniques to analyze the requirements.

[0518] Step 5:

[0519] The server matches the optimal robot based on skill evaluation data and work process requirements data. The input is quantified skill data and requirements data, and the output is the recommendation of the optimal robot. Here, a machine learning algorithm is used to select the robot that best meets the given requirements.

[0520] Step 6:

[0521] The terminal outputs the generated recommendation results as a report, which is displayed to the user via smart glasses or a head-mounted display. The input is data on the recommended robots, and the output is a report that the user can visually review. Web technologies are used for the user interface, providing information in real time.

[0522] 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.

[0523] This invention combines an emotion engine with a system for analyzing employee data and optimizing personnel allocation, thereby recognizing user emotions and achieving more flexible and effective personnel management. This system mainly consists of the following means.

[0524] First, the server collects employee work data from various databases. This data includes past project performance, skill information, and work history. Next, the server cleanses this data and processes it into a format suitable for AI analysis.

[0525] Next, the server uses an AI model to quantify employee skills from the collected data. This involves a process that leverages natural language processing techniques to extract insights from text data. This skills assessment data is updated in the organization's database, ensuring it is always up-to-date.

[0526] Meanwhile, the server quantifies the skills and experience required for the project using project requirements analysis tools. This clarifies the technical requirements for each project. Next, the AI ​​model matches the skill evaluation data with the project requirements and selects the most suitable personnel.

[0527] Furthermore, this system incorporates an emotion engine that analyzes user feedback and identifies emotions. The terminal sends user feedback to the emotion engine, which then identifies the user's emotional state. This information influences skill evaluations and talent recommendations, and is used to adjust recommendation results.

[0528] The generated talent recommendation results are drafted by the server in report format. This report is presented to the user via their device in an appropriate manner based on their emotional state. Specifically, if the user's emotional state is positive, detailed technical information is emphasized, while if their emotional state is negative, more supportive information is provided.

[0529] For example, if progress on an existing project is unsatisfactory, the server uses an emotion engine to detect the user's stress and provide more helpful guidance to help the user make optimal decisions. This entire process enables organizations to implement strategic talent allocation more effectively.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] The server accesses various databases within the organization to collect employee work data. This includes project performance records, skill information, and past work history. An API is used to retrieve the necessary data.

[0533] Step 2:

[0534] The server cleanses the collected data, removing noise and imputing missing values, and converts it to a standard format. This makes the data suitable for analysis by AI models.

[0535] Step 3:

[0536] The server uses an AI model to analyze normalized data and quantify each employee's skills. Natural language processing techniques are used to extract meaningful skill-related information from text data.

[0537] Step 4:

[0538] The server collects project requirements within the organization and quantifies the necessary skill sets and experience. It analyzes information input from project management systems and other sources to generate precise technical requirements.

[0539] Step 5:

[0540] An AI model matches skill assessment data with project requirements. Using machine learning techniques, it compares each employee's skills with project needs to select the most suitable personnel.

[0541] Step 6:

[0542] The server generates a report based on the selected personnel. The report includes an assessment of employee skills and their suitability for project requirements, and is prepared in an easy-to-read format.

[0543] Step 7:

[0544] The device receives user feedback and analyzes it using an emotion engine. It analyzes user input as text and dynamically determines the level of positive or negative emotion.

[0545] Step 8:

[0546] The emotion engine assesses the user's emotions and uses this information to adjust skill evaluations and talent recommendations. For example, if a user is feeling stressed, it presents information in a way that allows them to understand it in a relaxed state.

[0547] Step 9:

[0548] The device displays a tailored recommendation report. Users receive reports in different formats based on their emotions, and more comprehensive and supportive information is provided to help them make decisions.

[0549] In this way, the present invention enables data-driven personnel allocation while recognizing user emotions.

[0550] (Example 2)

[0551] 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."

[0552] In organizations, the appropriate allocation of talent is a critical issue directly linked to operational efficiency and project success. However, traditional methods have made it difficult to quantitatively evaluate employee skills and quickly and accurately match them to project requirements. Furthermore, talent allocation that takes employee emotions and feedback into account has not been achieved. Therefore, a new system is needed that enables flexible and effective talent management.

[0553] 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.

[0554] In this invention, the server includes information gathering means for collecting employee work information, information preprocessing means for cleansing and normalizing the collected information, and skill evaluation means for numerically analyzing the employee's skills based on the normalized information. This makes it possible to quantitatively evaluate employee skills and recommend the most suitable personnel after adjustments based on sentiment analysis.

[0555] "Information gathering methods" refer to techniques for obtaining information related to employees' work from various data sources.

[0556] "Information preprocessing means" refers to the process of cleansing and normalizing collected raw data and preparing it in a format suitable for analysis.

[0557] A "skill evaluation method" is a method of quantitatively evaluating an employee's skills and abilities numerically based on normalized data.

[0558] "Planning requirements analysis methods" are processes that quantify the skills and experience required for an organization's projects and compare them with skill assessments.

[0559] "Talent recommendation methods" refer to the process of matching evaluated skills with project requirements to select the most suitable personnel.

[0560] The "results reporting method" is a function that generates a report containing information about the selected personnel and displays that information.

[0561] "Emotional analysis methods" are technologies that analyze user feedback to understand their emotions and their emotional state.

[0562] "Result adjustment means" refers to a function that adjusts the recommended personnel and information according to the user's state, based on the results of sentiment analysis.

[0563] A description of embodiments for carrying out the present invention will be provided.

