system

Abstract

We provide the system. [Solution] A means of acquiring information on each worker's work environment and job content, A means for analyzing the results of each worker using a generative model based on the acquired information, Based on the analysis results, a means of matching other workers who are improving their work performance under similar operating conditions, Means for planning and implementing education and training for associated workers to share successful methods, A means of evaluating the worker's performance in accordance with the improvement of their results, A system that includes means for presenting efficient work procedures for inventory management at logistics facilities and means for linking information processing devices.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional sales operations, the skills and achievements of workers are highly dependent on individual environments, and it is difficult to provide prompt and effective solutions to individual problems through general training and advice. Therefore, there is a need for a specific method for workers to efficiently acquire know-how suitable for their respective environmental conditions and improve their performance.

Means for Solving the Problems

[0005] This system collects work environment and project information from each individual employee and analyzes their performance using machine learning models to match them with employees who have achieved success in similar environments. This system facilitates the sharing of success stories among matched employees online, ultimately evaluating their performance based on improvements and providing feedback to enhance their skills.

[0006] "Business personnel" refers to individuals who engage in specific tasks within a company or organization, such as sales or service provision.

[0007] "Work environment information" refers to information about the physical, geographical, and human conditions of the workplace to which the employee belongs, and includes factors related to the work in question.

[0008] "Project information" refers to information about the specific tasks and assigned projects that employees are working on.

[0009] A "machine learning model" refers to a computer-based analytical tool that uses large amounts of data to learn specific patterns and trends, and to make future outputs and predictions.

[0010] "Performance" refers to an indicator that comprehensively shows the degree of ability and results that an employee demonstrates in their work.

[0011] "Matching" refers to the process of connecting multiple elements that have similar characteristics or properties based on specific criteria or conditions.

[0012] "Online format" refers to a method of sharing information and communicating in real time using the internet.

[0013] "Evaluation" refers to the process of quantitatively or qualitatively assessing the skills and results of employees and measuring their value and effectiveness.

[0014] "Feedback" refers to evaluations and opinions given regarding specific actions or results, and is conducted to encourage improvement and growth. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

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

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

[0020] In the following embodiments, a storage with a reference number 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.

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

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention provides a system that can perform appropriate performance improvement matching based on the work environment and project information of the workforce. This system supports the skill improvement of workforce through data collection, analysis, matching, training, and evaluation.

[0037] Data collection

[0038] At system startup, users enter their environment information and details of their assigned projects into a terminal. This information includes store location, customer demographics, and customer traffic. The terminal collects the entered data and sends it to the server.

[0039] Data analysis and matching

[0040] The collected information is managed on a server. The server uses machine learning models to analyze the work environment and project information of each employee and calculate various performance indicators. This allows the server to identify and match other employees who are performing well under similar conditions.

[0041] Training and feedback

[0042] The server coordinates online training sessions between matched professionals. This training provides a platform for sharing specific success stories and effective methods. Users can participate in this training and apply the learned methods to their actual work, thereby improving efficiency and achieving better results. After the training, the server monitors the professionals' performance and evaluates them based on improvements in results. Professionals who shared knowledge are awarded evaluation points and provided with feedback.

[0043] Specific example

[0044] For example, suppose a salesperson selling smartphones at a suburban store uses this system. This salesperson inputs data indicating that the customer base is primarily young people and that customer traffic fluctuates seasonally. The server analyzes this data and identifies other salespeople who have achieved high sales performance in similar environments. Online training is conducted with these identified salespeople, allowing them to learn effective sales strategies on the spot and improve sales performance at the suburban store. If performance improves after the training, the salesperson who provided the knowledge is awarded points as a reward.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] Users use their own devices to input work environment information such as store location, customer base, and project characteristics, as well as project information.

[0048] Step 2:

[0049] The terminal formats the input data and sends it to the server as a data packet.

[0050] Step 3:

[0051] The server records the received data in a database and aggregates information on all employees involved in the work.

[0052] Step 4:

[0053] The server uses the database information to run a machine learning model to calculate performance metrics for each employee.

[0054] Step 5:

[0055] Based on the calculated performance metrics, the server identifies employees who are performing well in similar environments.

[0056] Step 6:

[0057] The server matches identified personnel with online training sessions based on the matching results.

[0058] Step 7:

[0059] The server notifies users' terminals of the details of the planned training and prepares for participants to connect in real time.

[0060] Step 8:

[0061] Users participate in online training at designated times, learning success stories and techniques from other professionals.

[0062] Step 9:

[0063] The server monitors the performance of employees after they complete the training and records their results in a database.

[0064] Step 10:

[0065] If an improvement in performance is confirmed, the server adds evaluation points to the employees who shared the knowledge and sends the feedback information to the user's terminal.

[0066] (Example 1)

[0067] 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."

[0068] In today's work environment, employees need to adapt to diverse work environments and customer needs. However, individual employees are often limited by their own experience and knowledge, making it difficult for them to obtain the information necessary to achieve optimal performance. Therefore, there is a growing need for support systems that enable employees to efficiently improve their skills and enhance their results.

[0069] 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.

[0070] In this invention, the server includes means for aggregating work environment information and activity information of each worker, means for analyzing the capabilities of each worker using a data processing model based on the aggregated information, and means for matching workers who have achieved results in similar environments based on the analysis results. This makes it possible for workers to obtain practical information that is optimal for their own work environment and to efficiently improve their performance.

[0071] "Worker" refers to an individual engaged in a specific task or operation.

[0072] "Work environment information" refers to information regarding the physical and social factors that affect workers when they perform their duties.

[0073] "Activity information" refers to data and records generated by workers in the process of performing their duties.

[0074] "To aggregate" refers to organizing individual data or pieces of information and combining them into a single, cohesive format.

[0075] A "data processing model" refers to a method for analyzing, predicting, or classifying data using specific algorithms or techniques.

[0076] "Analyzing capabilities" refers to evaluating a worker's performance and skills based on objective data.

[0077] "Similar environments" refer to situations in which different workers find themselves sharing common characteristics.

[0078] "To associate" refers to linking or relating related objects.

[0079] "Achieving results" refers to a state in which an employee is producing excellent results in a specific task.

[0080] "Practical information" refers to information about specific knowledge and skills that are useful in actual work and activities.

[0081] The system of this invention can improve skills more efficiently by utilizing the work environment and project information of employees. First, the user inputs their work environment information and activity information using a terminal. This information includes, for example, data on the work location and customer base. The terminal aggregates this information and transmits it to the server.

[0082] The server uses a data processing model to analyze information submitted by users and quantify the capabilities of the workforce. This data processing utilizes machine learning libraries such as TENSORFLOW® and Scikit-learn. This allows the server to clearly identify each workforce's strengths and areas for improvement, and based on this information, identify other workforces who are performing well under similar conditions.

[0083] Following this, the server matches identified business personnel with each other and plans and conducts training on an online platform. This training is conducted using tools such as Zoom and Microsoft Teams, and success stories and practical information are shared. Based on this information, users can make effective adjustments to their own work processes. After the training, the users' performance is evaluated, and feedback is given to the business personnel who provided the knowledge.

[0084] As a concrete example, consider a scenario where an employee working in an urban store and targeting a new customer segment utilizes this system. This employee inputs customer acquisition data and customer profile information into the system. The server analyzes this data, identifies other employees who have achieved success in similar situations, and provides a platform for learning effective approaches. As a result, efficient business improvement can be expected. An example of a prompt to the generated AI model would be, "Please tell me about effective marketing methods for targeting a new customer segment."

[0085] By utilizing this system, employees will have access to an environment that allows them to improve their work in a more specific and effective way.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] Users input their work environment and activity information into a terminal. This mainly consists of detailed data such as store location, customer age range, and customer traffic. The terminal checks the format of the input information and accurately transmits it to the server. The requirement for the input data is that all data fields necessary for subsequent processing are fully filled in.

[0089] Step 2:

[0090] The server receives data sent from the terminal. The received data is stored in the database by the server. During this storage process, the server checks whether the data format conforms to a specified format, and if there are any integrity issues, it sends a notification to the user requesting correction. The input is work environment information sent from the terminal, and the output is in a standardized data format.

[0091] Step 3:

[0092] The server performs analysis using stored data. This utilizes generative AI models. Specifically, it uses libraries such as TensorFlow and Scikit-learn to run models that quantify worker skills and performance from the data. The input is properly stored data, and the output is quantified performance indicators. Based on these indicators, areas requiring improvement or training are identified.

[0093] Step 4:

[0094] Based on the analysis results, the server identifies other workers who have demonstrated superior performance in similar work environments. This process uses an algorithm that references past performance data and matches workers with similar characteristics. The input is the worker's performance metrics, and the output is a list of matched workers.

[0095] Step 5:

[0096] The server plans and conducts online training for matched workers. This training planning utilizes platforms such as Zoom and Microsoft Teams, enabling participants to share success stories and specific methods. The input is a list of matched workers, and the output is the details of the scheduled training.

[0097] Step 6:

[0098] Users participate in training and apply what they learn to their actual work. After the training, the server re-evaluates the user's work performance and confirms improvement. Workers who provided knowledge are given feedback and reward points. Input is the user's work data after the training, and output is updated evaluation metrics and feedback.

[0099] (Application Example 1)

[0100] 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."

[0101] In logistics facilities, there is a lack of knowledge sharing among individual workers to efficiently manage inventory, and insufficient provision of successful methods suited to individual work environments. Therefore, it is necessary to improve work efficiency while responding to the fluctuating conditions of each store.

[0102] 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.

[0103] In this invention, the server includes means for acquiring operational environment information and work content information for each worker, means for analyzing the results of each worker using a generative model, and means for presenting efficient work procedures related to inventory management at a logistics facility. This makes it possible to share knowledge among workers and to immediately provide appropriate work methods according to the environment.

[0104] A "worker" is an individual who performs specific tasks in a logistics facility or various other work environments.

[0105] "Operating environment information" refers to information about environmental factors and conditions that affect workers' activities.

[0106] "Job description information" refers to information about the specific tasks and objectives that a worker is supposed to perform.

[0107] A "generative model" is a machine learning algorithm used to derive patterns and insights from large amounts of data.

[0108] "Analysis" is the process of performing evaluations and diagnoses based on acquired data, with a specific purpose or goal in mind.

[0109] "Mapping" is the act of finding relationships between multiple elements that have similar conditions and effectively linking them together.

[0110] "Education and training" refers to a program systematically provided to workers to acquire the knowledge and skills necessary to perform their duties.

[0111] A "logistics facility" is a place or building where goods are stored and shipped.

[0112] "Inventory management" refers to all operations necessary to effectively purchase, store, allocate, and ship goods within a logistics facility.

[0113] An "efficient work procedure" is a series of work processes designed to effectively achieve a goal by making the most of limited resources.

[0114] "Knowledge sharing" refers to the joint use and application of specific knowledge and expertise by multiple people.

[0115] This invention is a system for improving the efficiency of inventory management operations within a logistics facility. The system consists of a terminal carried by each worker and a server that supports it.

[0116] The server collects operational environment information and work content information received from workers and stores it in a database. This information is entered by workers using smartphones or smart glasses. The server manages the information via an API of the Django framework implemented in Python.

[0117] The system employs Scikit-learn as a generative model to analyze workers' activity history and work performance. Based on the patterns obtained from this analysis process, appropriate improvement procedures are derived for each worker. Furthermore, knowledge sharing is achieved through mapping these procedures to other workers. In addition, Unity is used to provide smart glasses with real-time, visual representations of efficient work procedures.

[0118] As a concrete example, worker A, who performs inventory counting in a logistics facility, uses this system. Worker A inputs their work details and current work environment using a terminal, and the server analyzes this information. From the generated data, methods that can achieve superior results under similar work conditions are suggested, enabling worker A to perform their work optimally. As a result, efficient inventory counting is performed, and the overall efficiency of the operation is improved.

[0119] As an example of a prompt, by inputting "Please provide examples of efficient methods for inventory management in logistics facilities" into the generating AI model, appropriate advice and methods can be obtained immediately.

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] Before starting work, users input operational environment information and work content information using their own terminals. The terminal displays an input form containing detailed data about the work environment conditions and current work objectives. The entered information is sent from the terminal to the server. The output at this stage is raw data obtained from the user.

[0123] Step 2:

[0124] The server stores the received operational environment information and business content information in a database. The database is managed using the Django framework. Next, the server uses the Scikit-learn library to run a generative model using the stored historical data and the newly sent data. Data processing in this step includes data normalization and feature extraction. The output is the result of the model's analysis.