[0564] In this system, the server plays a central role in acquiring employee work information from various databases using information gathering tools. This includes hardware such as cloud storage and internal databases, and database management software such as SQL is used. After this, the server uses information preprocessing tools to cleanse and normalize the collected data and convert it into an appropriate format for analysis.

[0565] Next, the server uses software that leverages natural language processing technology to execute the skills assessment and analyze the normalized data. This software is based on a generative AI model and has the ability to analyze text data and quantify employee skills.

[0566] The server also uses a planning requirements analysis tool to analyze project requirements and quantify the necessary skill sets. Based on this information, a talent recommendation tool operates, matching skill assessment results with project requirements to select the most suitable personnel.

[0567] Furthermore, the terminal receives feedback from the user and identifies the user's emotions through sentiment analysis. This involves determining whether the user's emotions are positive or negative through text analysis. Based on this sentiment information, the server uses result adjustment means to adjust the recommendation results according to the user's emotions.

[0568] For example, if an existing project is stalled, the server can sense the user's stress level from their feedback and provide detailed technical information and support to reduce that stress based on the recommendations.

[0569] An example of a prompt message would be, "The project is stalled; please suggest advice to alleviate the user's concerns."

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The server uses information gathering tools to retrieve employee work information from various databases. Inputs are queries based on employee IDs and project IDs, while outputs include employee data such as past project performance, skill information, and work history. At this stage, the server queries each database for the necessary information and then integrates it.

[0573] Step 2:

[0574] The server cleanses and normalizes the collected data using preprocessing tools. The input is the output data from step 1, and the output is clean data with outliers and duplicates removed. This step performs specific data processing such as unifying data types, handling missing values, and unifying formats.

[0575] Step 3:

[0576] The server executes a skill assessment using a generative AI model, quantifying employee skills from cleansed data. The input is the output data from step 2, and the output is numerical data representing skills. This includes extracting skill information by performing text mining from work reports and feedback comments using natural language processing.

[0577] Step 4:

[0578] The server utilizes planning requirements analysis tools to analyze project requirements and quantify the necessary skill sets. The input is a specific project-based requirements specification, and the output is numerical data indicating the skills required for the project. This analysis includes quantifying relevant technical elements and experience requirements.

[0579] Step 5:

[0580] The server utilizes talent recommendation tools to match skill assessments with project requirements and select the most suitable personnel. The input is the numerical data from steps 3 and 4, and the output is a list of the most suitable employees. Here, a scoring algorithm is used to perform specific data calculations that compare each employee's skill score with the project requirements.

[0581] Step 6:

[0582] The device acquires user feedback and identifies the emotional state using sentiment analysis tools. Input is user comments and reviews, and output is quantitative data indicating emotion. This process utilizes text analysis techniques to determine and quantify the positive / negative nature of the emotion.

[0583] Step 7:

[0584] The server uses result adjustment mechanisms based on the sentiment analysis results to adjust the recommendation results to suit the user's state. The input is the sentiment data from step 6, and the output is the adjusted list of recommended personnel. This stage includes adding supportive content and adjusting the emphasis of detailed information.

[0585] Step 8:

[0586] The server generates a report based on the talent recommendation results and presents it to the user via the terminal. The input is the adjusted recommendation results, and the output is a report appropriately formatted for the user. The final report includes information with emphasis adjusted according to the user's sentiment.

[0587] (Application Example 2)

[0588] 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."

[0589] In the face of the need to improve worker productivity and reduce workload at the worksite, achieving optimal work assignments that take into account workers' emotional states has been difficult. Furthermore, analyzing workers' skills and emotional states in real time to optimize work efficiency has also been challenging.

[0590] 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.

[0591] In this invention, the server includes information gathering means for collecting worker work information, emotion recognition means for recognizing the worker's emotional state and adjusting work assignments, and automatic work adjustment means for autonomously distributing tasks based on emotion recognition. This enables flexible and efficient work assignments based on the worker's emotional state.

[0592] "Information gathering means" refers to methods and devices for collecting worker work information, and has the function of acquiring worker work performance and behavioral data.

[0593] "Information preprocessing means" refers to methods or devices used to cleanse and normalize collected information, and are responsible for converting it into a format suitable for data analysis.

[0594] A "technical evaluation tool" is a method or device for numerically evaluating a worker's skills based on normalized information, and is used to quantitatively measure a worker's skills and abilities.

[0595] A "process requirements analysis tool" is a method or device for analyzing an organization's process requirements and quantifying the necessary set of technologies, and is used to clarify the technologies and experience required for a specific task or project.

[0596] A "worker recommendation system" is a method or device for selecting the most suitable worker by matching technical evaluations with process requirements, and has the function of identifying the worker best suited to the project or task.

[0597] "Result reporting means" refers to a method or apparatus for generating and displaying selection results as a report, and provides a means for communicating analysis results to users.

[0598] "Emotional recognition means" refers to a method or device for recognizing the emotional state of workers and adjusting their work assignments, and is used to understand the mental state of workers and to achieve an appropriate work assignment for the work environment.

[0599] "Automatic work adjustment means" refers to a method or device for autonomously distributing tasks based on emotion recognition, in which robots or other devices support the work according to the worker's condition, thereby assisting in efficient work execution.

[0600] In the system that implements this application example, the server plays a central role. First, the server collects work information from each worker using information gathering means. This information includes the worker's performance data and work history. Next, the collected data is cleansed and normalized by information preprocessing means and converted into a format suitable for analysis.

[0601] The server uses technical evaluation tools to quantify the skills of workers from normalized data. This process employs natural language processing techniques to gain insights from workers' comments and reports. The technical data thus evaluated is matched with the organization's process requirements, which are quantified by process requirements analysis tools, and the most suitable workers are selected through worker recommendation tools.

[0602] Furthermore, this system utilizes emotion recognition technology to analyze the emotional state of workers in real time. Based on this information, the server uses an automated work adjustment mechanism to distribute tasks among robots as needed, thereby achieving efficient work.

[0603] As a concrete example, if a worker is feeling fatigued, the server detects this state using emotion recognition and uses an automated task adjustment system to assign tasks to robots in a way that reduces the worker's burden. For instance, assigning heavy lifting tasks to robots reduces the worker's workload.

[0604] Prompts utilizing the generative AI model, such as "Collect recent emotional feedback from employee A and analyze it with the emotion engine. In particular, suggest how to optimize task assignment when the employee is experiencing high stress levels," can be used to achieve more precise emotion recognition and task allocation. This approach makes it possible to improve efficiency in the workplace and the comfort of workers.

[0605] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0606] Step 1:

[0607] The server collects work information from each worker using information gathering means. Specifically, it acquires worker performance data and work history from sensors and input devices and stores them in the system. The input in this step is raw work data, and the output is that data being incorporated into the system.

[0608] Step 2:

[0609] The server cleanses and normalizes the collected data using preprocessing tools. This includes noise reduction and data format matching. The input is the raw data collected in step 1, and the output is the cleansed and normalized data. This data is then prepared for smooth processing in subsequent analysis steps.

[0610] Step 3:

[0611] The server uses technical evaluation tools to quantify the worker's skills from normalized data. Specifically, it employs natural language processing algorithms to extract insights from text data. The input here is the data obtained in step 2, and the output is numerical data indicating the worker's skills.

[0612] Step 4:

[0613] The server uses process requirements analysis tools to quantify the organization's process requirements. It analyzes project details and clarifies the necessary skill sets. The input in this step is project specifications, etc., and the output is a list of quantified skills.

[0614] Step 5:

[0615] The server uses a worker recommendation system to match technical evaluations with process requirements and select the most suitable worker. This process uses a selection algorithm to identify the most suitable worker. The input is the numerical data obtained in steps 3 and 4, and the output is a list of selected workers.

[0616] Step 6:

[0617] The server recognizes the worker's emotional state using emotion recognition means. It identifies the worker's emotions through facial pattern recognition and voice analysis. The input for this step is real-time data from the worker, and the output is the evaluation result of their emotional state.

[0618] Step 7:

[0619] The server uses automated task adjustment mechanisms to automate tasks based on the results of emotion recognition. Through instructions to the robots, it appropriately distributes or redistributes tasks. The input is the emotion evaluation from step 6, and the output is the adjusted task process. This reduces the burden on the workers.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] [Fourth Embodiment]

[0624] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0625] 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.

[0626] 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).

[0627] 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.

[0628] 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.

[0629] 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).

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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".

[0637] This invention provides a system that effectively utilizes employee data within an organization, enabling objective and data-driven decision-making in human resource management. This system is comprised of the following means:

[0638] First, the server collects employee work data from the organization's database. This work data includes past project performance, skill information, and evaluation results. Next, the server cleanses and formats this collected data to make it usable by the AI ​​model.

[0639] Next, an AI model, used as a skill assessment tool, analyzes the data and quantifies each employee's skills and experience. This analysis utilizes natural language processing techniques, in particular, to extract useful information from text data. This quantified skill assessment data is then updated in the organization's database.

[0640] As a project requirements analysis tool, the server analyzes the requirements for each project within the organization. The results of this analysis are quantified and registered, including the necessary skill sets and years of experience for each project. This clarifies the technical requirements for each project.

[0641] Next, an AI model, used as a talent recommendation tool, selects the most suitable personnel by matching employee skill data with project requirements. This matching process utilizes machine learning algorithms to determine which employee is best suited for a specific job requirement.

[0642] The selected candidates are generated in report format by the results reporting system. The server generates this report, and the terminal displays it in a format that is easy for the user to view. This report details the skills assessment of the recommended candidates and the project requirements, helping the user make informed decisions.

[0643] For example, if a project requires a member with advanced Java skills, the server will identify employees with high Java skills based on past project and current work data and recommend them to the terminal. This consistent process allows the organization to efficiently place the right people in the right positions, thereby increasing the likelihood of project success.

[0644] The following describes the processing flow.

[0645] Step 1:

[0646] The server accesses various databases to collect employee work data. This includes results from past projects, daily work logs, and skill information. The data is retrieved using APIs and SQL queries.

[0647] Step 2:

[0648] The server cleanses and normalizes the business data it collects. Specifically, this includes imputing missing values, removing outliers, and standardizing data formats. This prepares the data for analysis by AI models.

[0649] Step 3:

[0650] The server passes the cleansed data to an AI model to perform skill assessments. The AI ​​analyzes the data and quantifies employees' skills and experience. In particular, natural language processing is used to extract useful information from the text data.

[0651] Step 4:

[0652] The server collects project requirements data from within the organization. This data includes information such as the required skill sets, years of experience, and roles for each project. An AI model analyzes this information and registers it in a database as numerical project requirements.

[0653] Step 5:

[0654] The AI ​​model matches skill assessment data with project requirements. Using machine learning algorithms, it compares employee skills with project needs to select the most suitable personnel.

[0655] Step 6:

[0656] The server generates a report based on the suitability of the personnel selected by the AI. The report shows details of the recommended employee's skills assessment and how well they fit the project requirements.