[0125] Step 3:

[0126] The server uses the analysis results to identify and match other workers who have achieved high performance under similar operating conditions. This matching process compares the analysis results with past success data to select the most relevant methods. The output is a list of recommended methods and information on other workers.

[0127] Step 4:

[0128] Users receive analysis results and detailed recommendations for efficient work procedures from the server via their devices. The information is presented in a visually easy-to-understand format on the device interface. Users with smart glasses, in particular, are provided with real-time work assistance using AR technology. The output in this case serves as a guide to specific work procedures for the user.

[0129] Step 5:

[0130] The user performs the task based on the provided specific procedures and feeds back the performance data to the server via the terminal. The server monitors the results of the performed work and saves the feedback data to a database as material for improvement in the future. The output is new performance data for program adjustments aimed at improving the work.

[0131] 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.

[0132] This invention provides a system that combines an emotion engine to improve the performance of employees and maximize the effectiveness of training. This system incorporates elements of emotion management into analysis, matching, and online training based on work environment information and project information, enabling more personalized responses.

[0133] Data collection and analysis

[0134] Users use a terminal to input information about the store's work environment and tasks. The terminal sends this data to a server, which records it in a database. The server uses machine learning models to analyze the collected information and evaluate the performance of each worker. Based on the analysis, it identifies workers who have achieved success in similar environments.

[0135] Online training and emotional management

[0136] Online training sessions are conducted among matched professionals with the aim of sharing best practices. This is where an emotion engine installed on the server comes into play. The emotion engine analyzes the facial expressions and voice data of users participating in the training in real time to measure their emotional state. This information is used to determine whether the user is experiencing stress or if their understanding is progressing.

[0137] Individual feedback and evaluation

[0138] The server adjusts training content and feedback individually based on data obtained from the emotion engine. For example, if a user appears confused, it can provide detailed explanations and additional materials. Furthermore, in post-training performance evaluations, the emotion data can be used to provide appropriate assessments tailored to the user's situation. This strongly supports maintaining employee motivation and skill development.

[0139] Specific example

[0140] Let's say an employee is participating in training to learn sales techniques for a new product. If the emotion engine detects that the user's facial expression is tense during the online training, the server will adjust the pace of the training or prompt the user to take a break to enhance the learning effect. In this way, by making fine adjustments according to the user's emotional state, it is possible to support the achievement of the training objectives.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] Users use their own devices to input information such as store location, customer attributes, and details of sales opportunities.

[0144] Step 2:

[0145] The terminal processes the entered data and sends it to the server. The server stores that information in a database.

[0146] Step 3:

[0147] The server collects information stored in the database and uses machine learning models to analyze the performance metrics of each employee.

[0148] Step 4:

[0149] Based on the analysis results, the server identifies and matches other workers who are achieving high performance under similar environmental conditions.

[0150] Step 5:

[0151] The server schedules online training for matched job performers. The training aims to share best practices and facilitate learning.

[0152] Step 6:

[0153] During training, users participate via their devices, and the server uses an emotion engine to analyze the users' facial expressions and voice in real time, monitoring their emotional state.

[0154] Step 7:

[0155] The server adjusts the training content and pace in real time based on the user's emotional state, as recognized by the emotion engine. For example, if a user is having difficulty understanding, it provides detailed explanations and visual aids.

[0156] Step 8:

[0157] After the training is completed, users will apply what they have learned to their work and aim to improve their performance.

[0158] Step 9:

[0159] The server monitors the performance of employees after training and evaluates improvements in performance based on that data.

[0160] Step 10:

[0161] The server generates feedback based on the evaluation results and sends it to the user. It also takes into account the user's emotional data from the emotion engine to provide advice on areas for future improvement and ways to boost motivation.

[0162] (Example 2)

[0163] 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".

[0164] In today's work environment, improving the work efficiency of each user requires providing appropriate education and support while considering their individual work environment and emotional state. However, traditional education systems are uniform and struggle to respond flexibly to the specific circumstances and emotional states of users. Furthermore, these systems lack mechanisms for efficiently sharing success stories among users and improving performance. As a result, declining user motivation and stagnation in skill development are becoming challenges.

[0165] 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.

[0166] In this invention, the server includes means for collecting information about each user's work environment and work information, means for analyzing each user's work efficiency using a learning algorithm, and means for analyzing the user's emotional state and adjusting the content of education and the information provided based on the user's emotional state. This enables more individualized responses for each user, helping to maintain motivation and improve skills. Furthermore, sharing success stories among users can improve the overall performance of the organization.

[0167] "User" refers to an individual who operates the system and inputs work environment information and job information.

[0168] "Information regarding the work environment" refers to data concerning the physical and conditional circumstances under which work is performed, such as temperature, humidity, and the status of work equipment.

[0169] "Work information" refers to data regarding the details and progress of ongoing projects.

[0170] A "learning algorithm" refers to the process of analyzing data using machine learning techniques and evaluating the user's work efficiency based on the results.

[0171] "Emotional state" refers to the mental condition analyzed based on the user's facial expressions and voice.

[0172] "Educational content" refers to the specific information and materials regarding the training and instruction that users receive.

[0173] "Means of collecting information" refers to the methods and processes for obtaining information about the work environment and work from the user.

[0174] "Means of analyzing information" refers to methods and techniques for processing collected data to gain useful insights.

[0175] "Means of adjusting information" refers to the process of optimizing the content of education and instruction provided to users based on the analyzed results.

[0176] This invention is a system that collects user work environment information and work information, and maximizes work efficiency and learning effectiveness through analysis based on this information. To achieve this, the server, terminal, and user interface work together to build a complex data processing and feedback loop.

[0177] The user uses a terminal to input information about the work environment and the job itself. This input includes specific data such as temperature, humidity, noise level at the work site, and current work progress. The terminal then transmits this information to the server in an appropriate format.

[0178] The server stores the submitted information in a database and then analyzes the data using a learning algorithm. This analysis utilizes machine learning libraries such as TensorFlow to evaluate each user's work efficiency and potential problems. This evaluation has the potential to suggest how each user can improve their performance.

[0179] Furthermore, the server is equipped with an emotion analysis engine. During training, it analyzes the user's facial expressions and voice data in real time to infer their emotional state. Based on these results, the server dynamically adjusts the content of the training and instruction. For example, if the user is feeling stressed, the server can slow down the pace of the training and provide the user with additional materials to improve their understanding.

[0180] As a concrete example, consider a scenario where a user participates in an online training session to learn sales techniques for a new product. During this training, the server senses the participant's level of tension from their facial expressions through the online conferencing system. Based on this, the server adjusts the training schedule and prompts breaks as needed to enhance learning effectiveness.

[0181] By utilizing a generative AI model, the server generates prompts such as, "When conducting online training on sales methods for a new product, how can the training content be adjusted based on the emotional state of the participants to be most effective?" and optimizes the instruction. This system improves the user's work efficiency while enhancing learning effectiveness through personalized support.

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] The user uses a terminal to input work environment information and work information. This input includes temperature and humidity of the work area, the status of the equipment being used, and details about the ongoing task. The terminal transmits this information to the server as digital data. The input for this process is environmental data obtained from manual user input and sensors, and the output is structured data sent to the server.

[0185] Step 2:

[0186] The server stores received work environment information and job information in a database. The stored data is associated with time stamps and recorded in a format suitable for subsequent analysis. The input is structured data sent from the terminal, and the output is historical data stored in the database.

[0187] Step 3:

[0188] The server performs analysis using a learning algorithm based on the stored data. In this step, machine learning libraries such as TensorFlow are used to evaluate each user's work efficiency and potential areas for improvement. The input is historical data stored in the database, and the output is a performance report for each user.

[0189] Step 4:

[0190] Based on the analysis results, the server matches users with successful track records in similar environments. This matching process performs optimal pairing based on past successes and specific business scenarios. The input is a performance report, and the output is a list of matched user pairs.

[0191] Step 5:

[0192] The server schedules and conducts training sessions to allow matched users to share success stories. This training is conducted in real-time via an online conferencing system, enabling information exchange through video calls. Inputs include a pair list and schedule information, while output is a log of the training implementation status.

[0193] Step 6:

[0194] During the learning process, the server uses an emotion analysis engine to analyze the user's facial expressions and voice, and estimates their emotional state in real time. Based on this data, it determines the user's level of comprehension and stress. Input is video and audio data, and output is an emotional state report.

[0195] Step 7:

[0196] The server dynamically adjusts the educational content based on the emotional state report. For example, if it determines that understanding is not progressing, the server adjusts the pace of the lesson or provides additional materials. The input is the emotional state report, and the output is customized educational content.

[0197] (Application Example 2)

[0198] 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".

[0199] In today's work environment, the challenges faced by workers are becoming increasingly complex. In particular, achieving both increased efficiency and improved employee skills simultaneously is difficult. Traditional training programs struggle to provide feedback tailored to the individual worker's emotions and level of understanding, which hinders skill development. Furthermore, the lack of technology to enable real-time, emotion-sensitive feedback prevents maximizing employee performance.

[0200] 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.

[0201] In this invention, the server includes means for collecting work environment information and case information for each worker; means for analyzing each worker's abilities using a machine learning algorithm based on the collected information; and means for adjusting training content and feedback using an emotion engine that analyzes the worker's facial expressions and voice data in real time and measures their emotional state. This enables detailed training tailored to each worker's emotions and level of understanding.

[0202] "Workers" refers to individuals or teams performing tasks in a specific environment, whose skills and knowledge are essential to the success of the work.

[0203] "Work environment information" refers to the physical and organizational conditions related to the work performed by the worker, and includes machinery and equipment, work procedures, and the conditions of the work site.

[0204] "Project information" refers to detailed data about a specific task or operation, including information such as the project's objectives, schedule, progress, and related resources.

[0205] A "machine learning algorithm" refers to a mathematical method that analyzes patterns based on collected data and automatically makes predictions and decisions from that data.

[0206] "Analyzing capabilities" means evaluating how efficiently and accurately a worker can perform a task under specific conditions.

[0207] The term "emotion engine" refers to a technology that analyzes workers' facial expressions and voice data in real time to estimate their emotions, thereby making it possible to understand the workers' emotional state.

[0208] "Adjusting training content and feedback" means optimizing the content and methodology of the training program, while taking into account the feelings and level of understanding of the workers, in order to provide an effective learning experience for each worker.

[0209] The program for the system realizing this invention begins by collecting work environment information and project information for each worker via a terminal and transmitting it to a server. The server stores the collected information in a database and uses a machine learning algorithm to evaluate the capabilities of each worker. This allows the server to match workers who have achieved success in similar environments.

[0210] When training is conducted online, the server uses an emotion engine to analyze the participants' facial expressions and voices in real time and measure their emotional state. This emotional data is then used by the server to adjust the training content and pace, and to provide individualized feedback in real time.

[0211] In terms of specific hardware, workers will use devices such as smart glasses and tablets, while the server will be equipped with a database and an emotion analysis engine. The software will include machine learning algorithms and facial recognition libraries (such as OpenCV and Microsoft Cognitive Services). This will enable a system that can handle everything from data acquisition and analysis to providing appropriate feedback in one integrated process.

[0212] As a concrete example, imagine a new employee participating in online training to learn how to operate a factory robot. The server detects high levels of tension from the worker's facial expressions and automatically slows the training pace and adds supplementary explanations. This creates an environment that facilitates the new employee's understanding.

[0213] An example of a prompt using a generative AI model is: "Assess whether the robot operator is experiencing stress during the process of learning a new work process, and explain what approaches would be effective in maximizing work efficiency." Based on this prompt, the generative AI model can propose appropriate training adjustments.

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] Users input work environment information and project information using a terminal. The entered data is sent from the terminal to the server. This digitizes the details of the work site and projects.

[0217] Step 2:

[0218] The server stores the received data in a database. Based on the stored information, it activates a machine learning algorithm to begin evaluating each user's work performance. As an output of the analysis, each user's ability level and characteristics are generated.

[0219] Step 3:

[0220] Based on the results of the machine learning algorithm, the server identifies and matches other users who have achieved high results under similar conditions. During this matching process, a pairing list of users with successful track records in similar environments is generated.

[0221] Step 4:

[0222] Online training sessions are scheduled and conducted between matched users. The server performs real-time emotion analysis, inputting users' facial expressions and voice data into an emotion engine to obtain their emotional state. This allows the system to understand the users' stress levels and concentration levels.

[0223] Step 5:

[0224] The server dynamically adjusts the pace and content of online training based on emotional data. If necessary, it individually optimizes the training content and presents slides and additional explanatory materials to deepen understanding. This makes it possible to provide feedback tailored to each user's level of comprehension.