[0657] Step 7:

[0658] The terminal displays the generated report to the user. Based on the report, the user considers personnel allocation for the project and provides feedback to the system as needed.

[0659] (Example 1)

[0660] 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".

[0661] In today's business environment, a key challenge is how to effectively utilize the vast amount of employee data an organization possesses to achieve optimal talent placement. Traditional talent management methods involve manual data analysis, which is time-consuming and labor-intensive, and also lack objectivity due to the inclusion of many subjective judgments.

[0662] 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.

[0663] In this invention, the server includes information acquisition means for obtaining worker performance information, information processing means for organizing and formalizing the acquired information, and ability evaluation means for numerically analyzing the worker's abilities based on the formalized information. This makes it possible to automatically and objectively analyze vast amounts of employee data and quickly recommend the most suitable personnel according to the organization's needs.

[0664] "Worker performance information" refers to data including past work results, skills, and evaluations of employees within an organization.

[0665] "Information acquisition means" refers to a process or device for collecting necessary data from information sources such as databases.

[0666] "Information processing means" refers to methods or systems for cleansing, standardizing, and shaping acquired data into a form suitable for analysis.

[0667] A "competency evaluation system" is a mechanism that uses data analysis technology to quantify a worker's skills and experience and objectively evaluate their abilities.

[0668] "Group work requirements" refer to conditions such as the skills and years of experience required for a project or task.

[0669] "Work requirements analysis methods" are techniques for analyzing project requirements, extracting the necessary capabilities and conditions, and quantifying them.

[0670] "Worker recommendation methods" refer to algorithms and techniques used to identify and recommend employees who are best suited to project requirements, based on collected data.

[0671] A "results display method" refers to a platform or format for providing users with the results of analysis and recommendations in a way that they can view.

[0672] "Natural language processing" refers to the techniques or methods used by computers to understand, analyze, and extract meaningful information from human language.

[0673] In this embodiment of the invention, a server retrieves employee performance information from a database within the organization. Database queries using SQL are generally used to retrieve the data, and MySQL or PostgreSQL are suitable database management systems. The data retrieved includes work performance, skill evaluations, and roles and performance in past projects.

[0674] The server then organizes and formats the acquired data using information processing tools. This process utilizes libraries such as Pandas, a Python programming language library, to handle missing data and standardize data formats. As a result, the data is transformed into a format that can be analyzed by the AI ​​model.

[0675] Next, the server uses a competency assessment tool to quantify each worker's skills and experience. Natural language processing technology plays a particularly important role at this stage. For example, using Hugging Face's Transformers library, it's possible to extract useful information from text data and calculate skill points.

[0676] To analyze project requirements, the server uses a work requirements analysis tool. Natural language processing technology is employed here to quantify the required skill sets and years of experience from the requirements document text. Through this analysis, the specific abilities and conditions required for each project become clear.

[0677] Ultimately, the server uses worker recommendation mechanisms to match skill assessments with work requirements and select the most suitable personnel. This matching process can utilize machine learning algorithms such as scikit-learn to identify the optimal worker.

[0678] For example, if a project requires advanced programming skills, the server will select the most suitable worker based on past data and recommend them to the terminal. A possible prompt might be something like, "Recommend an employee with Python skill level 7 or higher," which is input into the generating AI model, and the response is used as a reference. This entire process allows organizations to quickly find the right talent and lead projects to success.

[0679] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0680] Step 1:

[0681] The server retrieves worker performance information from the organization's database. The input data consists of work results, skill information, and evaluation data stored in the database. SQL queries are used to extract this data, and the retrieved data is loaded into memory. This process prepares the raw data for analysis, allowing the server to proceed to the next step.

[0682] Step 2:

[0683] The server formats the acquired raw data using information processing tools. The input data is the performance information acquired in step 1. Specifically, the Python Pandas library is used to impute missing values ​​and standardize the data format. For example, missing skill evaluations are imputed with the average value. This process creates a dataset that the AI ​​model can analyze.

[0684] Step 3:

[0685] The server uses a competency assessment tool to quantify the skills and experience of workers based on the formatted data. The input data is the data formatted in step 2. In this process, natural language processing is used to extract useful information from the text. Self-assessment statements are analyzed using tools such as Hugging Face's Transformers, and skill points are calculated. The output is quantified skill assessment data.

[0686] Step 4:

[0687] The server uses a work requirements analysis tool to analyze project requirements. The input data is the project requirements document within the organization. Natural language processing technology is used to quantify required skill sets and years of experience, generating quantified skill requirements information. This clarifies the specific capabilities and conditions for each project.

[0688] Step 5:

[0689] The server uses a worker recommendation system to match skill assessment data with project requirements. The input data consists of skill assessment data from step 3 and skill requirement information from step 4. Machine learning algorithms such as scikit-learn are applied to identify and select the most suitable worker. The output is a list of the workers best suited to the project.

[0690] Step 6:

[0691] The server generates a report based on the selection results and displays it on the terminal. The input data is the list of workers created in step 5. The generated report details the skills evaluation and suitability of the selected workers for the project. The terminal displays this in a user-friendly format to help the user make the best decision.

[0692] (Application Example 1)

[0693] 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".

[0694] In factories, multiple robots are required to function efficiently and optimally in increasingly complex work environments. However, accurately analyzing the skills of robots and the requirements of each work process, and then appropriately deploying them, is difficult. Therefore, there is a need for a system that quantitatively evaluates robot skills and work process requirements and uses that evaluation to support optimal robot deployment.

[0695] 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.