[0225] Step 6:

[0226] After the training is completed, the server analyzes the results of the training and provides feedback to the user. This feedback is based on emotional and performance data from the training and includes specific areas for improvement and success stories. It also uses a generating AI model to provide example prompts for the next training session.

[0227] 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.

[0228] Data generation model 58 is a type of 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.

[0229] 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.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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".

[0243] This invention provides a system that can perform appropriate performance improvement matching based on the work environment and project information of the workforce. This system supports the skill improvement of workforce through data collection, analysis, matching, training, and evaluation.

[0244] Data collection

[0245] At system startup, users enter their environment information and details of their assigned projects into a terminal. This information includes store location, customer demographics, and customer traffic. The terminal collects the entered data and sends it to the server.

[0246] Data analysis and matching

[0247] The collected information is managed on a server. The server uses machine learning models to analyze the work environment and project information of each employee and calculate various performance indicators. This allows the server to identify and match other employees who are performing well under similar conditions.

[0248] Training and feedback

[0249] The server coordinates online training sessions between matched professionals. This training provides a platform for sharing specific success stories and effective methods. Users can participate in this training and apply the learned methods to their actual work, thereby improving efficiency and achieving better results. After the training, the server monitors the professionals' performance and evaluates them based on improvements in results. Professionals who shared knowledge are awarded evaluation points and provided with feedback.

[0250] Specific example

[0251] For example, suppose a salesperson selling smartphones at a suburban store uses this system. This salesperson inputs data indicating that the customer base is primarily young people and that customer traffic fluctuates seasonally. The server analyzes this data and identifies other salespeople who have achieved high sales performance in similar environments. Online training is conducted with these identified salespeople, allowing them to learn effective sales strategies on the spot and improve sales performance at the suburban store. If performance improves after the training, the salesperson who provided the knowledge is awarded points as a reward.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] Users use their own devices to input work environment information such as store location, customer base, and project characteristics, as well as project information.

[0255] Step 2:

[0256] The terminal formats the input data and sends it to the server as a data packet.

[0257] Step 3:

[0258] The server records the received data in a database and aggregates information on all employees involved in the work.

[0259] Step 4:

[0260] The server uses the database information to run a machine learning model to calculate performance metrics for each employee.

[0261] Step 5:

[0262] Based on the calculated performance metrics, the server identifies employees who are performing well in similar environments.

[0263] Step 6:

[0264] The server matches identified personnel with online training sessions based on the matching results.

[0265] Step 7:

[0266] The server notifies users' terminals of the details of the planned training and prepares for participants to connect in real time.

[0267] Step 8:

[0268] Users participate in online training at designated times, learning success stories and techniques from other professionals.

[0269] Step 9:

[0270] The server monitors the performance of employees after they complete the training and records their results in a database.

[0271] Step 10:

[0272] If an improvement in performance is confirmed, the server adds evaluation points to the employees who shared the knowledge and sends the feedback information to the user's terminal.

[0273] (Example 1)

[0274] 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."

[0275] In today's work environment, employees need to adapt to diverse work environments and customer needs. However, individual employees are often limited by their own experience and knowledge, making it difficult for them to obtain the information necessary to achieve optimal performance. Therefore, there is a growing need for support systems that enable employees to efficiently improve their skills and enhance their results.

[0276] 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.

[0277] In this invention, the server includes means for aggregating work environment information and activity information of each worker, means for analyzing the capabilities of each worker using a data processing model based on the aggregated information, and means for matching workers who have achieved results in similar environments based on the analysis results. This makes it possible for workers to obtain practical information that is optimal for their own work environment and to efficiently improve their performance.

[0278] "Worker" refers to an individual engaged in a specific task or operation.

[0279] "Business environment information" refers to information regarding the physical and social factors when an operator conducts business.

[0280] "Activity information" refers to data and records generated in the process of an operator accomplishing business.

[0281] "To aggregate" means to organize individual data and information and put them together in one coherent form.

[0282] "Data processing model" refers to a method for analyzing, predicting, or classifying data using specific algorithms or techniques.

[0283] "To analyze capabilities" means to evaluate an operator's performance and skills based on objective data.

[0284] "Similar environments" means that the situations where different operators are placed have common characteristics.

[0285] "To associate" means to link or relate relevant objects.

[0286] "Having achieved good results" means the state where an operator has achieved excellent results in a specific business.

[0287] "Practical information" refers to information regarding specific knowledge and techniques useful in the actual business and activity sites.

[0288] The system of this invention can more efficiently improve skills by using the working environment of business practitioners and case information. The user first inputs their working environment information and activity information using a terminal. This information includes, for example, data regarding the business location conditions and customer segments. The terminal aggregates this information and sends it to the server.

[0289] The server uses a data processing model to analyze information submitted by users and quantify the capabilities of the workforce. This data processing utilizes machine learning libraries such as TensorFlow and Scikit-learn. This allows the server to clearly identify each workforce's strengths and areas for improvement, and based on this information, identify other workforces who are performing well under similar conditions.

[0290] Following this, the server matches identified business personnel with each other and plans and conducts training on an online platform. This training is conducted using tools such as Zoom and Microsoft Teams, and success stories and practical information are shared. Based on this information, users can make effective adjustments to their own work processes. After the training, the users' performance is evaluated, and feedback is given to the business personnel who provided the knowledge.

[0291] As a concrete example, consider a scenario where an employee working in an urban store and targeting a new customer segment utilizes this system. This employee inputs customer acquisition data and customer profile information into the system. The server analyzes this data, identifies other employees who have achieved success in similar situations, and provides a platform for learning effective approaches. As a result, efficient business improvement can be expected. An example of a prompt to the generated AI model would be, "Please tell me about effective marketing methods for targeting a new customer segment."

[0292] By utilizing this system, employees will have access to an environment that allows them to improve their work in a more specific and effective way.

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] Users input their work environment and activity information into a terminal. This mainly consists of detailed data such as store location, customer age range, and customer traffic. The terminal checks the format of the input information and accurately transmits it to the server. The requirement for the input data is that all data fields necessary for subsequent processing are fully filled in.

[0296] Step 2:

[0297] The server receives data sent from the terminal. The received data is stored in the database by the server. During this storage process, the server checks whether the data format conforms to a specified format, and if there are any integrity issues, it sends a notification to the user requesting correction. The input is work environment information sent from the terminal, and the output is in a standardized data format.

[0298] Step 3:

[0299] The server performs analysis using stored data. This utilizes generative AI models. Specifically, it uses libraries such as TensorFlow and Scikit-learn to run models that quantify worker skills and performance from the data. The input is properly stored data, and the output is quantified performance indicators. Based on these indicators, areas requiring improvement or training are identified.

[0300] Step 4:

[0301] Based on the analysis results, the server identifies other workers who have demonstrated superior performance in similar work environments. This process uses an algorithm that references past performance data and matches workers with similar characteristics. The input is the worker's performance metrics, and the output is a list of matched workers.

[0302] Step 5:

[0303] The server plans and conducts training for the matched workers using an online platform. In this training plan, Zoom or Microsoft Teams is used to enable the sharing of success stories and specific methods among participants. The input is a list of the matched workers, and the output is the details of the scheduled training.

[0304] Step 6:

[0305] The user participates in the training and reflects what they have learned in their actual work. After the training, the server re-evaluates the user's work performance and confirms the improvement in results. Reward points are given to the workers who provided the knowledge, along with feedback. The input is the user's work data after participating in the training, and the output is the updated evaluation metrics and feedback.

[0306] (Application Example 1)

[0307] 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".

[0308] In a logistics facility, there is insufficient knowledge sharing for individual workers to efficiently manage inventory and provision of success methods suitable for individual working environments. Therefore, it is required to improve work efficiency while coping with the varying conditions for each store.

[0309] 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.

[0310] In this invention, the server includes means for acquiring the operation environment information and work content information of each worker, means for analyzing the results of each worker using a generation model, and means for presenting an efficient work procedure regarding inventory management in a logistics facility. Thereby, it becomes possible to share knowledge among workers and immediately provide appropriate work methods according to the environment.

[0311] A "worker" is an individual who performs specific tasks in a logistics facility or various other work environments.

[0312] "Operating environment information" refers to information about environmental factors and conditions that affect workers' activities.

[0313] "Job description information" refers to information about the specific tasks and objectives that a worker is supposed to perform.

[0314] A "generative model" is a machine learning algorithm used to derive patterns and insights from large amounts of data.

[0315] "Analysis" is the process of performing evaluations and diagnoses based on acquired data, with a specific purpose or goal in mind.

[0316] "Mapping" is the act of finding relationships between multiple elements that have similar conditions and effectively linking them together.

[0317] "Education and training" refers to a program systematically provided to workers to acquire the knowledge and skills necessary to perform their duties.

[0318] A "logistics facility" is a place or building where goods are stored and shipped.

[0319] "Inventory management" refers to all operations necessary to effectively purchase, store, allocate, and ship goods within a logistics facility.

[0320] An "efficient work procedure" is a series of work processes designed to effectively achieve a goal by making the most of limited resources.

[0321] "Knowledge sharing" refers to the joint use and application of specific knowledge and expertise by multiple people.

[0322] This invention is a system for improving the efficiency of inventory management operations within a logistics facility. The system consists of a terminal carried by each worker and a server that supports it.

[0323] The server collects operational environment information and work content information received from workers and stores it in a database. This information is entered by workers using smartphones or smart glasses. The server manages the information via an API of the Django framework implemented in Python.

[0324] The system employs Scikit-learn as a generative model to analyze workers' activity history and work performance. Based on the patterns obtained from this analysis process, appropriate improvement procedures are derived for each worker. Furthermore, knowledge sharing is achieved through mapping these procedures to other workers. In addition, Unity is used to provide smart glasses with real-time, visual representations of efficient work procedures.

[0325] As a concrete example, worker A, who performs inventory counting in a logistics facility, uses this system. Worker A inputs their work details and current work environment using a terminal, and the server analyzes this information. From the generated data, methods that can achieve superior results under similar work conditions are suggested, enabling worker A to perform their work optimally. As a result, efficient inventory counting is performed, and the overall efficiency of the operation is improved.

[0326] As an example of a prompt, by inputting "Please provide examples of efficient methods for inventory management in logistics facilities" into the generating AI model, appropriate advice and methods can be obtained immediately.

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] Before starting work, users input operational environment information and work content information using their own terminals. The terminal displays an input form containing detailed data about the work environment conditions and current work objectives. The entered information is sent from the terminal to the server. The output at this stage is raw data obtained from the user.

[0330] Step 2:

[0331] The server stores the received operational environment information and business content information in a database. The database is managed using the Django framework. Next, the server uses the Scikit-learn library to run a generative model using the stored historical data and the newly sent data. Data processing in this step includes data normalization and feature extraction. The output is the result of the model's analysis.

[0332] Step 3:

[0333] The server uses the analysis results to identify and match other workers who have achieved high performance under similar operating conditions. This matching process compares the analysis results with past success data to select the most relevant methods. The output is a list of recommended methods and information on other workers.

[0334] Step 4:

[0335] Users receive analysis results and detailed recommendations for efficient work procedures from the server via their devices. The information is presented in a visually easy-to-understand format on the device interface. Users with smart glasses, in particular, are provided with real-time work assistance using AR technology. The output in this case serves as a guide to specific work procedures for the user.

[0336] Step 5:

[0337] The user performs the task based on the provided specific procedures and feeds back the performance data to the server via the terminal. The server monitors the results of the performed work and saves the feedback data to a database as material for improvement in the future. The output is new performance data for program adjustments aimed at improving the work.

[0338] 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.

[0339] This invention provides a system that combines an emotion engine to improve the performance of employees and maximize the effectiveness of training. This system incorporates elements of emotion management into analysis, matching, and online training based on work environment information and project information, enabling more personalized responses.

[0340] Data collection and analysis

[0341] Users use a terminal to input information about the store's work environment and tasks. The terminal sends this data to a server, which records it in a database. The server uses machine learning models to analyze the collected information and evaluate the performance of each worker. Based on the analysis, it identifies workers who have achieved success in similar environments.

[0342] Online training and emotional management

[0343] Online training sessions are conducted among matched professionals with the aim of sharing best practices. This is where an emotion engine installed on the server comes into play. The emotion engine analyzes the facial expressions and voice data of users participating in the training in real time to measure their emotional state. This information is used to determine whether the user is experiencing stress or if their understanding is progressing.

[0344] Individual feedback and evaluation

[0345] The server adjusts training content and feedback individually based on data obtained from the emotion engine. For example, if a user appears confused, it can provide detailed explanations and additional materials. Furthermore, in post-training performance evaluations, the emotion data can be used to provide appropriate assessments tailored to the user's situation. This strongly supports maintaining employee motivation and skill development.

[0346] Specific example

[0347] Let's say an employee is participating in training to learn sales techniques for a new product. If the emotion engine detects that the user's facial expression is tense during the online training, the server will adjust the pace of the training or prompt the user to take a break to enhance the learning effect. In this way, by making fine adjustments according to the user's emotional state, it is possible to support the achievement of the training objectives.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] Users use their own devices to input information such as store location, customer attributes, and details of sales opportunities.

[0351] Step 2:

[0352] The terminal processes the entered data and sends it to the server. The server stores that information in a database.

[0353] Step 3:

[0354] The server collects information stored in the database and uses machine learning models to analyze the performance metrics of each employee.

[0355] Step 4:

[0356] Based on the analysis results, the server identifies and matches other workers who are achieving high performance under similar environmental conditions.

[0357] Step 5:

[0358] The server schedules online training for matched job performers. The training aims to share best practices and facilitate learning.

[0359] Step 6:

[0360] During training, users participate via their devices, and the server uses an emotion engine to analyze the users' facial expressions and voice in real time, monitoring their emotional state.

[0361] Step 7:

[0362] The server adjusts the training content and pace in real time based on the user's emotional state, as recognized by the emotion engine. For example, if a user is having difficulty understanding, it provides detailed explanations and visual aids.

[0363] Step 8:

[0364] After the training is completed, users will apply what they have learned to their work and aim to improve their performance.

[0365] Step 9:

[0366] The server monitors the performance of employees after training and evaluates improvements in performance based on that data.

[0367] Step 10:

[0368] The server generates feedback based on the evaluation results and sends it to the user. It also takes into account the user's emotional data from the emotion engine to provide advice on areas for future improvement and ways to boost motivation.

[0369] (Example 2)

[0370] 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".

[0371] In today's work environment, improving the work efficiency of each user requires providing appropriate education and support while considering their individual work environment and emotional state. However, traditional education systems are uniform and struggle to respond flexibly to the specific circumstances and emotional states of users. Furthermore, these systems lack mechanisms for efficiently sharing success stories among users and improving performance. As a result, declining user motivation and stagnation in skill development are becoming challenges.

[0372] 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.

[0373] In this invention, the server includes means for collecting information about each user's work environment and work information, means for analyzing each user's work efficiency using a learning algorithm, and means for analyzing the user's emotional state and adjusting the content of education and the information provided based on the user's emotional state. This enables more individualized responses for each user, helping to maintain motivation and improve skills. Furthermore, sharing success stories among users can improve the overall performance of the organization.

[0374] "User" refers to an individual who operates the system and inputs work environment information and job information.

[0375] "Information regarding the work environment" refers to data concerning the physical and conditional circumstances under which work is performed, such as temperature, humidity, and the status of work equipment.

[0376] "Work information" refers to data regarding the details and progress of ongoing projects.

[0377] A "learning algorithm" refers to the process of analyzing data using machine learning techniques and evaluating the user's work efficiency based on the results.

[0378] "Emotional state" refers to the mental condition analyzed based on the user's facial expressions and voice.

[0379] "Educational content" refers to the specific information and materials regarding the training and instruction that users receive.

[0380] "Means of collecting information" refers to the methods and processes for obtaining information about the work environment and work from the user.

[0381] "Means of analyzing information" refers to methods and techniques for processing collected data to gain useful insights.

[0382] "Means of adjusting information" refers to the process of optimizing the content of education and instruction provided to users based on the analyzed results.

[0383] This invention is a system that collects user work environment information and work information, and maximizes work efficiency and learning effectiveness through analysis based on this information. To achieve this, the server, terminal, and user interface work together to build a complex data processing and feedback loop.

[0384] The user uses a terminal to input information about the work environment and the job itself. This input includes specific data such as temperature, humidity, noise level at the work site, and current work progress. The terminal then transmits this information to the server in an appropriate format.

[0385] The server stores the submitted information in a database and then analyzes the data using a learning algorithm. This analysis utilizes machine learning libraries such as TensorFlow to evaluate each user's work efficiency and potential problems. This evaluation has the potential to suggest how each user can improve their performance.

[0386] Furthermore, the server is equipped with an emotion analysis engine. During training, it analyzes the user's facial expressions and voice data in real time to infer their emotional state. Based on these results, the server dynamically adjusts the content of the training and instruction. For example, if the user is feeling stressed, the server can slow down the pace of the training and provide the user with additional materials to improve their understanding.

[0387] As a concrete example, consider a scenario where a user participates in an online training session to learn sales techniques for a new product. During this training, the server senses the participant's level of tension from their facial expressions through the online conferencing system. Based on this, the server adjusts the training schedule and prompts breaks as needed to enhance learning effectiveness.

[0388] By utilizing a generative AI model, the server generates prompts such as, "When conducting online training on sales methods for a new product, how can the training content be adjusted based on the emotional state of the participants to be most effective?" and optimizes the instruction. This system improves the user's work efficiency while enhancing learning effectiveness through personalized support.

[0389] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0390] Step 1:

[0391] The user uses a terminal to input work environment information and work information. This input includes temperature and humidity of the work area, the status of the equipment being used, and details about the ongoing task. The terminal transmits this information to the server as digital data. The input for this process is environmental data obtained from manual user input and sensors, and the output is structured data sent to the server.

[0392] Step 2:

[0393] The server stores received work environment information and job information in a database. The stored data is associated with time stamps and recorded in a format suitable for subsequent analysis. The input is structured data sent from the terminal, and the output is historical data stored in the database.

[0394] Step 3:

[0395] The server performs analysis using a learning algorithm based on the stored data. In this step, machine learning libraries such as TensorFlow are used to evaluate each user's work efficiency and potential areas for improvement. The input is historical data stored in the database, and the output is a performance report for each user.

[0396] Step 4:

[0397] Based on the analysis results, the server matches users with successful track records in similar environments. This matching process performs optimal pairing based on past successes and specific business scenarios. The input is a performance report, and the output is a list of matched user pairs.

[0398] Step 5:

[0399] The server schedules and conducts training sessions to allow matched users to share success stories. This training is conducted in real-time via an online conferencing system, enabling information exchange through video calls. Inputs include a pair list and schedule information, while output is a log of the training implementation status.

[0400] Step 6:

[0401] During the learning process, the server uses an emotion analysis engine to analyze the user's facial expressions and voice, and estimates their emotional state in real time. Based on this data, it determines the user's level of comprehension and stress. Input is video and audio data, and output is an emotional state report.

[0402] Step 7:

[0403] The server dynamically adjusts the educational content based on the emotional state report. For example, if it determines that understanding is not progressing, the server adjusts the pace of the lesson or provides additional materials. The input is the emotional state report, and the output is customized educational content.

[0404] (Application Example 2)

[0405] 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."

[0406] In today's work environment, the challenges faced by workers are becoming increasingly complex. In particular, achieving both increased efficiency and improved employee skills simultaneously is difficult. Traditional training programs struggle to provide feedback tailored to the individual worker's emotions and level of understanding, which hinders skill development. Furthermore, the lack of technology to enable real-time, emotion-sensitive feedback prevents maximizing employee performance.

[0407] 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.

[0408] In this invention, the server includes means for collecting work environment information and case information for each worker; means for analyzing each worker's abilities using a machine learning algorithm based on the collected information; and means for adjusting training content and feedback using an emotion engine that analyzes the worker's facial expressions and voice data in real time and measures their emotional state. This enables detailed training tailored to each worker's emotions and level of understanding.

[0409] "Workers" refers to individuals or teams performing tasks in a specific environment, whose skills and knowledge are essential to the success of the work.

[0410] "Work environment information" refers to the physical and organizational conditions related to the work performed by the worker, and includes machinery and equipment, work procedures, and the conditions of the work site.

[0411] "Project information" refers to detailed data about a specific task or operation, including information such as the project's objectives, schedule, progress, and related resources.

[0412] A "machine learning algorithm" refers to a mathematical method that analyzes patterns based on collected data and automatically makes predictions and decisions from that data.

[0413] "Analyzing capabilities" means evaluating how efficiently and accurately a worker can perform a task under specific conditions.

[0414] The term "emotion engine" refers to a technology that analyzes workers' facial expressions and voice data in real time to estimate their emotions, thereby making it possible to understand the workers' emotional state.

[0415] "Adjusting training content and feedback" means optimizing the content and methodology of the training program, while taking into account the feelings and level of understanding of the workers, in order to provide an effective learning experience for each worker.

[0416] The program for the system realizing this invention begins by collecting work environment information and project information for each worker via a terminal and transmitting it to a server. The server stores the collected information in a database and uses a machine learning algorithm to evaluate the capabilities of each worker. This allows the server to match workers who have achieved success in similar environments.

[0417] When training is conducted online, the server uses an emotion engine to analyze the participants' facial expressions and voices in real time and measure their emotional state. This emotional data is then used by the server to adjust the training content and pace, and to provide individualized feedback in real time.

[0418] In terms of specific hardware, workers will use devices such as smart glasses and tablets, while the server will be equipped with a database and an emotion analysis engine. The software will include machine learning algorithms and facial recognition libraries (such as OpenCV and Microsoft Cognitive Services). This will enable a system that can handle everything from data acquisition and analysis to providing appropriate feedback in one integrated process.

[0419] As a concrete example, imagine a new employee participating in online training to learn how to operate a factory robot. The server detects high levels of tension from the worker's facial expressions and automatically slows the training pace and adds supplementary explanations. This creates an environment that facilitates the new employee's understanding.

[0420] An example of a prompt using a generative AI model is: "Assess whether the robot operator is experiencing stress during the process of learning a new work process, and explain what approaches would be effective in maximizing work efficiency." Based on this prompt, the generative AI model can propose appropriate training adjustments.

[0421] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0422] Step 1:

[0423] Users input work environment information and project information using a terminal. The entered data is sent from the terminal to the server. This digitizes the details of the work site and projects.

[0424] Step 2:

[0425] The server stores the received data in a database. Based on the stored information, it activates a machine learning algorithm to begin evaluating each user's work performance. As an output of the analysis, each user's ability level and characteristics are generated.

[0426] Step 3:

[0427] Based on the results of the machine learning algorithm, the server identifies and matches other users who have achieved high results under similar conditions. During this matching process, a pairing list of users with successful track records in similar environments is generated.

[0428] Step 4:

[0429] Online training sessions are scheduled and conducted between matched users. The server performs real-time emotion analysis, inputting users' facial expressions and voice data into an emotion engine to obtain their emotional state. This allows the system to understand the users' stress levels and concentration levels.

[0430] Step 5:

[0431] The server dynamically adjusts the pace and content of online training based on emotional data. If necessary, it individually optimizes the training content and presents slides and additional explanatory materials to deepen understanding. This makes it possible to provide feedback tailored to each user's level of comprehension.

[0432] Step 6:

[0433] After the training is completed, the server analyzes the results of the training and provides feedback to the user. This feedback is based on emotional and performance data from the training and includes specific areas for improvement and success stories. It also uses a generating AI model to provide example prompts for the next training session.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] [Third Embodiment]

[0438] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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".

[0450] This invention provides a system that can perform appropriate performance improvement matching based on the work environment and project information of the workforce. This system supports the skill improvement of workforce through data collection, analysis, matching, training, and evaluation.

[0451] Data collection

[0452] At system startup, users enter their environment information and details of their assigned projects into a terminal. This information includes store location, customer demographics, and customer traffic. The terminal collects the entered data and sends it to the server.

[0453] Data analysis and matching

[0454] The collected information is managed on a server. The server uses machine learning models to analyze the work environment and project information of each employee and calculate various performance indicators. This allows the server to identify and match other employees who are performing well under similar conditions.

[0455] Training and feedback

[0456] The server coordinates online training sessions between matched professionals. This training provides a platform for sharing specific success stories and effective methods. Users can participate in this training and apply the learned methods to their actual work, thereby improving efficiency and achieving better results. After the training, the server monitors the professionals' performance and evaluates them based on improvements in results. Professionals who shared knowledge are awarded evaluation points and provided with feedback.

[0457] Specific example

[0458] For example, suppose a salesperson selling smartphones at a suburban store uses this system. This salesperson inputs data indicating that the customer base is primarily young people and that customer traffic fluctuates seasonally. The server analyzes this data and identifies other salespeople who have achieved high sales performance in similar environments. Online training is conducted with these identified salespeople, allowing them to learn effective sales strategies on the spot and improve sales performance at the suburban store. If performance improves after the training, the salesperson who provided the knowledge is awarded points as a reward.