[0696] In this invention, the server includes data collection means for collecting robot motion information, data preprocessing means for cleansing and normalizing the collected information, and skill evaluation means for numerically analyzing the robot's skills based on the normalized information. This enables the optimal placement of robots within the factory.

[0697] "Robot operation information" refers to all data related to the status and operation of robots operating within a factory.

[0698] "Data collection means" refers to a device or process for efficiently collecting robot operation information.

[0699] "Data preprocessing means" refers to methods and techniques for organizing and arranging collected robot motion information into an analyzable format.

[0700] "Skill evaluation method" refers to a technology for numerically evaluating the capabilities and efficiency of each robot based on robot motion data.

[0701] "Requirements analysis means" refers to a technology that analyzes the necessary skills and capabilities for each work process in a factory and presents the results as numerical values.

[0702] "Robot recommendation methods" refer to technologies that select and propose the most suitable robot based on skill evaluations and work process requirements.

[0703] "Result reporting method" refers to a method for displaying the recommended placement of selected robots in a way that is easily understandable to the user.

[0704] "Smart glasses and head-mounted displays" refer to electronic devices that users wear and use to receive information visually.

[0705] "User verification means" refers to an interface that allows users to provide opinions and feedback on the system's output.

[0706] This invention is a system for achieving the effective and optimal placement of robots in a factory. A server uses data collection means to collect operational information from each robot in the factory. This information relates to the robot's skills and operating status. Next, the collected data is cleansed and normalized by data preprocessing means. This preprocessing can utilize Python's Pandas library or NumPy.

[0707] After the data has been formatted, the server uses skill evaluation tools to quantify the capabilities of each robot. Suitable AI models for this process include machine learning frameworks such as TensorFlow and PyTorch. These tools extract valuable information from the robot's motion data.

[0708] Subsequently, the server uses requirements analysis tools to analyze and quantify the technical requirements for each work process in the factory. This clarifies the robot skills required for a specific work process. Finally, the robot recommendation tool selects the optimal robot based on the results of the skill evaluation and requirements analysis.

[0709] The placement information for the selected robots is generated by a results reporting system and displayed on smart glasses or a head-mounted display. Users can visually confirm this information and provide feedback to the system. For example, a system can be built to recommend the optimal robot for a specific part assembly process. In this case, an example of a prompt message to the generated AI model would be, "Analyze the operation data of the robots currently in use on the factory line, evaluate their handling skills for specific tasks, and select the optimal robot based on the next work plan."

[0710] In this way, appropriate and efficient robot placement is achieved, improving factory productivity.

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] The server collects operational and motion data from robots within the factory. Input is real-time motion data from each robot, and output is collected raw data. This data includes sensor information, operating time, and work details.

[0714] Step 2:

[0715] The server cleanses and normalizes the collected data using data preprocessing tools. The input is the collected raw data, and the output is formatted data that can be analyzed by the AI ​​model. Here, missing values ​​are imputed using Python's Pandas and NumPy, and preprocessing such as scaling is performed as needed.

[0716] Step 3:

[0717] The server uses an AI model to quantify the skills of each robot based on the formatted data. The input is the formatted data, and the output is data that quantifies the skills of each robot. TensorFlow and PyTorch are used for the AI ​​model, and skills are evaluated based on the performance the robots have shown in past tasks.

[0718] Step 4:

[0719] The server analyzes the factory's work process requirements and quantifies the necessary skill sets. The input is factory process information, and the output is quantified information on the skills required for each process. Based on project specifications and requirements, it utilizes natural language processing techniques to analyze the requirements.

[0720] Step 5:

[0721] The server matches the optimal robot based on skill evaluation data and work process requirements data. The input is quantified skill data and requirements data, and the output is the recommendation of the optimal robot. Here, a machine learning algorithm is used to select the robot that best meets the given requirements.

[0722] Step 6:

[0723] The terminal outputs the generated recommendation results as a report, which is displayed to the user via smart glasses or a head-mounted display. The input is data on the recommended robots, and the output is a report that the user can visually review. Web technologies are used for the user interface, providing information in real time.

[0724] 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.

[0725] This invention combines an emotion engine with a system for analyzing employee data and optimizing personnel allocation, thereby recognizing user emotions and achieving more flexible and effective personnel management. This system mainly consists of the following means.

[0726] First, the server collects employee work data from various databases. This data includes past project performance, skill information, and work history. Next, the server cleanses this data and processes it into a format suitable for AI analysis.

[0727] Next, the server uses an AI model to quantify employee skills from the collected data. This involves a process that leverages natural language processing techniques to extract insights from text data. This skills assessment data is updated in the organization's database, ensuring it is always up-to-date.

[0728] Meanwhile, the server quantifies the skills and experience required for the project using project requirements analysis tools. This clarifies the technical requirements for each project. Next, the AI ​​model matches the skill evaluation data with the project requirements and selects the most suitable personnel.

[0729] Furthermore, this system incorporates an emotion engine that analyzes user feedback and identifies emotions. The terminal sends user feedback to the emotion engine, which then identifies the user's emotional state. This information influences skill evaluations and talent recommendations, and is used to adjust recommendation results.

[0730] The generated talent recommendation results are drafted by the server in report format. This report is presented to the user via their device in an appropriate manner based on their emotional state. Specifically, if the user's emotional state is positive, detailed technical information is emphasized, while if their emotional state is negative, more supportive information is provided.

[0731] For example, if progress on an existing project is unsatisfactory, the server uses an emotion engine to detect the user's stress and provide more helpful guidance to help the user make optimal decisions. This entire process enables organizations to implement strategic talent allocation more effectively.

[0732] The following describes the processing flow.