[0459] The following describes the processing flow.

[0460] Step 1:

[0461] Users use their own devices to input work environment information such as store location, customer base, and project characteristics, as well as project information.

[0462] Step 2:

[0463] The terminal formats the input data and sends it to the server as a data packet.

[0464] Step 3:

[0465] The server records the received data in a database and aggregates information on all employees involved in the work.

[0466] Step 4:

[0467] The server uses the database information to run a machine learning model to calculate performance metrics for each employee.

[0468] Step 5:

[0469] Based on the calculated performance metrics, the server identifies employees who are performing well in similar environments.

[0470] Step 6:

[0471] The server matches identified personnel with online training sessions based on the matching results.

[0472] Step 7:

[0473] The server notifies users' terminals of the details of the planned training and prepares for participants to connect in real time.

[0474] Step 8:

[0475] Users participate in online training at designated times, learning success stories and techniques from other professionals.

[0476] Step 9:

[0477] The server monitors the performance of employees after they complete the training and records their results in a database.

[0478] Step 10:

[0479] If an improvement in performance is confirmed, the server adds evaluation points to the employees who shared the knowledge and sends the feedback information to the user's terminal.

[0480] (Example 1)

[0481] 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."

[0482] In today's work environment, employees need to adapt to diverse work environments and customer needs. However, individual employees are often limited by their own experience and knowledge, making it difficult for them to obtain the information necessary to achieve optimal performance. Therefore, there is a growing need for support systems that enable employees to efficiently improve their skills and enhance their results.

[0483] 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.

[0484] In this invention, the server includes means for aggregating work environment information and activity information of each worker, means for analyzing the capabilities of each worker using a data processing model based on the aggregated information, and means for matching workers who have achieved results in similar environments based on the analysis results. This makes it possible for workers to obtain practical information that is optimal for their own work environment and to efficiently improve their performance.

[0485] "Worker" refers to an individual engaged in a specific task or operation.

[0486] "Work environment information" refers to information regarding the physical and social factors that affect workers when they perform their duties.

[0487] "Activity information" refers to data and records generated by workers in the process of performing their duties.

[0488] "To aggregate" refers to organizing individual data or pieces of information and combining them into a single, cohesive format.

[0489] A "data processing model" refers to a method for analyzing, predicting, or classifying data using specific algorithms or techniques.

[0490] "Analyzing capabilities" refers to evaluating a worker's performance and skills based on objective data.

[0491] "Similar environments" refer to situations in which different workers find themselves sharing common characteristics.

[0492] "To associate" refers to linking or relating related objects.

[0493] "Achieving results" refers to a state in which an employee is producing excellent results in a specific task.

[0494] "Practical information" refers to information about specific knowledge and skills that are useful in actual work and activities.

[0495] The system of this invention can improve skills more efficiently by utilizing the work environment and project information of employees. First, the user inputs their work environment information and activity information using a terminal. This information includes, for example, data on the work location and customer base. The terminal aggregates this information and transmits it to the server.

[0496] The server uses a data processing model to analyze information submitted by users and quantify the capabilities of the workforce. This data processing utilizes machine learning libraries such as TensorFlow and Scikit-learn. This allows the server to clearly identify each workforce's strengths and areas for improvement, and based on this information, identify other workforces who are performing well under similar conditions.

[0497] Following this, the server matches identified business personnel with each other and plans and conducts training on an online platform. This training is conducted using tools such as Zoom and Microsoft Teams, and success stories and practical information are shared. Based on this information, users can make effective adjustments to their own work processes. After the training, the users' performance is evaluated, and feedback is given to the business personnel who provided the knowledge.

[0498] As a concrete example, consider a scenario where an employee working in an urban store and targeting a new customer segment utilizes this system. This employee inputs customer acquisition data and customer profile information into the system. The server analyzes this data, identifies other employees who have achieved success in similar situations, and provides a platform for learning effective approaches. As a result, efficient business improvement can be expected. An example of a prompt to the generated AI model would be, "Please tell me about effective marketing methods for targeting a new customer segment."

[0499] By utilizing this system, employees will have access to an environment that allows them to improve their work in a more specific and effective way.

[0500] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0501] Step 1:

[0502] Users input their work environment and activity information into a terminal. This mainly consists of detailed data such as store location, customer age range, and customer traffic. The terminal checks the format of the input information and accurately transmits it to the server. The requirement for the input data is that all data fields necessary for subsequent processing are fully filled in.

[0503] Step 2:

[0504] The server receives data sent from the terminal. The received data is stored in the database by the server. During this storage process, the server checks whether the data format conforms to a specified format, and if there are any integrity issues, it sends a notification to the user requesting correction. The input is work environment information sent from the terminal, and the output is in a standardized data format.

[0505] Step 3:

[0506] The server performs analysis using stored data. This utilizes generative AI models. Specifically, it uses libraries such as TensorFlow and Scikit-learn to run models that quantify worker skills and performance from the data. The input is properly stored data, and the output is quantified performance indicators. Based on these indicators, areas requiring improvement or training are identified.

[0507] Step 4:

[0508] Based on the analysis results, the server identifies other workers who have demonstrated superior performance in similar work environments. This process uses an algorithm that references past performance data and matches workers with similar characteristics. The input is the worker's performance metrics, and the output is a list of matched workers.

[0509] Step 5:

[0510] The server plans and conducts online training for matched workers. This training planning utilizes platforms such as Zoom and Microsoft Teams, enabling participants to share success stories and specific methods. The input is a list of matched workers, and the output is the details of the scheduled training.

[0511] Step 6:

[0512] Users participate in training and apply what they learn to their actual work. After the training, the server re-evaluates the user's work performance and confirms improvement. Workers who provided knowledge are given feedback and reward points. Input is the user's work data after the training, and output is updated evaluation metrics and feedback.

[0513] (Application Example 1)

[0514] 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."

[0515] In logistics facilities, there is a lack of knowledge sharing among individual workers to efficiently manage inventory, and insufficient provision of successful methods suited to individual work environments. Therefore, it is necessary to improve work efficiency while responding to the fluctuating conditions of each store.

[0516] 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.

[0517] In this invention, the server includes means for acquiring operational environment information and work content information for each worker, means for analyzing the results of each worker using a generative model, and means for presenting efficient work procedures related to inventory management at a logistics facility. This makes it possible to share knowledge among workers and to immediately provide appropriate work methods according to the environment.

[0518] A "worker" is an individual who performs specific tasks in a logistics facility or various other work environments.

[0519] "Operating environment information" refers to information about environmental factors and conditions that affect workers' activities.

[0520] "Job description information" refers to information about the specific tasks and objectives that a worker is supposed to perform.

[0521] A "generative model" is a machine learning algorithm used to derive patterns and insights from large amounts of data.

[0522] "Analysis" is the process of performing evaluations and diagnoses based on acquired data, with a specific purpose or goal in mind.

[0523] "Mapping" is the act of finding relationships between multiple elements that have similar conditions and effectively linking them together.

[0524] "Education and training" refers to a program systematically provided to workers to acquire the knowledge and skills necessary to perform their duties.

[0525] A "logistics facility" is a place or building where goods are stored and shipped.

[0526] "Inventory management" refers to all operations necessary to effectively purchase, store, allocate, and ship goods within a logistics facility.

[0527] An "efficient work procedure" is a series of work processes designed to effectively achieve a goal by making the most of limited resources.

[0528] "Knowledge sharing" refers to the joint use and application of specific knowledge and expertise by multiple people.

[0529] This invention is a system for improving the efficiency of inventory management operations within a logistics facility. The system consists of a terminal carried by each worker and a server that supports it.

[0530] The server collects operational environment information and work content information received from workers and stores it in a database. This information is entered by workers using smartphones or smart glasses. The server manages the information via an API of the Django framework implemented in Python.

[0531] The system employs Scikit-learn as a generative model to analyze workers' activity history and work performance. Based on the patterns obtained from this analysis process, appropriate improvement procedures are derived for each worker. Furthermore, knowledge sharing is achieved through mapping these procedures to other workers. In addition, Unity is used to provide smart glasses with real-time, visual representations of efficient work procedures.

[0532] As a concrete example, worker A, who performs inventory counting in a logistics facility, uses this system. Worker A inputs their work details and current work environment using a terminal, and the server analyzes this information. From the generated data, methods that can achieve superior results under similar work conditions are suggested, enabling worker A to perform their work optimally. As a result, efficient inventory counting is performed, and the overall efficiency of the operation is improved.

[0533] As an example of a prompt, by inputting "Please provide examples of efficient methods for inventory management in logistics facilities" into the generating AI model, appropriate advice and methods can be obtained immediately.

[0534] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0535] Step 1:

[0536] Before starting work, users input operational environment information and work content information using their own terminals. The terminal displays an input form containing detailed data about the work environment conditions and current work objectives. The entered information is sent from the terminal to the server. The output at this stage is raw data obtained from the user.

[0537] Step 2:

[0538] The server stores the received operational environment information and business content information in a database. The database is managed using the Django framework. Next, the server uses the Scikit-learn library to run a generative model using the stored historical data and the newly sent data. Data processing in this step includes data normalization and feature extraction. The output is the result of the model's analysis.

[0539] Step 3:

[0540] The server uses the analysis results to identify and match other workers who have achieved high performance under similar operating conditions. This matching process compares the analysis results with past success data to select the most relevant methods. The output is a list of recommended methods and information on other workers.

[0541] Step 4:

[0542] Users receive analysis results and detailed recommendations for efficient work procedures from the server via their devices. The information is presented in a visually easy-to-understand format on the device interface. Users with smart glasses, in particular, are provided with real-time work assistance using AR technology. The output in this case serves as a guide to specific work procedures for the user.

[0543] Step 5:

[0544] The user performs the task based on the provided specific procedures and feeds back the performance data to the server via the terminal. The server monitors the results of the performed work and saves the feedback data to a database as material for improvement in the future. The output is new performance data for program adjustments aimed at improving the work.

[0545] 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.

[0546] This invention provides a system that combines an emotion engine to improve the performance of employees and maximize the effectiveness of training. This system incorporates elements of emotion management into analysis, matching, and online training based on work environment information and project information, enabling more personalized responses.

[0547] Data collection and analysis

[0548] Users use a terminal to input information about the store's work environment and tasks. The terminal sends this data to a server, which records it in a database. The server uses machine learning models to analyze the collected information and evaluate the performance of each worker. Based on the analysis, it identifies workers who have achieved success in similar environments.

[0549] Online training and emotional management

[0550] Online training sessions are conducted among matched professionals with the aim of sharing best practices. This is where an emotion engine installed on the server comes into play. The emotion engine analyzes the facial expressions and voice data of users participating in the training in real time to measure their emotional state. This information is used to determine whether the user is experiencing stress or if their understanding is progressing.

[0551] Individual feedback and evaluation

[0552] The server adjusts training content and feedback individually based on data obtained from the emotion engine. For example, if a user appears confused, it can provide detailed explanations and additional materials. Furthermore, in post-training performance evaluations, the emotion data can be used to provide appropriate assessments tailored to the user's situation. This strongly supports maintaining employee motivation and skill development.

[0553] Specific example

[0554] Let's say an employee is participating in training to learn sales techniques for a new product. If the emotion engine detects that the user's facial expression is tense during the online training, the server will adjust the pace of the training or prompt the user to take a break to enhance the learning effect. In this way, by making fine adjustments according to the user's emotional state, it is possible to support the achievement of the training objectives.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] Users use their own devices to input information such as store location, customer attributes, and details of sales opportunities.

[0558] Step 2:

[0559] The terminal processes the entered data and sends it to the server. The server stores that information in a database.

[0560] Step 3:

[0561] The server collects information stored in the database and uses machine learning models to analyze the performance metrics of each employee.

[0562] Step 4:

[0563] Based on the analysis results, the server identifies and matches other workers who are achieving high performance under similar environmental conditions.

[0564] Step 5:

[0565] The server schedules online training for matched job performers. The training aims to share best practices and facilitate learning.

[0566] Step 6:

[0567] During training, users participate via their devices, and the server uses an emotion engine to analyze the users' facial expressions and voice in real time, monitoring their emotional state.

[0568] Step 7:

[0569] The server adjusts the training content and pace in real time based on the user's emotional state, as recognized by the emotion engine. For example, if a user is having difficulty understanding, it provides detailed explanations and visual aids.

[0570] Step 8:

[0571] After the training is completed, users will apply what they have learned to their work and aim to improve their performance.

[0572] Step 9:

[0573] The server monitors the performance of employees after training and evaluates improvements in performance based on that data.