[0733] Step 1:

[0734] The server accesses various databases within the organization to collect employee work data. This includes project performance records, skill information, and past work history. An API is used to retrieve the necessary data.

[0735] Step 2:

[0736] The server cleanses the collected data, removing noise and imputing missing values, and converts it to a standard format. This makes the data suitable for analysis by AI models.

[0737] Step 3:

[0738] The server uses an AI model to analyze normalized data and quantify each employee's skills. Natural language processing techniques are used to extract meaningful skill-related information from text data.

[0739] Step 4:

[0740] The server collects project requirements within the organization and quantifies the necessary skill sets and experience. It analyzes information input from project management systems and other sources to generate precise technical requirements.

[0741] Step 5:

[0742] An AI model matches skill assessment data with project requirements. Using machine learning techniques, it compares each employee's skills with project needs to select the most suitable personnel.

[0743] Step 6:

[0744] The server generates a report based on the selected personnel. The report includes an assessment of employee skills and their suitability for project requirements, and is prepared in an easy-to-read format.

[0745] Step 7:

[0746] The device receives user feedback and analyzes it using an emotion engine. It analyzes user input as text and dynamically determines the level of positive or negative emotion.

[0747] Step 8:

[0748] The emotion engine assesses the user's emotions and uses this information to adjust skill evaluations and talent recommendations. For example, if a user is feeling stressed, it presents information in a way that allows them to understand it in a relaxed state.

[0749] Step 9:

[0750] The device displays a tailored recommendation report. Users receive reports in different formats based on their emotions, and more comprehensive and supportive information is provided to help them make decisions.

[0751] In this way, the present invention enables data-driven personnel allocation while recognizing user emotions.

[0752] (Example 2)

[0753] 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".

[0754] In organizations, the appropriate allocation of talent is a critical issue directly linked to operational efficiency and project success. However, traditional methods have made it difficult to quantitatively evaluate employee skills and quickly and accurately match them to project requirements. Furthermore, talent allocation that takes employee emotions and feedback into account has not been achieved. Therefore, a new system is needed that enables flexible and effective talent management.

[0755] 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.

[0756] In this invention, the server includes information gathering means for collecting employee work information, information preprocessing means for cleansing and normalizing the collected information, and skill evaluation means for numerically analyzing the employee's skills based on the normalized information. This makes it possible to quantitatively evaluate employee skills and recommend the most suitable personnel after adjustments based on sentiment analysis.

[0757] "Information gathering methods" refer to techniques for obtaining information related to employees' work from various data sources.

[0758] "Information preprocessing means" refers to the process of cleansing and normalizing collected raw data and preparing it in a format suitable for analysis.

[0759] A "skill evaluation method" is a method of quantitatively evaluating an employee's skills and abilities numerically based on normalized data.

[0760] "Planning requirements analysis methods" are processes that quantify the skills and experience required for an organization's projects and compare them with skill assessments.

[0761] "Talent recommendation methods" refer to the process of matching evaluated skills with project requirements to select the most suitable personnel.

[0762] The "results reporting method" is a function that generates a report containing information about the selected personnel and displays that information.

[0763] "Emotional analysis methods" are technologies that analyze user feedback to understand their emotions and their emotional state.

[0764] "Result adjustment means" refers to a function that adjusts the recommended personnel and information according to the user's state, based on the results of sentiment analysis.

[0765] A description of embodiments for carrying out the present invention will be provided.

[0766] In this system, the server plays a central role in acquiring employee work information from various databases using information gathering tools. This includes hardware such as cloud storage and internal databases, and database management software such as SQL is used. After this, the server uses information preprocessing tools to cleanse and normalize the collected data and convert it into an appropriate format for analysis.

[0767] Next, the server uses software that leverages natural language processing technology to execute the skills assessment and analyze the normalized data. This software is based on a generative AI model and has the ability to analyze text data and quantify employee skills.

[0768] The server also uses a planning requirements analysis tool to analyze project requirements and quantify the necessary skill sets. Based on this information, a talent recommendation tool operates, matching skill assessment results with project requirements to select the most suitable personnel.

[0769] Furthermore, the terminal receives feedback from the user and identifies the user's emotions through sentiment analysis. This involves determining whether the user's emotions are positive or negative through text analysis. Based on this sentiment information, the server uses result adjustment means to adjust the recommendation results according to the user's emotions.

[0770] For example, if an existing project is stalled, the server can sense the user's stress level from their feedback and provide detailed technical information and support to reduce that stress based on the recommendations.

[0771] An example of a prompt message would be, "The project is stalled; please suggest advice to alleviate the user's concerns."

[0772] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0773] Step 1:

[0774] The server uses information gathering tools to retrieve employee work information from various databases. Inputs are queries based on employee IDs and project IDs, while outputs include employee data such as past project performance, skill information, and work history. At this stage, the server queries each database for the necessary information and then integrates it.

[0775] Step 2:

[0776] The server cleanses and normalizes the collected data using preprocessing tools. The input is the output data from step 1, and the output is clean data with outliers and duplicates removed. This step performs specific data processing such as unifying data types, handling missing values, and unifying formats.

[0777] Step 3:

[0778] The server executes a skill assessment using a generative AI model, quantifying employee skills from cleansed data. The input is the output data from step 2, and the output is numerical data representing skills. This includes extracting skill information by performing text mining from work reports and feedback comments using natural language processing.

[0779] Step 4:

[0780] The server utilizes planning requirements analysis tools to analyze project requirements and quantify the necessary skill sets. The input is a specific project-based requirements specification, and the output is numerical data indicating the skills required for the project. This analysis includes quantifying relevant technical elements and experience requirements.