[0574] Step 10:

[0575] The server generates feedback based on the evaluation results and sends it to the user. It also takes into account the user's emotional data from the emotion engine to provide advice on areas for future improvement and ways to boost motivation.

[0576] (Example 2)

[0577] 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."

[0578] In today's work environment, improving the work efficiency of each user requires providing appropriate education and support while considering their individual work environment and emotional state. However, traditional education systems are uniform and struggle to respond flexibly to the specific circumstances and emotional states of users. Furthermore, these systems lack mechanisms for efficiently sharing success stories among users and improving performance. As a result, declining user motivation and stagnation in skill development are becoming challenges.

[0579] 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.

[0580] In this invention, the server includes means for collecting information about each user's work environment and work information, means for analyzing each user's work efficiency using a learning algorithm, and means for analyzing the user's emotional state and adjusting the content of education and the information provided based on the user's emotional state. This enables more individualized responses for each user, helping to maintain motivation and improve skills. Furthermore, sharing success stories among users can improve the overall performance of the organization.

[0581] "User" refers to an individual who operates the system and inputs work environment information and job information.

[0582] "Information regarding the work environment" refers to data concerning the physical and conditional circumstances under which work is performed, such as temperature, humidity, and the status of work equipment.

[0583] "Work information" refers to data regarding the details and progress of ongoing projects.

[0584] A "learning algorithm" refers to the process of analyzing data using machine learning techniques and evaluating the user's work efficiency based on the results.

[0585] "Emotional state" refers to the mental condition analyzed based on the user's facial expressions and voice.

[0586] "Educational content" refers to the specific information and materials regarding the training and instruction that users receive.

[0587] "Means of collecting information" refers to the methods and processes for obtaining information about the work environment and work from the user.

[0588] "Means of analyzing information" refers to methods and techniques for processing collected data to gain useful insights.

[0589] "Means of adjusting information" refers to the process of optimizing the content of education and instruction provided to users based on the analyzed results.

[0590] This invention is a system that collects user work environment information and work information, and maximizes work efficiency and learning effectiveness through analysis based on this information. To achieve this, the server, terminal, and user interface work together to build a complex data processing and feedback loop.

[0591] The user uses a terminal to input information about the work environment and the job itself. This input includes specific data such as temperature, humidity, noise level at the work site, and current work progress. The terminal then transmits this information to the server in an appropriate format.

[0592] The server stores the submitted information in a database and then analyzes the data using a learning algorithm. This analysis utilizes machine learning libraries such as TensorFlow to evaluate each user's work efficiency and potential problems. This evaluation has the potential to suggest how each user can improve their performance.

[0593] Furthermore, the server is equipped with an emotion analysis engine. During training, it analyzes the user's facial expressions and voice data in real time to infer their emotional state. Based on these results, the server dynamically adjusts the content of the training and instruction. For example, if the user is feeling stressed, the server can slow down the pace of the training and provide the user with additional materials to improve their understanding.

[0594] As a concrete example, consider a scenario where a user participates in an online training session to learn sales techniques for a new product. During this training, the server senses the participant's level of tension from their facial expressions through the online conferencing system. Based on this, the server adjusts the training schedule and prompts breaks as needed to enhance learning effectiveness.

[0595] By utilizing a generative AI model, the server generates prompts such as, "When conducting online training on sales methods for a new product, how can the training content be adjusted based on the emotional state of the participants to be most effective?" and optimizes the instruction. This system improves the user's work efficiency while enhancing learning effectiveness through personalized support.

[0596] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0597] Step 1:

[0598] The user uses a terminal to input work environment information and work information. This input includes temperature and humidity of the work area, the status of the equipment being used, and details about the ongoing task. The terminal transmits this information to the server as digital data. The input for this process is environmental data obtained from manual user input and sensors, and the output is structured data sent to the server.

[0599] Step 2:

[0600] The server stores received work environment information and job information in a database. The stored data is associated with time stamps and recorded in a format suitable for subsequent analysis. The input is structured data sent from the terminal, and the output is historical data stored in the database.

[0601] Step 3:

[0602] The server performs analysis using a learning algorithm based on the stored data. In this step, machine learning libraries such as TensorFlow are used to evaluate each user's work efficiency and potential areas for improvement. The input is historical data stored in the database, and the output is a performance report for each user.

[0603] Step 4:

[0604] Based on the analysis results, the server matches users with successful track records in similar environments. This matching process performs optimal pairing based on past successes and specific business scenarios. The input is a performance report, and the output is a list of matched user pairs.

[0605] Step 5:

[0606] The server schedules and conducts training sessions to allow matched users to share success stories. This training is conducted in real-time via an online conferencing system, enabling information exchange through video calls. Inputs include a pair list and schedule information, while output is a log of the training implementation status.

[0607] Step 6:

[0608] During the learning process, the server uses an emotion analysis engine to analyze the user's facial expressions and voice, and estimates their emotional state in real time. Based on this data, it determines the user's level of comprehension and stress. Input is video and audio data, and output is an emotional state report.

[0609] Step 7:

[0610] The server dynamically adjusts the educational content based on the emotional state report. For example, if it determines that understanding is not progressing, the server adjusts the pace of the lesson or provides additional materials. The input is the emotional state report, and the output is customized educational content.

[0611] (Application Example 2)

[0612] 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."

[0613] In today's work environment, the challenges faced by workers are becoming increasingly complex. In particular, achieving both increased efficiency and improved employee skills simultaneously is difficult. Traditional training programs struggle to provide feedback tailored to the individual worker's emotions and level of understanding, which hinders skill development. Furthermore, the lack of technology to enable real-time, emotion-sensitive feedback prevents maximizing employee performance.

[0614] 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.

[0615] In this invention, the server includes means for collecting work environment information and case information for each worker; means for analyzing each worker's abilities using a machine learning algorithm based on the collected information; and means for adjusting training content and feedback using an emotion engine that analyzes the worker's facial expressions and voice data in real time and measures their emotional state. This enables detailed training tailored to each worker's emotions and level of understanding.

[0616] "Workers" refers to individuals or teams performing tasks in a specific environment, whose skills and knowledge are essential to the success of the work.

[0617] "Work environment information" refers to the physical and organizational conditions related to the work performed by the worker, and includes machinery and equipment, work procedures, and the conditions of the work site.

[0618] "Project information" refers to detailed data about a specific task or operation, including information such as the project's objectives, schedule, progress, and related resources.

[0619] A "machine learning algorithm" refers to a mathematical method that analyzes patterns based on collected data and automatically makes predictions and decisions from that data.

[0620] "Analyzing capabilities" means evaluating how efficiently and accurately a worker can perform a task under specific conditions.

[0621] The term "emotion engine" refers to a technology that analyzes workers' facial expressions and voice data in real time to estimate their emotions, thereby making it possible to understand the workers' emotional state.

[0622] "Adjusting training content and feedback" means optimizing the content and methodology of the training program, while taking into account the feelings and level of understanding of the workers, in order to provide an effective learning experience for each worker.

[0623] The program for the system realizing this invention begins by collecting work environment information and project information for each worker via a terminal and transmitting it to a server. The server stores the collected information in a database and uses a machine learning algorithm to evaluate the capabilities of each worker. This allows the server to match workers who have achieved success in similar environments.

[0624] When training is conducted online, the server uses an emotion engine to analyze the participants' facial expressions and voices in real time and measure their emotional state. This emotional data is then used by the server to adjust the training content and pace, and to provide individualized feedback in real time.

[0625] In terms of specific hardware, workers will use devices such as smart glasses and tablets, while the server will be equipped with a database and an emotion analysis engine. The software will include machine learning algorithms and facial recognition libraries (such as OpenCV and Microsoft Cognitive Services). This will enable a system that can handle everything from data acquisition and analysis to providing appropriate feedback in one integrated process.

[0626] As a concrete example, imagine a new employee participating in online training to learn how to operate a factory robot. The server detects high levels of tension from the worker's facial expressions and automatically slows the training pace and adds supplementary explanations. This creates an environment that facilitates the new employee's understanding.

[0627] An example of a prompt using a generative AI model is: "Assess whether the robot operator is experiencing stress during the process of learning a new work process, and explain what approaches would be effective in maximizing work efficiency." Based on this prompt, the generative AI model can propose appropriate training adjustments.

[0628] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0629] Step 1:

[0630] Users input work environment information and project information using a terminal. The entered data is sent from the terminal to the server. This digitizes the details of the work site and projects.

[0631] Step 2:

[0632] The server stores the received data in a database. Based on the stored information, it activates a machine learning algorithm to begin evaluating each user's work performance. As an output of the analysis, each user's ability level and characteristics are generated.

[0633] Step 3:

[0634] Based on the results of the machine learning algorithm, the server identifies and matches other users who have achieved high results under similar conditions. During this matching process, a pairing list of users with successful track records in similar environments is generated.

[0635] Step 4:

[0636] Online training sessions are scheduled and conducted between matched users. The server performs real-time emotion analysis, inputting users' facial expressions and voice data into an emotion engine to obtain their emotional state. This allows the system to understand the users' stress levels and concentration levels.

[0637] Step 5:

[0638] The server dynamically adjusts the pace and content of online training based on emotional data. If necessary, it individually optimizes the training content and presents slides and additional explanatory materials to deepen understanding. This makes it possible to provide feedback tailored to each user's level of comprehension.

[0639] Step 6:

[0640] After the training is completed, the server analyzes the results of the training and provides feedback to the user. This feedback is based on emotional and performance data from the training and includes specific areas for improvement and success stories. It also uses a generating AI model to provide example prompts for the next training session.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] [Fourth Embodiment]

[0645] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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).

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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".

[0658] This invention provides a system that can perform appropriate performance improvement matching based on the work environment and project information of the workforce. This system supports the skill improvement of workforce through data collection, analysis, matching, training, and evaluation.

[0659] Data collection

[0660] At system startup, users enter their environment information and details of their assigned projects into a terminal. This information includes store location, customer demographics, and customer traffic. The terminal collects the entered data and sends it to the server.

[0661] Data analysis and matching

[0662] The collected information is managed on a server. The server uses machine learning models to analyze the work environment and project information of each employee and calculate various performance indicators. This allows the server to identify and match other employees who are performing well under similar conditions.

[0663] Training and feedback

[0664] The server coordinates online training sessions between matched professionals. This training provides a platform for sharing specific success stories and effective methods. Users can participate in this training and apply the learned methods to their actual work, thereby improving efficiency and achieving better results. After the training, the server monitors the professionals' performance and evaluates them based on improvements in results. Professionals who shared knowledge are awarded evaluation points and provided with feedback.

[0665] Specific example

[0666] For example, suppose a salesperson selling smartphones at a suburban store uses this system. This salesperson inputs data indicating that the customer base is primarily young people and that customer traffic fluctuates seasonally. The server analyzes this data and identifies other salespeople who have achieved high sales performance in similar environments. Online training is conducted with these identified salespeople, allowing them to learn effective sales strategies on the spot and improve sales performance at the suburban store. If performance improves after the training, the salesperson who provided the knowledge is awarded points as a reward.

[0667] The following describes the processing flow.

[0668] Step 1:

[0669] Users use their own devices to input work environment information such as store location, customer base, and project characteristics, as well as project information.

[0670] Step 2:

[0671] The terminal formats the input data and sends it to the server as a data packet.

[0672] Step 3:

[0673] The server records the received data in a database and aggregates information on all employees involved in the work.

[0674] Step 4:

[0675] The server uses the database information to run a machine learning model to calculate performance metrics for each employee.

[0676] Step 5:

[0677] Based on the calculated performance metrics, the server identifies employees who are performing well in similar environments.

[0678] Step 6:

[0679] The server matches identified personnel with online training sessions based on the matching results.

[0680] Step 7:

[0681] The server notifies users' terminals of the details of the planned training and prepares for participants to connect in real time.

[0682] Step 8:

[0683] Users participate in online training at designated times, learning success stories and techniques from other professionals.

[0684] Step 9:

[0685] The server monitors the performance of employees after they complete the training and records their results in a database.

[0686] Step 10:

[0687] If an improvement in performance is confirmed, the server adds evaluation points to the employees who shared the knowledge and sends the feedback information to the user's terminal.

[0688] (Example 1)

[0689] 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".

[0690] In today's work environment, employees need to adapt to diverse work environments and customer needs. However, individual employees are often limited by their own experience and knowledge, making it difficult for them to obtain the information necessary to achieve optimal performance. Therefore, there is a growing need for support systems that enable employees to efficiently improve their skills and enhance their results.

[0691] 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.