[0781] Step 5:

[0782] The server utilizes talent recommendation tools to match skill assessments with project requirements and select the most suitable personnel. The input is the numerical data from steps 3 and 4, and the output is a list of the most suitable employees. Here, a scoring algorithm is used to perform specific data calculations that compare each employee's skill score with the project requirements.

[0783] Step 6:

[0784] The device acquires user feedback and identifies the emotional state using sentiment analysis tools. Input is user comments and reviews, and output is quantitative data indicating emotion. This process utilizes text analysis techniques to determine and quantify the positive / negative nature of the emotion.

[0785] Step 7:

[0786] The server uses result adjustment mechanisms based on the sentiment analysis results to adjust the recommendation results to suit the user's state. The input is the sentiment data from step 6, and the output is the adjusted list of recommended personnel. This stage includes adding supportive content and adjusting the emphasis of detailed information.

[0787] Step 8:

[0788] The server generates a report based on the talent recommendation results and presents it to the user via the terminal. The input is the adjusted recommendation results, and the output is a report appropriately formatted for the user. The final report includes information with emphasis adjusted according to the user's sentiment.

[0789] (Application Example 2)

[0790] 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".

[0791] In the face of the need to improve worker productivity and reduce workload at the worksite, achieving optimal work assignments that take into account workers' emotional states has been difficult. Furthermore, analyzing workers' skills and emotional states in real time to optimize work efficiency has also been challenging.

[0792] 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.

[0793] In this invention, the server includes information gathering means for collecting worker work information, emotion recognition means for recognizing the worker's emotional state and adjusting work assignments, and automatic work adjustment means for autonomously distributing tasks based on emotion recognition. This enables flexible and efficient work assignments based on the worker's emotional state.

[0794] "Information gathering means" refers to methods and devices for collecting worker work information, and has the function of acquiring worker work performance and behavioral data.

[0795] "Information preprocessing means" refers to methods or devices used to cleanse and normalize collected information, and are responsible for converting it into a format suitable for data analysis.

[0796] A "technical evaluation tool" is a method or device for numerically evaluating a worker's skills based on normalized information, and is used to quantitatively measure a worker's skills and abilities.

[0797] A "process requirements analysis tool" is a method or device for analyzing an organization's process requirements and quantifying the necessary set of technologies, and is used to clarify the technologies and experience required for a specific task or project.

[0798] A "worker recommendation system" is a method or device for selecting the most suitable worker by matching technical evaluations with process requirements, and has the function of identifying the worker best suited to the project or task.

[0799] "Result reporting means" refers to a method or apparatus for generating and displaying selection results as a report, and provides a means for communicating analysis results to users.

[0800] "Emotional recognition means" refers to a method or device for recognizing the emotional state of workers and adjusting their work assignments, and is used to understand the mental state of workers and to achieve an appropriate work assignment for the work environment.

[0801] "Automatic work adjustment means" refers to a method or device for autonomously distributing tasks based on emotion recognition, in which robots or other devices support the work according to the worker's condition, thereby assisting in efficient work execution.

[0802] In the system that implements this application example, the server plays a central role. First, the server collects work information from each worker using information gathering means. This information includes the worker's performance data and work history. Next, the collected data is cleansed and normalized by information preprocessing means and converted into a format suitable for analysis.

[0803] The server uses technical evaluation tools to quantify the skills of workers from normalized data. This process employs natural language processing techniques to gain insights from workers' comments and reports. The technical data thus evaluated is matched with the organization's process requirements, which are quantified by process requirements analysis tools, and the most suitable workers are selected through worker recommendation tools.

[0804] Furthermore, this system utilizes emotion recognition technology to analyze the emotional state of workers in real time. Based on this information, the server uses an automated work adjustment mechanism to distribute tasks among robots as needed, thereby achieving efficient work.

[0805] As a concrete example, if a worker is feeling fatigued, the server detects this state using emotion recognition and uses an automated task adjustment system to assign tasks to robots in a way that reduces the worker's burden. For instance, assigning heavy lifting tasks to robots reduces the worker's workload.

[0806] Prompts utilizing the generative AI model, such as "Collect recent emotional feedback from employee A and analyze it with the emotion engine. In particular, suggest how to optimize task assignment when the employee is experiencing high stress levels," can be used to achieve more precise emotion recognition and task allocation. This approach makes it possible to improve efficiency in the workplace and the comfort of workers.

[0807] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0808] Step 1:

[0809] The server collects work information from each worker using information gathering means. Specifically, it acquires worker performance data and work history from sensors and input devices and stores them in the system. The input in this step is raw work data, and the output is that data being incorporated into the system.

[0810] Step 2:

[0811] The server cleanses and normalizes the collected data using preprocessing tools. This includes noise reduction and data format matching. The input is the raw data collected in step 1, and the output is the cleansed and normalized data. This data is then prepared for smooth processing in subsequent analysis steps.

[0812] Step 3:

[0813] The server uses technical evaluation tools to quantify the worker's skills from normalized data. Specifically, it employs natural language processing algorithms to extract insights from text data. The input here is the data obtained in step 2, and the output is numerical data indicating the worker's skills.

[0814] Step 4:

[0815] The server uses process requirements analysis tools to quantify the organization's process requirements. It analyzes project details and clarifies the necessary skill sets. The input in this step is project specifications, etc., and the output is a list of quantified skills.