[0692] In this invention, the server includes means for aggregating work environment information and activity information of each worker, means for analyzing the capabilities of each worker using a data processing model based on the aggregated information, and means for matching workers who have achieved results in similar environments based on the analysis results. This makes it possible for workers to obtain practical information that is optimal for their own work environment and to efficiently improve their performance.

[0693] "Worker" refers to an individual engaged in a specific task or operation.

[0694] "Work environment information" refers to information regarding the physical and social factors that affect workers when they perform their duties.

[0695] "Activity information" refers to data and records generated by workers in the process of performing their duties.

[0696] "To aggregate" refers to organizing individual data or pieces of information and combining them into a single, cohesive format.

[0697] A "data processing model" refers to a method for analyzing, predicting, or classifying data using specific algorithms or techniques.

[0698] "Analyzing capabilities" refers to evaluating a worker's performance and skills based on objective data.

[0699] "Similar environments" refer to situations in which different workers find themselves sharing common characteristics.

[0700] "To associate" refers to linking or relating related objects.

[0701] "Achieving results" refers to a state in which an employee is producing excellent results in a specific task.

[0702] "Practical information" refers to information about specific knowledge and skills that are useful in actual work and activities.

[0703] The system of this invention can improve skills more efficiently by utilizing the work environment and project information of employees. First, the user inputs their work environment information and activity information using a terminal. This information includes, for example, data on the work location and customer base. The terminal aggregates this information and transmits it to the server.

[0704] The server uses a data processing model to analyze information submitted by users and quantify the capabilities of the workforce. This data processing utilizes machine learning libraries such as TensorFlow and Scikit-learn. This allows the server to clearly identify each workforce's strengths and areas for improvement, and based on this information, identify other workforces who are performing well under similar conditions.

[0705] Following this, the server matches identified business personnel with each other and plans and conducts training on an online platform. This training is conducted using tools such as Zoom and Microsoft Teams, and success stories and practical information are shared. Based on this information, users can make effective adjustments to their own work processes. After the training, the users' performance is evaluated, and feedback is given to the business personnel who provided the knowledge.

[0706] As a concrete example, consider a scenario where an employee working in an urban store and targeting a new customer segment utilizes this system. This employee inputs customer acquisition data and customer profile information into the system. The server analyzes this data, identifies other employees who have achieved success in similar situations, and provides a platform for learning effective approaches. As a result, efficient business improvement can be expected. An example of a prompt to the generated AI model would be, "Please tell me about effective marketing methods for targeting a new customer segment."

[0707] By utilizing this system, employees will have access to an environment that allows them to improve their work in a more specific and effective way.

[0708] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0709] Step 1:

[0710] Users input their work environment and activity information into a terminal. This mainly consists of detailed data such as store location, customer age range, and customer traffic. The terminal checks the format of the input information and accurately transmits it to the server. The requirement for the input data is that all data fields necessary for subsequent processing are fully filled in.

[0711] Step 2:

[0712] The server receives data sent from the terminal. The received data is stored in the database by the server. During this storage process, the server checks whether the data format conforms to a specified format, and if there are any integrity issues, it sends a notification to the user requesting correction. The input is work environment information sent from the terminal, and the output is in a standardized data format.

[0713] Step 3:

[0714] The server performs analysis using stored data. This utilizes generative AI models. Specifically, it uses libraries such as TensorFlow and Scikit-learn to run models that quantify worker skills and performance from the data. The input is properly stored data, and the output is quantified performance indicators. Based on these indicators, areas requiring improvement or training are identified.

[0715] Step 4:

[0716] Based on the analysis results, the server identifies other workers who have demonstrated superior performance in similar work environments. This process uses an algorithm that references past performance data and matches workers with similar characteristics. The input is the worker's performance metrics, and the output is a list of matched workers.

[0717] Step 5:

[0718] The server plans and conducts online training for matched workers. This training planning utilizes platforms such as Zoom and Microsoft Teams, enabling participants to share success stories and specific methods. The input is a list of matched workers, and the output is the details of the scheduled training.

[0719] Step 6:

[0720] Users participate in training and apply what they learn to their actual work. After the training, the server re-evaluates the user's work performance and confirms improvement. Workers who provided knowledge are given feedback and reward points. Input is the user's work data after the training, and output is updated evaluation metrics and feedback.

[0721] (Application Example 1)

[0722] 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".

[0723] In logistics facilities, there is a lack of knowledge sharing among individual workers to efficiently manage inventory, and insufficient provision of successful methods suited to individual work environments. Therefore, it is necessary to improve work efficiency while responding to the fluctuating conditions of each store.

[0724] 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.

[0725] In this invention, the server includes means for acquiring operational environment information and work content information for each worker, means for analyzing the results of each worker using a generative model, and means for presenting efficient work procedures related to inventory management at a logistics facility. This makes it possible to share knowledge among workers and to immediately provide appropriate work methods according to the environment.

[0726] A "worker" is an individual who performs specific tasks in a logistics facility or various other work environments.

[0727] "Operating environment information" refers to information about environmental factors and conditions that affect workers' activities.

[0728] "Job description information" refers to information about the specific tasks and objectives that a worker is supposed to perform.

[0729] A "generative model" is a machine learning algorithm used to derive patterns and insights from large amounts of data.

[0730] "Analysis" is the process of performing evaluations and diagnoses based on acquired data, with a specific purpose or goal in mind.

[0731] "Mapping" is the act of finding relationships between multiple elements that have similar conditions and effectively linking them together.

[0732] "Education and training" refers to a program systematically provided to workers to acquire the knowledge and skills necessary to perform their duties.

[0733] A "logistics facility" is a place or building where goods are stored and shipped.

[0734] "Inventory management" refers to all operations necessary to effectively purchase, store, allocate, and ship goods within a logistics facility.

[0735] An "efficient work procedure" is a series of work processes designed to effectively achieve a goal by making the most of limited resources.

[0736] "Knowledge sharing" refers to the joint use and application of specific knowledge and expertise by multiple people.

[0737] This invention is a system for improving the efficiency of inventory management operations within a logistics facility. The system consists of a terminal carried by each worker and a server that supports it.

[0738] The server collects operational environment information and work content information received from workers and stores it in a database. This information is entered by workers using smartphones or smart glasses. The server manages the information via an API of the Django framework implemented in Python.

[0739] The system employs Scikit-learn as a generative model to analyze workers' activity history and work performance. Based on the patterns obtained from this analysis process, appropriate improvement procedures are derived for each worker. Furthermore, knowledge sharing is achieved through mapping these procedures to other workers. In addition, Unity is used to provide smart glasses with real-time, visual representations of efficient work procedures.

[0740] As a concrete example, worker A, who performs inventory counting in a logistics facility, uses this system. Worker A inputs their work details and current work environment using a terminal, and the server analyzes this information. From the generated data, methods that can achieve superior results under similar work conditions are suggested, enabling worker A to perform their work optimally. As a result, efficient inventory counting is performed, and the overall efficiency of the operation is improved.

[0741] As an example of a prompt, by inputting "Please provide examples of efficient methods for inventory management in logistics facilities" into the generating AI model, appropriate advice and methods can be obtained immediately.

[0742] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0743] Step 1:

[0744] Before starting work, users input operational environment information and work content information using their own terminals. The terminal displays an input form containing detailed data about the work environment conditions and current work objectives. The entered information is sent from the terminal to the server. The output at this stage is raw data obtained from the user.

[0745] Step 2:

[0746] The server stores the received operational environment information and business content information in a database. The database is managed using the Django framework. Next, the server uses the Scikit-learn library to run a generative model using the stored historical data and the newly sent data. Data processing in this step includes data normalization and feature extraction. The output is the result of the model's analysis.

[0747] Step 3:

[0748] The server uses the analysis results to identify and match other workers who have achieved high performance under similar operating conditions. This matching process compares the analysis results with past success data to select the most relevant methods. The output is a list of recommended methods and information on other workers.

[0749] Step 4:

[0750] Users receive analysis results and detailed recommendations for efficient work procedures from the server via their devices. The information is presented in a visually easy-to-understand format on the device interface. Users with smart glasses, in particular, are provided with real-time work assistance using AR technology. The output in this case serves as a guide to specific work procedures for the user.

[0751] Step 5:

[0752] The user performs the task based on the provided specific procedures and feeds back the performance data to the server via the terminal. The server monitors the results of the performed work and saves the feedback data to a database as material for improvement in the future. The output is new performance data for program adjustments aimed at improving the work.

[0753] 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.

[0754] This invention provides a system that combines an emotion engine to improve the performance of employees and maximize the effectiveness of training. This system incorporates elements of emotion management into analysis, matching, and online training based on work environment information and project information, enabling more personalized responses.

[0755] Data collection and analysis

[0756] Users use a terminal to input information about the store's work environment and tasks. The terminal sends this data to a server, which records it in a database. The server uses machine learning models to analyze the collected information and evaluate the performance of each worker. Based on the analysis, it identifies workers who have achieved success in similar environments.

[0757] Online training and emotional management

[0758] Online training sessions are conducted among matched professionals with the aim of sharing best practices. This is where an emotion engine installed on the server comes into play. The emotion engine analyzes the facial expressions and voice data of users participating in the training in real time to measure their emotional state. This information is used to determine whether the user is experiencing stress or if their understanding is progressing.

[0759] Individual feedback and evaluation

[0760] The server adjusts training content and feedback individually based on data obtained from the emotion engine. For example, if a user appears confused, it can provide detailed explanations and additional materials. Furthermore, in post-training performance evaluations, the emotion data can be used to provide appropriate assessments tailored to the user's situation. This strongly supports maintaining employee motivation and skill development.

[0761] Specific example

[0762] Let's say an employee is participating in training to learn sales techniques for a new product. If the emotion engine detects that the user's facial expression is tense during the online training, the server will adjust the pace of the training or prompt the user to take a break to enhance the learning effect. In this way, by making fine adjustments according to the user's emotional state, it is possible to support the achievement of the training objectives.

[0763] The following describes the processing flow.

[0764] Step 1:

[0765] Users use their own devices to input information such as store location, customer attributes, and details of sales opportunities.

[0766] Step 2:

[0767] The terminal processes the entered data and sends it to the server. The server stores that information in a database.

[0768] Step 3:

[0769] The server collects information stored in the database and uses machine learning models to analyze the performance metrics of each employee.

[0770] Step 4:

[0771] Based on the analysis results, the server identifies and matches other workers who are achieving high performance under similar environmental conditions.

[0772] Step 5:

[0773] The server schedules online training for matched job performers. The training aims to share best practices and facilitate learning.

[0774] Step 6:

[0775] During training, users participate via their devices, and the server uses an emotion engine to analyze the users' facial expressions and voice in real time, monitoring their emotional state.

[0776] Step 7:

[0777] The server adjusts the training content and pace in real time based on the user's emotional state, as recognized by the emotion engine. For example, if a user is having difficulty understanding, it provides detailed explanations and visual aids.

[0778] Step 8:

[0779] After the training is completed, users will apply what they have learned to their work and aim to improve their performance.

[0780] Step 9:

[0781] The server monitors the performance of employees after training and evaluates improvements in performance based on that data.

[0782] Step 10:

[0783] The server generates feedback based on the evaluation results and sends it to the user. It also takes into account the user's emotional data from the emotion engine to provide advice on areas for future improvement and ways to boost motivation.

[0784] (Example 2)

[0785] 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".

[0786] In today's work environment, improving the work efficiency of each user requires providing appropriate education and support while considering their individual work environment and emotional state. However, traditional education systems are uniform and struggle to respond flexibly to the specific circumstances and emotional states of users. Furthermore, these systems lack mechanisms for efficiently sharing success stories among users and improving performance. As a result, declining user motivation and stagnation in skill development are becoming challenges.

[0787] 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.

[0788] In this invention, the server includes means for collecting information about each user's work environment and work information, means for analyzing each user's work efficiency using a learning algorithm, and means for analyzing the user's emotional state and adjusting the content of education and the information provided based on the user's emotional state. This enables more individualized responses for each user, helping to maintain motivation and improve skills. Furthermore, sharing success stories among users can improve the overall performance of the organization.

[0789] "User" refers to an individual who operates the system and inputs work environment information and job information.

[0790] "Information regarding the work environment" refers to data concerning the physical and conditional circumstances under which work is performed, such as temperature, humidity, and the status of work equipment.

[0791] "Work information" refers to data regarding the details and progress of ongoing projects.

[0792] A "learning algorithm" refers to the process of analyzing data using machine learning techniques and evaluating the user's work efficiency based on the results.

[0793] "Emotional state" refers to the mental condition analyzed based on the user's facial expressions and voice.

[0794] "Educational content" refers to the specific information and materials regarding the training and instruction that users receive.

[0795] "Means of collecting information" refers to the methods and processes for obtaining information about the work environment and work from the user.

[0796] "Means of analyzing information" refers to methods and techniques for processing collected data to gain useful insights.

[0797] "Means of adjusting information" refers to the process of optimizing the content of education and instruction provided to users based on the analyzed results.

[0798] This invention is a system that collects user work environment information and work information, and maximizes work efficiency and learning effectiveness through analysis based on this information. To achieve this, the server, terminal, and user interface work together to build a complex data processing and feedback loop.

[0799] The user uses a terminal to input information about the work environment and the job itself. This input includes specific data such as temperature, humidity, noise level at the work site, and current work progress. The terminal then transmits this information to the server in an appropriate format.

[0800] The server stores the submitted information in a database and then analyzes the data using a learning algorithm. This analysis utilizes machine learning libraries such as TensorFlow to evaluate each user's work efficiency and potential problems. This evaluation has the potential to suggest how each user can improve their performance.

[0801] Furthermore, the server is equipped with an emotion analysis engine. During training, it analyzes the user's facial expressions and voice data in real time to infer their emotional state. Based on these results, the server dynamically adjusts the content of the training and instruction. For example, if the user is feeling stressed, the server can slow down the pace of the training and provide the user with additional materials to improve their understanding.

[0802] As a concrete example, consider a scenario where a user participates in an online training session to learn sales techniques for a new product. During this training, the server senses the participant's level of tension from their facial expressions through the online conferencing system. Based on this, the server adjusts the training schedule and prompts breaks as needed to enhance learning effectiveness.

[0803] By utilizing a generative AI model, the server generates prompts such as, "When conducting online training on sales methods for a new product, how can the training content be adjusted based on the emotional state of the participants to be most effective?" and optimizes the instruction. This system improves the user's work efficiency while enhancing learning effectiveness through personalized support.

[0804] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0805] Step 1:

[0806] The user uses a terminal to input work environment information and work information. This input includes temperature and humidity of the work area, the status of the equipment being used, and details about the ongoing task. The terminal transmits this information to the server as digital data. The input for this process is environmental data obtained from manual user input and sensors, and the output is structured data sent to the server.

[0807] Step 2:

[0808] The server stores received work environment information and job information in a database. The stored data is associated with time stamps and recorded in a format suitable for subsequent analysis. The input is structured data sent from the terminal, and the output is historical data stored in the database.

[0809] Step 3:

[0810] The server performs analysis using a learning algorithm based on the stored data. In this step, machine learning libraries such as TensorFlow are used to evaluate each user's work efficiency and potential areas for improvement. The input is historical data stored in the database, and the output is a performance report for each user.

[0811] Step 4:

[0812] Based on the analysis results, the server matches users with successful track records in similar environments. This matching process performs optimal pairing based on past successes and specific business scenarios. The input is a performance report, and the output is a list of matched user pairs.

[0813] Step 5:

[0814] The server schedules and conducts training sessions to allow matched users to share success stories. This training is conducted in real-time via an online conferencing system, enabling information exchange through video calls. Inputs include a pair list and schedule information, while output is a log of the training implementation status.

[0815] Step 6:

[0816] During the learning process, the server uses an emotion analysis engine to analyze the user's facial expressions and voice, and estimates their emotional state in real time. Based on this data, it determines the user's level of comprehension and stress. Input is video and audio data, and output is an emotional state report.

[0817] Step 7:

[0818] The server dynamically adjusts the educational content based on the emotional state report. For example, if it determines that understanding is not progressing, the server adjusts the pace of the lesson or provides additional materials. The input is the emotional state report, and the output is customized educational content.

[0819] (Application Example 2)

[0820] 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".

[0821] In today's work environment, the challenges faced by workers are becoming increasingly complex. In particular, achieving both increased efficiency and improved employee skills simultaneously is difficult. Traditional training programs struggle to provide feedback tailored to the individual worker's emotions and level of understanding, which hinders skill development. Furthermore, the lack of technology to enable real-time, emotion-sensitive feedback prevents maximizing employee performance.

[0822] 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.

[0823] In this invention, the server includes means for collecting work environment information and case information for each worker; means for analyzing each worker's abilities using a machine learning algorithm based on the collected information; and means for adjusting training content and feedback using an emotion engine that analyzes the worker's facial expressions and voice data in real time and measures their emotional state. This enables detailed training tailored to each worker's emotions and level of understanding.

[0824] "Workers" refers to individuals or teams performing tasks in a specific environment, whose skills and knowledge are essential to the success of the work.

[0825] "Work environment information" refers to the physical and organizational conditions related to the work performed by the worker, and includes machinery and equipment, work procedures, and the conditions of the work site.

[0826] "Project information" refers to detailed data about a specific task or operation, including information such as the project's objectives, schedule, progress, and related resources.

[0827] A "machine learning algorithm" refers to a mathematical method that analyzes patterns based on collected data and automatically makes predictions and decisions from that data.

[0828] "Analyzing capabilities" means evaluating how efficiently and accurately a worker can perform a task under specific conditions.

[0829] The term "emotion engine" refers to a technology that analyzes workers' facial expressions and voice data in real time to estimate their emotions, thereby making it possible to understand the workers' emotional state.

[0830] "Adjusting training content and feedback" means optimizing the content and methodology of the training program, while taking into account the feelings and level of understanding of the workers, in order to provide an effective learning experience for each worker.

[0831] The program for the system realizing this invention begins by collecting work environment information and project information for each worker via a terminal and transmitting it to a server. The server stores the collected information in a database and uses a machine learning algorithm to evaluate the capabilities of each worker. This allows the server to match workers who have achieved success in similar environments.

[0832] When training is conducted online, the server uses an emotion engine to analyze the participants' facial expressions and voices in real time and measure their emotional state. This emotional data is then used by the server to adjust the training content and pace, and to provide individualized feedback in real time.

[0833] In terms of specific hardware, workers will use devices such as smart glasses and tablets, while the server will be equipped with a database and an emotion analysis engine. The software will include machine learning algorithms and facial recognition libraries (such as OpenCV and Microsoft Cognitive Services). This will enable a system that can handle everything from data acquisition and analysis to providing appropriate feedback in one integrated process.

[0834] As a concrete example, imagine a new employee participating in online training to learn how to operate a factory robot. The server detects high levels of tension from the worker's facial expressions and automatically slows the training pace and adds supplementary explanations. This creates an environment that facilitates the new employee's understanding.

[0835] An example of a prompt using a generative AI model is: "Assess whether the robot operator is experiencing stress during the process of learning a new work process, and explain what approaches would be effective in maximizing work efficiency." Based on this prompt, the generative AI model can propose appropriate training adjustments.

[0836] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0837] Step 1:

[0838] Users input work environment information and project information using a terminal. The entered data is sent from the terminal to the server. This digitizes the details of the work site and projects.

[0839] Step 2:

[0840] The server stores the received data in a database. Based on the stored information, it activates a machine learning algorithm to begin evaluating each user's work performance. As an output of the analysis, each user's ability level and characteristics are generated.

[0841] Step 3:

[0842] Based on the results of the machine learning algorithm, the server identifies and matches other users who have achieved high results under similar conditions. During this matching process, a pairing list of users with successful track records in similar environments is generated.

[0843] Step 4:

[0844] Online training sessions are scheduled and conducted between matched users. The server performs real-time emotion analysis, inputting users' facial expressions and voice data into an emotion engine to obtain their emotional state. This allows the system to understand the users' stress levels and concentration levels.

[0845] Step 5:

[0846] The server dynamically adjusts the pace and content of online training based on emotional data. If necessary, it individually optimizes the training content and presents slides and additional explanatory materials to deepen understanding. This makes it possible to provide feedback tailored to each user's level of comprehension.

[0847] Step 6:

[0848] After the training is completed, the server analyzes the results of the training and provides feedback to the user. This feedback is based on emotional and performance data from the training and includes specific areas for improvement and success stories. It also uses a generating AI model to provide example prompts for the next training session.

[0849] 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.

[0850] 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.

[0851] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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."

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0870] The following is further disclosed regarding the embodiments described above.

[0871] (Claim 1)

[0872] A means of collecting information on the work environment and project information of each person engaged in the work,

[0873] A means for analyzing the performance of each worker using a machine learning model based on the information collected,

[0874] Based on the analysis results, a means of matching business professionals who are achieving results in similar environments,

[0875] A means of scheduling and conducting training for matched professionals to share success stories,

[0876] A system that includes means for evaluating the performance of the aforementioned employees in accordance with their improvement.

[0877] (Claim 2)

[0878] The system according to claim 1, wherein the training is conducted in the form of an online conference.

[0879] (Claim 3)

[0880] The system according to claim 1, wherein the evaluation includes providing feedback to the personnel who provided the knowledge.

[0881] "Example 1"

[0882] (Claim 1)

[0883] A means of aggregating information on each worker's work environment and activities,

[0884] Based on the aggregated information, a means for analyzing the capabilities of each worker using a data processing model,

[0885] Based on the analysis results, a means of matching workers who have achieved results in similar environments,

[0886] Means for planning and implementing training for assigned workers to share their experiences,

[0887] A system including means of rewarding the aforementioned workers in accordance with their improved performance.

[0888] (Claim 2)

[0889] The system according to claim 1, wherein the education is conducted in the form of a remote conference.

[0890] (Claim 3)

[0891] The system according to claim 1, wherein the compensation includes return information for the worker who provided the knowledge.

[0892] "Application Example 1"

[0893] (Claim 1)

[0894] A means of acquiring information on each worker's work environment and job content,

[0895] A means for analyzing the results of each worker using a generative model based on the acquired information,

[0896] Based on the analysis results, a means of matching other workers who are improving their work performance under similar operating conditions,

[0897] Means for planning and implementing education and training for associated workers to share successful methods,

[0898] A means of evaluating the worker's performance in accordance with the improvement of their results,

[0899] A system that includes means for presenting efficient work procedures for inventory management at logistics facilities and means for linking information processing devices.

[0900] (Claim 2)

[0901] The system according to claim 1, wherein the aforementioned education and training are conducted in the form of remote conferencing.

[0902] (Claim 3)

[0903] The system according to claim 1, wherein the evaluation includes feedback to the worker who provided the knowledge.

[0904] "Example 2 of combining an emotion engine"

[0905] (Claim 1)

[0906] A means of collecting information about each user's work environment and work-related information,

[0907] A means for analyzing the work efficiency of each user using a learning algorithm based on the information collected,

[0908] Based on the analysis results, a means of matching users who are achieving results in similar environments,

[0909] Means for planning and implementing education for associated users to share success stories,

[0910] A means of analyzing the emotional state of users during education,

[0911] Means for adjusting the content of the education and the information provided based on the user's emotional state,

[0912] A system including means for performing evaluations according to the user's level of achievement.

[0913] (Claim 2)

[0914] The system according to claim 1, wherein the education is conducted via remote communication.

[0915] (Claim 3)

[0916] The system according to claim 1, further comprising providing feedback to the user who provided the knowledge in the evaluation.

[0917] "Application example 2 when combining with an emotional engine"

[0918] (Claim 1)

[0919] A means of collecting information on the work environment and project details for each worker,

[0920] A means for analyzing the capabilities of each worker using a machine learning algorithm based on the information collected,

[0921] Based on the analysis results, a means of matching workers who are achieving results in similar environments,

[0922] A means of scheduling and implementing training for matched workers to share success stories,

[0923] A means of adjusting training content and feedback using an emotion engine that analyzes workers' facial expressions and voice data in real time to measure their emotional state,

[0924] A system including means for evaluating the performance of the aforementioned workers in accordance with their improvement.

[0925] (Claim 2)

[0926] The system according to claim 1, wherein the education is conducted in an online communication format.

[0927] (Claim 3)

[0928] The system according to claim 1, further comprising providing feedback to the worker who provided the knowledge in the aforementioned evaluation. [Explanation of Symbols]

[0929] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring information on each worker's work environment and job content, A means for analyzing the results of each worker using a generative model based on the acquired information, Based on the analysis results, a means of matching other workers who are improving their work performance under similar operating conditions, Means for planning and implementing education and training for associated workers to share successful methods, A means of evaluating the worker's performance in accordance with the improvement of their results, A system that includes means for presenting efficient work procedures for inventory management at logistics facilities and means for linking information processing devices.

2. The system according to claim 1, wherein the aforementioned education and training are conducted in the form of remote conferencing.

3. The system according to claim 1, wherein the evaluation includes feedback to the worker who provided the knowledge.

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