[0816] Step 5:

[0817] The server uses a worker recommendation system to match technical evaluations with process requirements and select the most suitable worker. This process uses a selection algorithm to identify the most suitable worker. The input is the numerical data obtained in steps 3 and 4, and the output is a list of selected workers.

[0818] Step 6:

[0819] The server recognizes the worker's emotional state using emotion recognition means. It identifies the worker's emotions through facial pattern recognition and voice analysis. The input for this step is real-time data from the worker, and the output is the evaluation result of their emotional state.

[0820] Step 7:

[0821] The server uses automated task adjustment mechanisms to automate tasks based on the results of emotion recognition. Through instructions to the robots, it appropriately distributes or redistributes tasks. The input is the emotion evaluation from step 6, and the output is the adjusted task process. This reduces the burden on the workers.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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."

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] Data collection methods for collecting employee work data,

[0846] Data preprocessing means for cleansing and normalizing the collected data,

[0847] A skill evaluation method that numerically analyzes employee skills based on normalized data,

[0848] A project requirements analysis tool that analyzes the organization's project requirements and quantifies the necessary skill sets,

[0849] A talent recommendation system for selecting the most suitable personnel by matching skill assessments with project requirements,

[0850] A system including a means for generating and displaying selection results as a report.

[0851] (Claim 2)

[0852] A system according to claim 1, which is a skill evaluation means for analyzing each employee's data using natural language processing.

[0853] (Claim 3)

[0854] The system according to claim 1, comprising user verification means including an interface for the user to provide feedback.

[0855] "Example 1"

[0856] (Claim 1)

[0857] Information acquisition means for obtaining worker performance information,

[0858] Information processing means for organizing and formalizing acquired information,

[0859] A capability evaluation method that numerically analyzes the abilities of workers based on formalized information,

[0860] A work requirements analysis method that examines the work requirements of a group and quantifies the necessary set of capabilities,

[0861] A worker recommendation system for determining the most suitable worker by comparing ability assessments with work requirements,

[0862] A means for generating and displaying the decision results as a report,

[0863] A system including analytical means for extracting useful information from text information using natural language processing.

[0864] (Claim 2)

[0865] The system according to claim 1, which uses a generative machine learning model to predict capabilities and contribute to the selection of the optimal worker.

[0866] (Claim 3)

[0867] The system according to claim 1, which provides a dialogue means for users to provide feedback that helps them confirm and make decisions.

[0868] "Application Example 1"

[0869] (Claim 1)

[0870] A data collection means for collecting robot motion information,

[0871] Data preprocessing means for cleansing and normalizing the collected information,

[0872] A skill evaluation method that numerically analyzes robot skills based on normalized information,

[0873] A requirements analysis tool that analyzes the requirements of factory work processes and quantifies the necessary skill sets,

[0874] A robot recommendation method for selecting the optimal robot by matching skill evaluations with work process requirements,

[0875] A system including a means for generating and displaying selection results as a report.

[0876] (Claim 2)

[0877] The system according to claim 1, comprising display means for displaying information using smart glasses or a head-mounted display.

[0878] (Claim 3)

[0879] The system according to claim 1, comprising user verification means including an interface for the user to provide feedback.

[0880] "Example 2 of combining an emotion engine"

[0881] (Claim 1)

[0882] Information gathering methods for collecting employee work information,

[0883] Information preprocessing means for cleansing and normalizing the collected information,

[0884] A skills evaluation method that numerically analyzes employee skills based on normalized information,

[0885] A planning requirements analysis tool that analyzes the planning requirements of an organization and quantifies the necessary skill set,

[0886] A talent recommendation method for selecting the most suitable personnel by matching skill assessments with project requirements,

[0887] A means for generating and displaying the selection results as a report,

[0888] A means of sentiment analysis for identifying user emotions,

[0889] A means of adjusting the results of recommendations based on the sentiment analysis results,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, which is a skill evaluation means for analyzing each employee's information using natural language processing.

[0893] (Claim 3)

[0894] The system according to claim 1, comprising user verification means including an interface for the user to provide feedback.

[0895] "Application example 2 when combining with an emotional engine"

[0896] (Claim 1)

[0897] Information gathering means for collecting worker work information,

[0898] Information preprocessing means for cleansing and normalizing the collected information,

[0899] A technical evaluation method that numerically analyzes the skills of workers based on normalized information,

[0900] A process requirements analysis tool that analyzes the process requirements of an organization and quantifies the necessary set of technologies,

[0901] A worker recommendation system for selecting the most suitable worker by matching technical evaluation and process requirements,

[0902] A means for generating and displaying the selection results as a report,

[0903] An emotion recognition means for recognizing the emotional state of workers and adjusting work assignments,

[0904] A system including an automated task adjustment mechanism for autonomously distributing tasks based on emotion recognition.

[0905] (Claim 2)

[0906] A system according to claim 1, which is a technical evaluation means for analyzing information on each worker using natural language processing.

[0907] (Claim 3)

[0908] The system according to claim 1, comprising user verification means including an interface for an operator to provide feedback and recognize their emotional state. [Explanation of Symbols]

[0909] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data collection means for collecting robot motion information, Data preprocessing means for cleansing and normalizing the collected information, A skill evaluation method that numerically analyzes robot skills based on normalized information, A requirements analysis tool that analyzes the requirements of factory work processes and quantifies the necessary skill sets, A robot recommendation method for selecting the optimal robot by matching skill evaluations with work process requirements, A system including a means for generating and displaying selection results as a report.

2. The system according to claim 1, comprising display means for displaying information using smart glasses or a head-mounted display.

3. The system according to claim 1, comprising user verification means including an interface for the user to provide feedback.

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